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Towards comprehensive support for organizational mining
Abstract
Process mining has emerged as a way to analyze processes based on the event logs of the systems that support them. Today's information systems (e.g., ERP systems) log all kinds of events. Moreover, also embedded systems (e.g., medical equipment, copiers, and other high-tech systems) start producing detailed event logs. The omnipresence of event logs is an important enabler for process mining. The primary goal of process mining is to extract knowledge from these logs and use it for a detailed analysis of reality. Lion's share of the efforts in this domain has been devoted to control-flow discovery. Many algorithms have been proposed to construct a process model based on an analysis of the event sequences observed in the log. As a result, other aspects have been neglected, e.g., the organizational setting and interactions among coworkers. Therefore, we focus on organizational mining. We will present techniques to discover organizational models and social networks and show how these models can assist in improving the underlying processes. To do this, we present new process mining techniques but also use existing techniques in an innovative manner. The approach has been implemented in the context of the ProM framework and has been applied in various case studies. In this paper, we demonstrate the applicability of our techniques by analyzing the logs of a municipality in the Netherlands.
... Process mining can discover employee-related insights from event log data [14] to support workforce analytics in the context of business processes. Often, employees (human resources) from the same organizational group (such as role, team, department, etc.) share similar characteristics in process execution [12], e.g., resources of the same role are in charge of a subset of process activities. ...
... The connection between resource groupings and groups' characteristics in process execution is a natural consequence of the specialization of work, i.e., division of labor in an organization [4]. Some existing work on resource-oriented process mining considers such characteristics [12,9,8] but treats different dimensions separately. Only few [15,17] has considered the characterization of resource groups across various process dimensions holistically. ...
... Our research contributes a solution to the problem of learning execution contexts, thus enhances resource-oriented process mining techniques that focus on analyzing human resources [8] and their groups [12,16,17]. Our work also contributes a method to derive process cube views in multidimensional process mining research [13,2]. ...
Process mining enables extracting insights into human resources working in business processes and supports employee management and process improvement. Often, resources from the same organizational group exhibit similar characteristics in process execution, e.g., executing the same set of process activities or participating in the same types of cases. This is a natural consequence of division of labor in organizations. These characteristics can be organized along various process dimensions, e.g., case, activity, and time, which ideally are all considered in the application of resource-oriented process mining, especially analytics of resource groups and their behavior. In this paper, we use the concept of execution context to classify cases, activities, and times to enable a precise characterization of resource groups. We propose an approach to automatically learning execution contexts from process execution data recorded in event logs, incorporating domain knowledge and discriminative information embedded in data. Evaluation using real-life event log data demonstrates the usefulness of our approach.KeywordsExecution contextResource groupEvent logProcess miningWorkforce analytics
... In addition, it may record resources who executed those activities. As such, event logs capture the trails of human resource participation in actual business process execution, and therefore provide a reliable starting point for mining timely process-and resource-related information [6,7]. ...
... Process mining [5] offers a growing body of methods to extract knowledge from event logs for process management and improvement. The subfield of organizational model mining [6] is concerned with the study of groups of human resources, specifically how models can be derived from event logs to reflect resource groupings in process execution. The relatively underexplored area of organizational model mining [6,8,9] contains some research gaps which impede its use in practice. ...
... The subfield of organizational model mining [6] is concerned with the study of groups of human resources, specifically how models can be derived from event logs to reflect resource groupings in process execution. The relatively underexplored area of organizational model mining [6,8,9] contains some research gaps which impede its use in practice. Three open issues in particular will be explored in this paper. ...
In order to streamline business processes and increase competitiveness, organizations need to have a deep insight into the resources that they deploy. Among others, they need to understand how these resources act in groups to achieve organizational outcomes. Accurate and timely information is a sine qua non to achieve this understanding. Process mining can be exploited for the purpose of deriving organizational models from event logs that contain resource-related data. But existing process mining techniques are not fully up to this task, as they are not able to cope with the multi-faceted nature of business processes and are not yet able to determine how resource groupings are involved in process execution. In addition, there is no provision for how to evaluate the quality of discovered organizational models.
To tackle these challenges, we propose a novel framework, OrdinoR, capable of supporting discovery of organizational models using event logs, their evaluation, and their analysis. OrdinoR is constructed around a rich notion of organizational model where resource groupings are linked to multiple dimensions of process execution. The framework also provides a set of measures for systematically evaluating such models and analyzing the behavior of resource groups therein. Experiments have been conducted to evaluate the framework using a publicly available real-life dataset. These demonstrate the usefulness of the approach.
... This type of visualizations ( Figure 15) captures information about event sequences as a node-link diagram, where nodes usually represent events and the links represent transition between events. Chen et al. [70] Jang et al. [10] Zeng et al. [46] Nguyen et al. [71] Gotz et al. [72] Jin et al. [41] Cui et al. [73] Shi et al. [74] Wu et al. [50] Zeng et al. [46] Qi et al. [75] Guo et al. [68] Bartolomeo et al. [ Winter et al. [78] Munoz-Gama et al. [79] Leoni et al. [80] Bolt et al. [32] Low et al. [58] Knuplesch et al. [81] Dongen et al. [82] TKDE DSS Song et al. [69] Song et al. [69] Wynn et al. [83] visual representations. This view then shows a hierarchical relationship of which patterns exists in the event sequence data. ...
... This type of visualizations ( Figure 15) captures information about event sequences as a node-link diagram, where nodes usually represent events and the links represent transition between events. Chen et al. [70] Jang et al. [10] Zeng et al. [46] Nguyen et al. [71] Gotz et al. [72] Jin et al. [41] Cui et al. [73] Shi et al. [74] Wu et al. [50] Zeng et al. [46] Qi et al. [75] Guo et al. [68] Bartolomeo et al. [ Winter et al. [78] Munoz-Gama et al. [79] Leoni et al. [80] Bolt et al. [32] Low et al. [58] Knuplesch et al. [81] Dongen et al. [82] TKDE DSS Song et al. [69] Song et al. [69] Wynn et al. [83] visual representations. This view then shows a hierarchical relationship of which patterns exists in the event sequence data. ...
... Nodeused, e.g. in[53,63, IV] and[26,69, 60, PM].Law et al.[63, IV] and Bose et al.[26, PM] use color to distinguish different event types. Also Leite et al. [53, IV] and de Leoni et al. [69, 60, PM] use various glyph designs for event types additionally to color.A second group of Node-link hierarchy-based visualization designs consider the node size, link size or both to show additional dimension in the data. ...
Event sequence data is increasingly available. Many business operations are supported by information systems that record transactions, events, state changes, message exchanges, and so forth. This observation is equally valid for various industries, including production, logistics, healthcare, financial services, education, to name but a few. The variety of application areas explains that techniques for event sequence data analysis have been developed rather independently in different fields of computer science. Most prominent are contributions from information visualization and from process mining. So far, the contributions from these two fields have neither been compared nor have they been mapped to an integrated framework. In this paper, we develop the Event Sequence Visualization framework (ESeVis) that gives due credit to the traditions of both fields. Our mapping study provides an integrated perspective on both fields and identifies potential for synergies for future research.
... Previous studies on the social aspect of BPM include various initiatives to integrate social technologies (e.g. chats, blogs, etc.) to promote stakeholder interactions (Dengler et al., 2010;Dustdar & Hoffmann, 2007;Johannesson et al., 2008); methods to allocate resources to individual tasks by role, capability and execution history (Arias, Munoz-Gama, et al., 2018;Cabanillas et al., 2015;Erasmus et al., 2018); and discovering resource profiles and collaboration patterns from process logs (Pika et al., 2017;Schönig et al., 2018;Song & van der Aalst, 2008). However, the work in this area has not studied team formation at both the intra-and inter-task levels during process execution. ...
... Resource profiles & Org. patterns mining Liu et al., 2013Liu et al., , 2015Pika et al., 2017;Schönig et al., 2018;Song & van der Aalst, 2008;van der Aalst & Song, 2004 The discovered social profiles and collaboration patterns from these studies can be used to build social networks. Our algorithm takes such social networks as an input. ...
... To understand the social aspects of resources, process mining techniques have been used to discover resource profiles and team compositions from logs of their execution history (Pika et al., 2017;Schönig et al., 2018;Song & van der Aalst, 2008;van der Aalst & Song, 2004). A systematic approach is introduced by van der Aalst and Song (2004) along with an analytics tool called MiSoN that creates social networks based on task execution logs. ...
Business Process Management (BPM) systems usually neglect the human and social aspects (or team effects) involved in business process execution. Our work fills a large gap in literature by addressing multi-level teams that arise in business processes where teams are formed at both the task and process levels. In this paper, we develop a methodology called BPMTeams based on social network analysis for building an execution model for a social BPM. This model is used to make resource assignments to form dynamic teams that perform various team-based activities in a process. We further develop various resource assignment strategies and evaluate them using parameters estimated from a real data set in the IT incident management domain to understand how team effects play out in social business processes. The overall team effect in a process is analyzed at two levels: as a task team effect where the synergistic role of a team in a specific task is realized; and a process team effect that arises from inter-team synergies across the individual task teams in a process. The results offer some balanced insights for the interplay of these effects by highlighting the benefits and disadvantages of teams selected by a purely data-driven approach.
... The research area of process mining is large and leads to a wide range of applications. For example, process mining techniques have been applied to support the invoice handling process in a road construction and maintenance company [3], for organizational mining to discover social networks and to optimize the underlying processes [4], or in the healthcare environment to improve the quality of patient care [5]. This section presents an overview of this field and its applications, pointing at the own objectives of the work presented in this paper. ...
... Many process discovery algorithms have been proposed in the literature. The α algorithm [4] is the first and simple discovery technique that constructs causal relationships observed between tasks. This algorithm was proven to be correct for a large class of processes [4]. ...
... The α algorithm [4] is the first and simple discovery technique that constructs causal relationships observed between tasks. This algorithm was proven to be correct for a large class of processes [4]. However, it has problems with noise and incompleteness [6]. ...
A real challenge for manufacturing industry is to be able to control not only the manufacturing process but also the production quality. Products that are suspected to be faulty are deviated from their nominal path in the production line and inspected more closely. The fact that some products deviate from the nominal path and others fail at some check operations can be used as an indicator of poor product quality. Based on this idea, this paper proposes a method to compute a product quality index or more exactly a penalty index taking into account both product path and production batches. The method relies on categorizing the products according to how they follow the production path and process mining techniques. The originality of the proposed index is to be built from advanced data analysis techniques enhanced by expert know-how. The quality index highlights risk of customer return, which is highly relevant information for the after sales service. The significance of the method is illustrated on a printed circuit board production line using surface mount technology at Vitesco Technologies. Data is collected from the real manufacturing execution system. The results obtained over more than 10,000 single electronic boards show that 91.7% of the products are in good compliance with respect to the requirements. For the other products, the method identifies the root causes of poor quality that may call for maintenance or reconfiguration actions.
... Process mining [3] offers a growing body of methods to analyze event logs to extract knowledge about the actual behavior of a process along with other aspects relevant to process execution, including human resources. In process mining research, studies that concern human resource grouping are known as organizational model mining, which aims at finding groups of resources having similar characteristics in process execution by utilizing event log data [4]. Our research interest lies in applying organizational model mining to support the analysis and improvement of resource grouping based on event log data. ...
... Process mining research that aims at deriving human-resource-related insights from event log data is known as organizational mining [4]. Four topics of interest can be identified, respectively: (1) organizational model mining, which aims at discovering knowledge related to organizational structures around human resources (e.g., [4,5]); (2) social network mining, which aims at extracting and analyzing the relationships among resources (e.g., [6,7,8]); (3) rule mining, which aims at extracting inherent rules that decide the use of human resources in process execution, e.g., rules for the assignment of tasks to resources [9,10,11,12], and rules for the composition of resource teams with various expertise [13]; and (4) behavioral profile mining, which aims at extracting and analyzing different aspects of individual resource behavior as they participate in process execution (e.g., [14,15,16,17]). ...
... Process mining research that aims at deriving human-resource-related insights from event log data is known as organizational mining [4]. Four topics of interest can be identified, respectively: (1) organizational model mining, which aims at discovering knowledge related to organizational structures around human resources (e.g., [4,5]); (2) social network mining, which aims at extracting and analyzing the relationships among resources (e.g., [6,7,8]); (3) rule mining, which aims at extracting inherent rules that decide the use of human resources in process execution, e.g., rules for the assignment of tasks to resources [9,10,11,12], and rules for the composition of resource teams with various expertise [13]; and (4) behavioral profile mining, which aims at extracting and analyzing different aspects of individual resource behavior as they participate in process execution (e.g., [14,15,16,17]). ...
Providing appropriate structures around human resources can streamline operations and thus facilitate the competitiveness of an organization. To achieve this goal, modern organizations need to acquire an accurate and timely understanding of human resource grouping while faced with an ever-changing environment. The use of process mining offers a promising way to help address the need through utilizing event log data stored in information systems. By extracting knowledge about the actual behavior of resources participating in business processes from event logs, organizational models can be constructed, which facilitate the analysis of the de facto grouping of human resources relevant to process execution. Nevertheless, open research gaps remain to be addressed when applying the state-of-the-art process mining to analyze resource grouping. For one, the discovery of organizational models has only limited connections with the context of process execution. For another, a rigorous solution that evaluates organizational models against event log data is yet to be proposed. In this paper, we aim to tackle these research challenges by developing a novel framework built upon a richer definition of organizational models coupling resource grouping with process execution knowledge. By introducing notions of conformance checking for organizational models, the framework allows effective evaluation of organizational models, and therefore provides a foundation for analyzing and improving resource grouping based on event logs. We demonstrate the feasibility of this framework by proposing an approach underpinned by the framework for organizational model discovery, and also conduct experiments on real-life event logs to discover and evaluate organizational models.
... One concerns using event logs for analyzing the formation of resource groups, e.g., Schönig et al. [28] propose an approach that uncovers the composition rules of human resource groups in process executions, and Appice [2] proposes a method that reveals the construction and destruction of organizational groups over time using event logs. The other topic concerns the discovery of organizational groups (e.g., [30]), which aims at extracting the grouping structures around resources. Third, there exists research (e.g., [17,25]) focusing on analysis of individual resource behavior by building resource "profiles" from event logs, which represent objective descriptions of how individual resources were involved in process execution. ...
... This is straightforward when a group identity attribute is present in the log. Otherwise, organizational group discovery (e.g., [30]) can be applied to extract group identities of resources. In either situation, one can determine the number of resource groups, their members, and thus the associated event data in the log. ...
Workforce analytics brings data-driven methods to organizations for deriving insights from employee-related data and supports decision making. However, it faces an open challenge of lacking the capability to analyze the behavior of employee groups in order to understand organizational performance. This paper proposes a novel notion of work profiles of resource groups, informed by the management literature, for characterizing resource group behavior from multiple aspects relevant to workforce performance. This notion is central to the design of a new, systematic approach that supports resource group analysis by exploiting business process execution data. The approach also provides managers and business analysts with an intuitive means of group-oriented resource analysis by applying visual analytics. We demonstrate the applicability of the approach and usefulness of the proposed notion of resource group work profiles using real datasets from five Dutch municipalities.
... One concerns using event logs for analyzing the formation of resource groups, e.g., Schönig et al. [28] propose an approach that uncovers the composition rules of human resource groups in process executions, and Appice [2] proposes a method that reveals the construction and destruction of organizational groups over time using event logs. The other topic concerns the discovery of organizational groups (e.g., [30]), which aims at extracting the grouping structures around resources. Third, there exists research (e.g., [17,25]) focusing on analysis of individual resource behavior by building resource "profiles" from event logs, which represent objective descriptions of how individual resources were involved in process execution. ...
... This is straightforward when a group identity attribute is present in the log. Otherwise, organizational group discovery (e.g., [30]) can be applied to extract group identities of resources. In either situation, one can determine the number of resource groups, their members, and thus the associated event data in the log. ...
Workforce analytics brings data-driven methods to organizations for deriving insights from employee-related data and supports decision making. However, it faces an open challenge of lacking the capability to analyze the behavior of employee groups in order to understand organizational performance. This paper proposes a novel notion of work profiles of resource groups, informed by the management literature, for characterizing resource group behavior from multiple aspects relevant to workforce performance. This notion is central to the design of a new, systematic approach that supports resource group analysis by exploiting business process execution data. The approach also provides managers and business analysts with an intuitive means of group-oriented resource analysis by applying visual analytics. We demonstrate the applicability of the approach and usefulness of the proposed notion of resource group work profiles using real datasets from five Dutch municipalities.
... Furthermore, recently, Appice has applied community detection methods to discover organizational structure [5]. It means that few researchers believe that network analysis is a comprehensive approach in process mining [73]. ...
... DOI: 10.4067/S0718-18762021000200104 The NA metrics for analyzing simple networks in organizational mining research are divided into two categories [2], [15], [73]: 1) micro-level metrics: metrics that examine only one specific vertex; such as degree centrality (in-degree and out-degree), betweenness centrality, closeness centrality (in-closeness and out-closeness), eigenvector centrality, and clustering coefficient. 2) macro-level metrics: metrics that analyze the entire network; for example, density, clustering coefficient, and centrality. ...
Process mining in the context of information systems, which consists of information flows, has been one of the major research areas in the past decade. One of the most common objectives of process mining is the automated business process discovery. There are many challenges in the business process discovery, such as spaghetti models, same-name activities, and discovering loop structures. The researchers have presented a variety of methods that focus on one or more challenges. Due to the importance of commercial systems and the diversity of flows in them, in this research, the process mining problem in the context of social commerce systems is studied. Moreover, the research objective is to present a new method for commercial process discovery that has not been considered before. The proposed method is based on network analysis methods, multi-layered networks (networks with heterogeneous relations), and attributed networks. The results obtained from the proposed method are more precise and more comfortable to understand than the previous ones.
... However, key organizational perspectives, in particular roles and resources, are not treated as firstclass abstractions in typical process querying languages. Still, a substantial body of research exists on organization mining [17] 8 , the extraction and analysis of organizational information from event logs. More recently, a first approach to agent system mining [18] has emerged, focusing on the organizational resource that executes particular activities as a first-class abstraction. ...
This paper provides an introduction to and discussion of SIGNAL, an industry-scale process data querying language and engine for large-scale cloud-based systems that is developed by SAP Signavio. SIGNAL is optimized for fast read access to process data in event log format and utilizes an in-memory columnar store to this end. To facilitate usability, SIGNAL uses an SQL-like syntax with additional domain-specific querying features and in particular row-pattern matching-based temporal operators. Also, the paper highlights research challenges related to process querying that are informed by the implementation and application of SIGNAL.
... Les projets de re-conception de processus d'affaires sont utiles pour accroître leur orientation et améliorer l'efficacité et la performance de l'entreprise. L'effort concerté des utilisateurs et / ou des consultants ne peut apporter qu'une valeur à un certain point, puisque la plupart des propriétaires de processus ont des informations limitées concernant ce qui se passe réellement [11], et les analyses sont généralement séparées dans des domaines dispersés sans un aperçu de leur efficacité. ...
... The RO measures each resource pool's percentage occupancy in the log, implying that a new feature is created for each pool to record the occupation-specific variations. Since the information about the size and composition of the resource pools is not always included in the logs, we grouped resources into roles by using the algorithm described in [25]. This algorithm discovers resource pools based on the definition of activity execution profiles for each resource and the creation of a correlation matrix of similarity of those profiles. ...
Business process simulation is a well-known approach to estimate the impact of changes to a process with respect to time and cost measures – a practice known as what-if process analysis. The usefulness of such estimations hinges on the accuracy of the underlying simulation model. Data-Driven Simulation (DDS) methods leverage process mining techniques to learn process simulation models from event logs. Empirical studies have shown that, while DDS models adequately capture the observed sequences of activities and their frequencies, they fail to accurately capture the temporal dynamics of real-life processes. In contrast, generative Deep Learning (DL) models are better able to capture such temporal dynamics. The drawback of DL models is that users cannot alter them for what-if analysis due to their black-box nature. This paper presents a hybrid approach to learn process simulation models from event logs wherein a (stochastic) process model is extracted via DDS techniques, and then combined with a DL model to generate timestamped event sequences. An experimental evaluation shows that the resulting hybrid simulation models match the temporal accuracy of pure DL models, while partially retaining the what-if analysis capability of DDS approaches.
... The study of data mining, although in recent years has attracted the interest of many researchers (Tsumoto et al., 2014), few of these studies nevertheless approach the issue of business reorganisation and human resources, creating a gap in the development of integrated such methods (Huang et al., 2011;Kerzner, 2003;Liu et al., 2013;Ly et al., 2005;Rinderle-ma & Van der Aalst, 2007;Schönig et al., 2016). In contrast, research into the development of automated decision-making tools through the extraction of functional data and the estimation of employee performance does not exist in the literature (Boj et al., 2014;Gurrea et al., 2014;Song &Van der Aalst, 2008;Van der Aalst & Song, 2004). ...
The issue of allocating human resources costs in a realistic manner to products, processes, activities and
individual employees has been an issue of research and practice in the field of accounting during the last
two decades. The major problem is that the cost of calculating human resources costs has proven to be
very high in the vast majority of cases. Also, in the vast majority of approaches the validity of the
calculation of time allocated to resource usage has been questioned putting costing estimations in doubt.
Approaches like activity based costing have been enhanced by incorporating the element of Time and led
to the creation of the so called Time Driven activity based costing approaches. However, even these
approaches are depended on the calculations of unit costs of time usage per resource and in the majority
of cases have also been questioned if unit costs have been calculated correctly and also if the time
allocated to individual employees per activity are realistic. Our research presented in this paper proposes
the use of a process mining approach for human resource cost estimations based on data-records captured
in event logs that enhances existing Time Driven activity based costing approaches.
... The study of data mining, although in recent years has attracted the interest of many researchers (Tsumoto et al., 2014), few of these studies nevertheless approach the issue of business reorganisation and human resources, creating a gap in the development of integrated such methods (Huang et al., 2011;Kerzner, 2003;Liu et al., 2013;Ly et al., 2005;Rinderle-ma & Van der Aalst, 2007;Schönig et al., 2016). In contrast, research into the development of automated decision-making tools through the extraction of functional data and the estimation of employee performance does not exist in the literature (Boj et al., 2014;Gurrea et al., 2014;Song &Van der Aalst, 2008;Van der Aalst & Song, 2004). ...
... Desta maneira, o problema em questão a ser tratado é: como aplicar uma metodologia de mineração de processos que seja factível a área de serviço de pós-venda de concessionária de veículos? Embora o processo de mineração trate do contexto organizacional dos processos de negócios, poucas pesquisas têm sido realizadas na análise de processos de negócios na perspectiva organizacional (SONG e VAN DER AALST, 2008). ...
Objetivo: Como objetivo, busca-se aplicar uma metodologia de mineração de processos nas informações de pós-venda de concessionárias de automóveis, de uma montadora brasileira, analisando como negócio. Referencial teórico: A utilização da mineração de processos na indústria automotiva proporciona diversos benefícios gerenciais, que são refletidos na melhora da qualidade do produto. Quando se trata de concessionária de automóveis e seus serviços de pós-venda, satisfação e/ou reclamações de cliente, evidencia uma escassez na literatura que reforçam o uso da técnica de mineração de processos. Dessa forma, para cobrir tal lacuna, há clara a necessidade desta pesquisa. Metodologia/abordagem: Primeiramente, técnicas de estruturação de dados foram desenvolvidas para a adequação dos dados brutos cedidos pela empresa estudada, para um padrão adequado à mineração de processos. Busca-se assim, obter informações, tendências e padrões em dados de clientes através do sistema de pós-venda destas concessionárias, durante um período e, apresentar o resultado destas análises. Resultados: Como resultados, destaca-se a criação de gráficos e tabelas que mostram a evolução de cada problema, relatados pelos clientes, em quilometragens de revisão e qual foi encontrado pelas concessionárias, baseando-se em palavras-chave citadas pelos clientes no momento da reclamação. Pesquisa, implicações práticas e sociais: As implicações desta pesquisa culminam na melhora da análise e utilização dos dados pela empresa estudada, exaltam a importância da mineração de processos, para encontrar informações escondidas na grande quantidade de dados, gerada por uma empresa multinacional do setor automovível. Originalidade: Nesta pesquisa foi detectada para o setor de concessionárias de automóveis há uma escasses de pesquisas voltadas a mineração de processos. Logo, esta pesquisa, teve como objetivo propor esta metodologia.
... A comprehensive organizational analysis requires a fully designed data model incorporating other organizational information such as resource availability and work capacity. Moreover, even traditional business process simulation approaches do not consider the organizational structure [14,40,43,45,[53][54][55]. Additionally, information on the organizational structure is limited due to the process log [56][57][58]. An integrated organizational ontology expressing various relationships between organizational units was proposed to compensate for this limitation. ...
In the COVID-19 crisis, telecommuting has become one of the most powerful countermeasures against spreading infections. Companies cannot effectively implement telecommuting owing to difficulties predicting organizational performance and future problems and responding to them in advance. Furthermore, even after overcoming the crisis, it is expected that the performance of so-called “ontact” jobs involving telecommuting will increase rapidly in the new typical environment. Nevertheless, there has been no systematic study on a holistic response method considering work interruption time and lead time from work interruption in the ontact work environment. This study predicts organizational performance by modeling the impact of the ontact work environment on organizational performance and presents problem-solving guidelines from three perspectives: business process, organizational structure, and human resource allocation. Additionally, it presents a case study of a simulation model established by extending a previously developed enterprise simulation software. This study presents a scientific model for predicting organizational performance and solving problems in the ontact work environment, which is presently the most significant concern in companies. This facilitates decision-making to minimize damage based on predicting corporate performance in the ontact work environment.
... These models are very effective for analyzing, optimizing and support processes against rapid change [6][7][8]. PM is a new approach that, like a bridge, connects the two areas of data mining and BPM [9][10][11]. ...
The importance of process analysis in engineering, procurement and construction companies (EPC), due to the complexity of the measures, the high level of communication between people, different and non-integrated information systems, as well as the amount of capital involved in these projects is much higher and more challenging. Limited research has been done on exploring business processes in these companies. In this study, in order to better and more accurately analyze the company's performance, three perspectives of process mining (process flow, case and organizational) is analyzed by using the event logs recorded in the supplier selection process. The results of this study led to the identification of challenges in the process, including repetitive loops, duplicate activities, survey of factors affecting the implementation of the process and also examining the relationships between people involved in the project, which can be used to improve the future performance of the company.
... Organizational miner is conducted to mine resource assignments in a specific activity. Several algorithms, such as default mining, doing similar-task, and agglomerative hierarchical clustering, can be used to perform this resource assignment [9]. Statistical analysis is used to analyze the performance based on the event log. ...
This paper presents an initial study and implementation of a simulation framework based on Colored Petri Nets model. We implement this study based on cloud applications that use python programming language mainly for the backend services and React-based framework for its frontend application. The implementation incorporates a process mining technique to construct the flow of the simulation model as a backbone from event logs. The part related to resource assignment is constructed based on social network algorithm also in the process mining field. Other parts of the simulation model, such as performance analysis, resource allocation, decision point of activities, and arrival cases distribution, can be added flexibly using two mechanisms, hardcoded or plug-unplug from pickle file. Based on this mechanism, it is possible to use the result of an instance trained machine learning model that is trained outside of the application as long as it complies with the input-output criteria defined by our application.
... This perspective represents, for example, all the medical examinations undergone by a patient, all the forms filled out by a customer or all the steps required in the manufacturing of a product. The second focuses on a more human-centered point of view [3,4] which aims to consider the resources, i.e. to describe what happens behind the process itself by considering all those who are involved in the actions. This perspective highlights how the organization that harbors the processes is structured and what the relationship between the actors is. ...
In this paper, we focus on the resource perspective of process mining and more precisely on the clustering of resources sharing the same behaviors. This problematic was addressed through the use of a well-known facility layout method: cell formation. We propose an algorithm combining the resource perspective and cell formation approach to make the best use of their respective features. We wish to identify both subgroups of resources that perform similar activities and subgroups of activities performed by common resources. This new hierarchical approach provides new insights into the clustering problematic because of its bi-dimensional clustering. Experiments are considered on synthetic and real data.
... The initial empirical studies proposed the first tools (Herbst & Karagiannis, 2004), and stated limitation such as "refinement of the process data preparation stage to better handle the problem of multiple executions of a node within the same process instance" (Grigori, et al., 2004). Later on, studies concentrated on techniques for conformance checking (Rozinat, et al., kein Datum), process discovery, clustering and visualization (Song & Van der Aalst, 2008). Two sources, which refer to the topic on this thesis in the best way are described in detail in the following: The main focus of this journal is on the implementation of process mining in supply management, which is further characterized in the intra-organizational and interorganizational perspective. ...
For a detailed discussion of process mining, the objective of this paper is the analysis of the successful implementation of process mining in the practical fields of supply chain management. The research comprises the investigation of use cases in companies that are already actively using process mining. Purpose: This research aims to highlight the applicability of process mining in the supply chain management business field. Research Methodology: In order to examine the applicability of process mining in supply chain management a research study was conducted among experts in this business field. Further, theoretical findings were compared to the results and evaluated. Results: Process Mining can be applied very well in the SCM area. The advantages that arise primarily reflect significant potential benefits and improved process throughput times. The information that can be gained from the operational areas supported by process mining is suitable for reliable decisions, both in the tactical and strategic areas. Limitations: The results on the application of process mining show a certain generalization and have to be adapted and adjusted to the respective application case. Contribution: This study is useful, especially for the purchasing and logistics business area.
... Initially, PM research centered on control-flow discovery, i.e. retrieving a process flow model from an event log. While control-flow discovery has remained an important use case (Augusto et al. 2018), PM research has broadened its scope over time to include techniques for checking conformance between a control-flow model and an event log (Carmona et al. 2018), gaining insights in the involvement of resources in a process (Song and van der Aalst 2008), or connecting PM to other techniques such as simulation and predictive process monitoring (Kratsch et al. 2020;Martin et al. 2016;Teinemaa et al. 2019). While many of the state-of-the-art PM algorithms have been integrated into the open-source platform ProM, the use of PM in organizations has been stimulated by the development of commercial tools such as Apromore, Celonis, and Disco (van der Aalst 2016). ...
Process Mining is an active research domain and has been applied to understand and improve business processes. While significant research has been conducted on the development and improvement of algorithms, evidence on the application of Process Mining in organisations has been far more limited. In particular, there is limited understanding of the opportunities and challenges of using Process Mining in organisations. Such an understanding has the potential to guide research by highlighting barriers for Process Mining adoption and, thus, can contribute to successful Process Mining initiatives in practice. In this respect, this paper provides a holistic view of opportunities and challenges for Process Mining in organisations identified in a Delphi study with 40 international experts from academia and industry. Besides proposing a set of 30 opportunities and 32 challenges, the paper conveys insights into the comparative relevance of individual items, as well as differences in the perceived relevance between academics and practitioners. Therefore, the study contributes to the future development of Process Mining, both as a research field and regarding its application in organisations.
... These models are very effective for analyzing, optimizing and support processes against rapid change [6][7][8]. PM is a new approach that, like a bridge, connects the two areas of data mining and BPM [9][10][11]. ...
— Efficient monitoring and quick feedback control are the main requirements of smart cities to guarantee the stability and safety of urban infrastructures. Real-time monitoring in order to detect anomalies leads to the intensive data processing and hence requires a new computing scheme to offer large-scale and low latency services. Fog architecture by extending computing to the edge of the network, provides the ability to accurate and fast detection of abnormal patterns. A hierarchical fog computing architecture and an efficient hyperellipsoidal clustering algorithm presented in previous studies have been applied to identify anomalous behaviors in water distribution grids. We created an urban water distribution grid dataset using Epanet2w simulator software by measuring grid features: pressure and head for several scenarios. We created 12 distinct events (unexpected behavior) with different scales during the simulation time. To evaluate the effectiveness of the hierarchical anomaly detection model in water distribution grids, the data and computing nodes at different layers were executed as docker containers. The evaluation results proved the efficiency of the proposed hierarchical anomaly detection model with a significant reduction in latency compared to the centralized scheme, while reaching a significant detection accuracy compared to the centralized one.
... control-flow, trajectories, activity paths, and care pathways) from the data. Besides the large number of control-flow discovery algorithms, other discovery algorithms help to gain knowledge about how resources work throughout the process, focusing on role discovery [30,31], social networks [32,33], and task prioritisation patterns [34]. Examples of use cases include the discovery of models of gynecological oncology processes showing relations between the organisational units involved and therapeutics pathways that patients received [35,36], or the discovery of collaboration patterns discovered between the physician, nurse and dietitian involved in diabetes treatment [37]. ...
Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.
... Initially, PM research centered on control-flow discovery, i.e. retrieving a process flow model from an event log. While control-flow discovery has remained an important use case (Augusto et al. 2018), PM research has broadened its scope over time to include techniques for checking conformance between a control-flow model and an event log (Carmona et al. 2018), gaining insights in the involvement of resources in a process (Song and van der Aalst 2008), or connecting PM to other techniques such as simulation and predictive process monitoring (Kratsch et al. 2020;Martin et al. 2016;Teinemaa et al. 2019). While many of the state-of-the-art PM algorithms have been integrated into the open-source platform ProM, the use of PM in organizations has been stimulated by the development of commercial tools such as Apromore, Celonis, and Disco (van der Aalst 2016). ...
Process mining is an active research domain and has been applied to understand and improve business processes. While significant research has been conducted on the development and improvement of algorithms, evidence on the application of process mining in organizations has been far more limited. In particular, there is limited understanding of the opportunities and challenges of using process mining in organizations. Such an understanding has the potential to guide research by highlighting barriers for process mining adoption and, thus, can contribute to successful process mining initiatives in practice. In this respect, the paper provides a holistic view of opportunities and challenges for process mining in organizations identified in a Delphi study with 40 international experts from academia and industry. Besides proposing a set of 30 opportunities and 32 challenges, the paper conveys insights into the comparative relevance of individual items, as well as differences in the perceived relevance between academics and practitioners. Therefore, the study contributes to the future development of process mining, both as a research field and regarding its application in organizations.
... Process mining is a research discipline at the intersection of Data Science and BPM which provides techniques for extracting insights from process execution data [1]. Organisational mining is an area of process mining concerned with extracting knowledge about behaviour of resources in processes [24], [29]. ...
Performance of business processes is affected by capabilities of employees who execute activities in these processes. Resource allocation approaches aim to identify an optimal allocation of resources to activities which supports the achievement of some process performance goals (e.g., in terms of time or cost). The majority of resource allocation approaches proposed in the Business Process Management community are process-centric (i.e., they aim to optimise process performance) and they neglect capability development needs of employees. In this article, we propose a novel approach for recommending unfamiliar process activities to employees which is based on the application of machine learning techniques to information extracted from process execution data. The goal of the approach is to assist organisations in their quest for capacity development by providing employees with opportunities to gain experience through the execution of new activities. The approach was implemented and evaluated by conducting experiments with real publicly available event logs. In the experiments, we compared the predictions provided by the approach with actual activity executions recorded in the logs. The experiments demonstrated the effectiveness of different approach configurations and showed that this machine-learning based approach significantly outperforms an existing algorithm proposed in earlier work.
... The evaluated PDFs correspond to those supported by the BIMP simulator (i.e., normal, lognormal, gamma, exponential, uniform, and triangular distributions). The resource pool is discovered using the algorithm proposed by Song & Van der Aalst (2008); likewise, the resources are assigned to the different activities according to the frequency of execution. Finally, Simod discovers calendar expressions that capture the resources' time availability restricting the hours they can execute tasks. ...
A generative model is a statistical model capable of generating new data instances from previously observed ones. In the context of business processes, a generative model creates new execution traces from a set of historical traces, also known as an event log. Two types of generative business process models have been developed in previous work: data-driven simulation models and deep learning models. Until now, these two approaches have evolved independently, and their relative performance has not been studied. This paper fills this gap by empirically comparing a data-driven simulation approach with multiple deep learning approaches for building generative business process models. The study sheds light on the relative strengths of these two approaches and raises the prospect of developing hybrid approaches that combine these strengths.
... Other Process Mining perspectives may require events to contain additional attributes. For example, the organizational perspective requires data on resources such as people, roles, teams, or technological entities that executed the activities [3]. ...
In the Agent-Based Modeling (ABM) paradigm, an organization is a Multi-Agent System (MAS) composed of autonomous agents inducing business processes. Process Mining automates the creation, update, and analysis of explicit business process models based on event data. Process Mining techniques make simplifying assumptions about the processes discovered from data. However, actual business processes are often more complex than those restricted by Process Mining assumptions. Several Process Mining approaches relax these standard assumptions by discovering more realistic process models. These approaches can discover more realistic process models. However, these models are often difficult to visualize and, consequently, to understand. Many MASs induce processes whose behaviors become more complex with each next embraced time step, while the complexities of these MASs remain constant. Thus, the ABM paradigm can cope naturally with the increasing complexity of the discovered process models. This paper proposes Agent System Mining (ASM) and ASM Framework. ASM combines Process Mining and ABM in the Business Process Management (BPM) context to infer MAS models of operational business processes from real-world event data, while ASM Framework maps ASM activities to different phases of the MAS modeling lifecycle. The paper also discusses the benefits of using ASM and outlines challenges associated with the implementation of the ASM Framework.
... Questions and scenarios are based on the design choices, which are highlighted by the process mining insights. As shown in Fig. 3, process discovery [1], conformance checking [6], performance analysis, and organizational mining [14] results enable designing the simulation scenarios and models. These insights should be quantified in order to be put into action. ...
Most process mining techniques are backward-looking, i.e., event data are used to diagnose performance and compliance problems. The combination of process mining and simulation allows for forward-looking approaches to answer “What if?” questions. However, it is difficult to create fine-grained simulation models that describe the process at the level of individual events and cases in such a way that reality is captured well. Therefore, we propose to use coarse-grained simulation models (e.g., System Dynamics) that simulate processes at a higher abstraction level. Coarse-grained simulation provides two advantages: (1) it is easier to discover models that mimic reality, and (2) it is possible to explore alternative scenarios more easily (e.g., brainstorming on the effectiveness of process interventions). However, this is only possible by bridging the gap between low-level event data and the coarse-grained process data needed to create higher-level simulation models where one simulation step may correspond to a day or week. This paper provides a general approach and corresponding tool support to bridge this gap. We show that we can indeed learn System Dynamics models from standard event data.
... To visually analyse the organisational perspective of a business process, a handoverof-work network can be constructed [126]. This is a directed graph that shows how cases are handed over from one resource to the next. ...
In this chapter, we evaluate the use of bupaR in the context of education.
... To visually analyse the organisational perspective of a business process, a handoverof-work network can be constructed [126]. This is a directed graph that shows how cases are handed over from one resource to the next. ...
This book is a revised version of the PhD dissertation written by the author at Hasselt University in Belgium.This dissertation introduces the concept of process realism. Process realism is approached from two perspectives in this dissertation. First, quality dimensions and measures for process discovery are analyzed on a large scale and compared with each other on the basis of empirical experiments. It is shown that there are important differences between the different quality measures in terms of feasibility, validity and sensitivity. Moreover, the role and meaning of the generalization dimension is unclear. Second, process realism is also tackled from a data point of view. By developing a transparent and extensible tool-set, a framework is offered to analyze process data from different perspectives. From both perspectives, recommendations are made for future research, and a call is made to give the process realism mindset a central place within process mining analyses.
In 2020, the PhD dissertation won the “BPM Dissertation Award”, granted to outstanding PhD theses in the field of Business Process Management.
... Such event logs must contain information about resources (i.e., employees) who executed process activities. Organisational mining techniques can help to discover organisational models and social networks, and by uncovering actual social structures in an organisation, they can provide insights for process improvement [51]. Organisational mining can also help to analyse behaviours of teams or individual employees, e.g., this can be done with the help of an extensible framework for resource behaviour analysis [39]. ...
The management of health and safety plays an important role in safety performance, and is therefore an important foundational element in an organisation's overall sustainable development. Many organisations are now able to collect vast amounts of data being in an attempt to shed light on the underlying causes behind accidents and safety-related incidents, and to spot patterns that can lead to solutions. Despite these well-intentioned Big Data collection efforts, however, accident statistics in asset-intensive industries remain stubbornly high as the data frequently fails to reveal actionable insights. In this paper, we answer Wang and Wu's (2020) [60], [60] and Wang et al.'s (2019) [61], [61] calls for the application of Big Data science to the safety domain by exploring the potential of applying tools and techniques from process mining, a research area concerned with analysing process execution data, to derive novel insights from and improve the visualisation of safety process data. We demonstrate how these tools can yield useful insights in the occupational health and safety domain by analysing process execution data from a Permit to Work system in an Australian energy company. Specifically, the analysis presented here highlights the underlying complexity of the organisation's Permit to Work process, reveals conformance and performance issues, and uncovers resources associated with conformance issues and changes in the frequency of such issues over time, thereby underlining the need to simplify the system. Encouraged by these fresh perspectives and insights delivered by process mining, we hope that this novel application will be a catalyst for further research at the interface between these research disciplines.
... In any case, even if they use the resource information, they do not use the raw resource data. In fact, they resort to the algorithm described in [41] to group the resources in roles based on information enclosed in the resource activity profiles. They use the roles, instead of the raw resources, in the input data by handling an activity-based representation of the resource view. ...
The predictive business process monitoring is a family of online approaches to predict the unfolding of running traces basedon the knowledge learned from historical event logs. In this paper, we address the task of predicting the next trace activity from the completed events in a running trace. This is an important business capability as counting on accurate predictions of the future activities may allow companies to guarantee the higher utilization by acting proactively in anticipation. We propose a novel predictive process approach that couples multi-view learning and deep learning, in order to gain predictive accuracy by accounting for the variety ofinformation possibly recorded in event logs. Experiments with various benchmark event logs prove the effectiveness of the proposed approach compared to several recent state-of-the-art methods.
... They also apply these concepts to a real-life event log. In [18], the authors build on these and extend the approach to discover organisational models from event logs. ...
Work-induced stress is widely acknowledged as harming physical and psychosocial health and has been linked with adverse outcomes such as a decrease in productivity. Recently, workplace stressors have increased due to the COVID-19 pandemic. This study aims to contribute to the literature base in a couple of areas. First, it extends the current knowledge base by utilising generative additive modelling (GAMs) to uncover the nature of the relationship between workload (a key workplace stressor) and productivity based on real-world event logs. Additionally, it uses recursive partitioning modelling to shed light on the factors that drive the relationship between these variables. Secondly, it utilises a simulation-based approach to investigate the diffusion of workload-induced stress in the workplace. Simulation is a valuable tool for exploring the effect of changes in a risk-free manner as it provides the ability to run multiple scenarios in a safe and virtual environment with a view to making recommendations to stakeholders. However, there are several recognised issues with traditional simulation approaches, such as inadequate resource modelling and the limited use of simulations for operational decision making. In this study, we propose an approach which extracts the required parameters from an event log and subsequently utilises them to initialise a workload-induced stress diffusion simulation model accurately. We also explore the effects of varying the parameters to control the spread of workload-induced stress within the network. With suitable amendments, this approach can be extended to model the spread of disease (e.g., COVID-19), diffusion of ideas, among other things, in the workplace.
Interactions among employees promote spread of infectious diseases at workplaces. Enterprise architecture provides rich information about organizational structure and its relations to other elements in an enterprise. This paper develops a workplace topology model as a sub-set of enterprise architecture to analyze both static and dynamic interactions among the employees and to monitor and to limit spread of infectious diseases. The model is developed as a multi-layer graph combining organizational, facility and sensing layers. The graph analytical methods are elaborated to analyze the interactions. The analysis provides inputs to spread of infectious diseases risk assessment models. The application of the graph analytics methods is demonstrated using an example from an Information Technology consulting company producing both software and hardware products.
Process event data is a fundamental building block for process mining as event logs portray the execution trails of business processes from which knowledge and insights can be extracted. In this Chapter, we discuss the core structure of event logs, in particular the three main requirements in the form of the presence of case IDs, activity labels, and timestamps. Moreover, we introduce fundamental concepts of event log processing and preparation, including data sources, extraction, correlation and abstraction techniques. The chapter is concluded with an imperative section on data quality, arguably the most important determinant of process mining project success.
Process-orientation has gained significant momentum in manufacturing as enabler for the integration of machines, sensors, systems, and human workers across all levels of the automation pyramid. With process orientation comes the opportunity to collect manufacturing data in a contextualized and integrated way in the form of process event logs (no data silos) and with that data, in turn, the opportunity to exploit the full range of process mining techniques. Process mining techniques serve three tasks, i.e., (i) the discovery of process models based on process event logs, (ii) checking the conformance between a process model and process event logs, and (iii) enhancing process models. Recent studies show that particularly, (ii) and (iii) have become increasingly important. Conformance checking during run-time can help to detect deviations and errors in manufacturing processes and related data (e.g., sensor data) when they actually happen. This facilitates an instant reaction to these deviations and errors, e.g., by adapting the processes accordingly (process enhancement), and can be taken as input for predicting deviations and errorsfor future process executions. This chapter discusses process mining in the context of manufacturing processes along the phases of an analysis project, i.e., preparation and analysis of manufacturing data during design and run-time and the visualization and interpretation of process mining results. In particular, this chapter features recommendations on how to employ which process mining technique for different analysis goals in manufacturing.
Collaborative work leads to better organizational performance. However, a team leader’s view on collaboration does not always match reality. Due to the increased adoption of (online) collaboration systems in the wake of the COVID pandemic, more digital traces on collaboration are available for a wide variety of use cases. These traces allow for the discovery of accurate and objective insights into a team’s inner workings. Existing social network discovery algorithms however, are often not tailored to discover collaborations. These techniques often have a different view on collaboration by mostly focusing on handover of work, resource profile similarity, or establishing relationships between resources when they work on the same case or activities without any restrictions. Furthermore, only the frequency of appearance of patterns is typically used as a measure of interestingness, which limits the kind of insights one can discover. Therefore we propose an algorithm to discover collaborations from event data using a more realistic approach than basing collaboration on the sequence of resources that carry out activities for the same case. Furthermore, a new research path is explored by adopting the Recency-Frequency-Monetary (RFM) concept, which is used in the marketing research field to assess customer value, in this context to value both the resource and the collaboration on these three dimensions. Our approach and the benefits of adopting RFM to gain insights are empirically demonstrated on a use case of collaboratively developing a curriculum.
Process analytics approaches allow organizations to support the practice of Business Process Management and continuous improvement by leveraging all process-related data to extract knowledge, improve process performance and support decision-making across the organization. Process execution data once collected will contain hidden insights and actionable knowledge that are of considerable business value enabling firms to take a data-driven approach for identifying performance bottlenecks, reducing costs, extracting insights and optimizing the utilization of available resources. Understanding the properties of 'current deployed process' (whose execution trace is often available in these logs), is critical to understanding the variation across the process instances, root-causes of inefficiencies and determining the areas for investing improvement efforts. In this survey, we discuss various methods that allow organizations to understand the behaviour of their processes, monitor currently running process instances, predict the future behavior of those instances and provide better support for operational decision-making across the organization.
The COVID-19 pandemic has affected virtually every human activity over the past 2 years. This paper examines how the COVID-19 pandemic interfered with the business processes in Brazil’s public vocational and higher education institution. Throughout the pandemic, the Organization forced the enactment of the paper-recorded processes in a virtual implementation. To unveil how the referred paper-recorded processes subset got executed during the pandemic, we conduct a process mining on the company’s information system. The process mining data shows various indications of task merging, precluding, and duration modifications. The analysis of 4231 instances of administrative processes between 2019 and 2021 showed a reduction in duration times and the number of tasks.KeywordsProcess miningPandemicCOVID-19
Healthcare organisations are becoming increasingly aware of the need to improve their care processes and to manage their scarce resources efficiently to secure high-quality care standards. As these processes are knowledge-intensive and heavily depend on human resources, a comprehensive understanding of the complex relationship between processes and resources is indispensable for efficient resource management. Organisational mining, a subfield of Process Mining, reveals insights into how (human) resources organise their work based on analysing process execution data recorded in Health Information Systems (HIS). This can be used to, e.g., discover resource profiles which are groups of resources performing similar activity instances, providing an extensive overview of resource behaviour within healthcare organisations. Healthcare managers can employ these insights to allocate their resources efficiently, e.g., by improving the scheduling and staffing of nurses. Existing resource profiling algorithms are limited in their ability to apprehend the complex relationship between processes and resources because they do not take into account the context in which activities were executed, particularly in the context of multitasking. Therefore, this paper introduces ResProMin–MT to discover context-aware resource profiles in the presence of multitasking. In contrast to the state-of-the-art, ResProMin–MT is capable of taking into account more complex contextual activity dimensions, such as activity durations and the degree of multitasking by resources. We demonstrate the feasibility of our method within a real-life healthcare context, validated by medical domain experts.
Robotic Process Automation (RPA) is an emerging automation technology that creates software (SW) robots to partially or fully automate rule-based and repetitive tasks (aka routines) previously performed by human users in their applications’ user interfaces (UIs). Successful usage of RPA requires strong support by skilled human experts, from the detection of the routines to be automated to the development of the executable scripts required to enact SW robots. In this paper, we discuss how process mining can be leveraged to minimize the manual and time-consuming steps required for the creation of SW robots, enabling new levels of automation and support for RPA. We first present a reference data model that can be used for a standardized specification of UI logs recording the interactions between workers and SW applications to enable interoperability among different tools. Then, we introduce a pipeline of processing steps that enable us to (1) semi-automatically discover the anatomy of a routine directly from the UI logs, and (2) automatically develop executable scripts for performing SW robots at run-time. We show how this pipeline can be effectively enacted by researchers/practitioners through the SmartRPA tool.KeywordsRobotic Process AutomationProcess miningUser Interface (UI) logsReference data model for UI logsSegmentationAutomated generation of SW robots from UI LogsSmartRPA
Process discovery aims at analysing the execution logs of information systems (IS), used when performing business activities, for discovering business process (BP) knowledge. Significant research works has been conducted in such area. However, they generally assume that these execution logs are of high or of middle level of maturity w.r.t BP discovery. This means that (i) they are composed of structured records while each one captures evidence of one activity execution, and (ii) a part of events’ attributes (e.g. activity name, timestamp) are explicitly included in these records which facilitates their inference. Nevertheless, BP can be entirely or partially performed through less structured IS generating execution logs of low level of maturity. More precisely, emailing systems are widely used as an alternative tool to collaboratively perform BP tasks. Traditional BP discovery techniques could not be applied or at least not directly applied due to the unstructured nature of email logs data. Recently, there have been several initiatives to extend the scope of BP discovery to consider email logs. However, most of them: (i) mostly require human intervention, and (ii) were limited to BP discovery according to its behavioral perspective. In this thesis, we propose to discover BP fragments from email logs w.r.t their functional, data, organizational and behavioral perspectives. We first formalize these perspectives considering emailing systems specifities. We introduce the notion of actors’ contributions towards performing activities to enrich the organizational and the behavioral perspectives. We additionally consider the informational entities manipulated by BP activities to describe the data perspective. To automate their discovery, we introduce a completely unsupervised approach. This approach mainly transforms the unstructured email log into a structured event log before mining it for discovering BP w.r.t multiple perspectives. We introduce in this context several algorithmic solutions for: (i) unsupervised learning activities based on discovering frequent patterns of words from emails, (ii) discovering activity occurrences in emails for capturing event attributes, (iii) discovering speech acts of activity occurrences for recognizing the sender purposes of including activities in emails, (iv) overlapping clustering of activities to discover their manipulated artifacts (i.e. informational entities), and (v) mining sequencing constraints between event types to discover BP behavioral perspective. We validated our approach using emails from the public dataset Enron to show the effectiveness of the obtained results. We publically provide these results to ensure reproducibility in the studied area. We finally show the usefulness of our results for improving BPM through two potential applications: (i) a BP discovery & recommendation tool to be integrated in emailing systems, and (ii) CRM data analysis for mining reasons of users’ satisfaction/non-satisfaction.
Process mining can provide valuable insights in business processes using an event log containing process execution data. Despite the significant potential of process mining to support the analysis and improvement of processes, the reliability of process mining outcomes depends on the quality of the event log. Real-life logs typically suffer from various data quality issues. Consequently, thorough event log quality assessment is required before applying process mining algorithms. This paper introduces DaQAPO, the first R-package which supports flexible and fine-grained event log quality assessment. It provides a rich set of tests to identify a wide range of event log quality issues, while having sufficient flexibility to allow the detection of context-specific quality issues.
In recent years, process mining has been used quite extensively for security analysis. Yet, we have limited understanding of the use of process mining for Role Based Access Control (RBAC) analysis. We carried out a Systematic Literature Review (SLR) to fill this gap and, in particular, to identify what are the main characteristics of the current approaches for process mining for RBAC analysis, what are the problems and what are the benefits. We have analysed 27 publications that discuss 40 approaches. The results show that using process mining for RBAC analysis is very promising, but that it is still in its early stages and more effort is required.
Due to growing digital opportunities, persistent legislative pressure, and recent challenges in the wake of the COVID-19 pandemic, public universities need to engage in digital innovation (DI). While society expects universities to lead DI efforts, the successful development and implementation of DIs, particularly in administration and management contexts, remains a challenge. In addition, research lacks knowledge on the DI process at public universities, while further understanding and guidance are needed. Against this backdrop, our study aims to enhance the understanding of the DI process at public universities by providing a structured overview of corresponding drivers and barriers through an exploratory single case study. We investigate the case of a German public university and draw from primary and secondary data of its DI process from the development of three specific digital process innovations. Building upon Business Process Management (BPM) as a theoretical lens to study the DI process, we present 13 drivers and 17 barriers structured along the DI actions and BPM core elements. We discuss corresponding findings and provide related practice recommendations for public universities that aim to engage in DI. In sum, our study contributes to the explanatory knowledge at the convergent interface between DI and BPM in the context of public universities.
Business process management organizes work into several interrelated “units of work”, fundamentally conceptualized as a task. The classical concept of a task as a single step executed by a single actor in a single case fails to capture more complex aspects of work that occur in real-life processes. For instance, actors working together or the processing of work in batches, where multiple actors and/or cases meet for a number of steps. Established process mining and modeling techniques lack concepts for dealing with these more complex manifestations of work. We leverage event graphs as a data structure to model behavior along the actor and the case perspective in an integrated model, revealing a variety of fundamentally different types of task executions. We contribute a novel taxonomy and interpretation of these task execution patterns as well as techniques for detecting these in event graphs, complementing recent research in identifying patterns of work and their changes in routine dynamics. Our evaluation on two real-life event logs shows that these non-classical task execution patterns not only exist, but make up for the larger share of events in a process and reveal changes in how actors do their work.
Efficient resource management is a critical success factor for all businesses. Correct insights into actual resource profiles, i.e. groups of resources performing similar activity instances, is important for successful knowledge and (human) resource management. To this end, organisational mining, a subfield of Process Mining, focuses on techniques to extract such resource profiles from event logs. However, existing techniques ignore contextual factors that impact how and by whom an activity is performed. This paper introduces the novel method ResProMin to discover context-aware resource profiles from event logs. In contrast to the state-of-the-art, this method builds upon the notion of activity instance archetypes, which incorporates the activity instance’s context. An evaluation of the method on real-life event logs demonstrates its feasibility and potential to uncover valuable business insights.
Das Denken in Prozessen löst die durch starre Hierarchiemuster geprägte Aufbauorganisation durch eine an bereichsübergreifenden Prozessen ausgerichtete Ablauforganisation ab. Die Prozessorientierung ist im Gegensatz zur Ablauforganisation nicht auf Stellen oder Abteilungen beschränkt, sondern zielt auf die ganzheitliche Optimierung des gesamten Wertschöpfungsprozesses ab. Das heutige Prozessverständnis ist gerade von dem Gedanken einer übergreifenden Sichtweise gekennzeichnet, d. h Prozesse werden unabhängig von organisatorischen und funktionalen Bereichen betrachtet.
Während bei der Datenbereitstellung die Transformation und Speicherung der Daten im Vordergrund stehen, beschäftigt sich das folgende Kapitel mit deren anschließender Aufbereitung und Bereitstellung. Hierbei werden zunächst Komponenten erläutert, mit denen die Daten in eine aus Anwendersicht informationstragende Form konvertiert werden – durch ihre Exploration, ihre Überführung in Berichtsstrukturen, anspruchsvollere modellgestützte Analysen (Advanced und Predictive Analytics) sowie eine kombinierte Nutzung verschiedener Analyseverfahren zur Umsetzung spezifischer fachlicher Konzepte.
Most information systems log events (e.g., transaction logs, audit traits) to audit and monitor the processes they support. At the same time, many of these processes have been explicitly modeled. For example, SAP R/3 logs events in transaction logs and there are EPCs (Event-driven Process Chains) describing the so-called reference models. These reference models describe how the system should be used. The coexistence of event logs and process models raises an interesting question: "Does the event log conform to the process model and vice versa?". This paper demonstrates that there is not a simple answer to this question. To tackle the problem, we distinguish two dimensions of conformance: fitness (the event log may be the result of the process modeled) and appropriateness (the model is a likely candidate from a structural and behavioral point of view). Different metrics have been defined and a Conformance Checker has been implemented within the ProM Framework
Workflow management systems enable the exact and timely analysis of automated business processes through the analysis of logged audit trail data. Within the research project CONGO, we develop a process analysis tool (PISA) that can be employed to analyze the audit trail data of different workflow management systems in conjunction with target data from business process modeling tools. A working prototype has been completed that integrates data of the ARIS Toolset and IBM MQSeries Workflow. The analysis focuses on three different perspectives: processes and functions, involved resources, and process objects. We outline the economic aspects of workflow based process monitoring and control and the current state of the art in monitoring facilities provided by current workflow management systems and existing standards. After a discussion of the three evaluation perspectives, sample evaluation methods for each perspective are discussed. The concept and architecture of PISA are described and implementation issues are outlined before an outlook on further research is given.
In this paper, the authors present a methodology for quantifying the degree of fit between a mission and an organization based on the closeness between the task structure (i.e., resource requirements and task interdependence) and the Decision Maker-asset (DM) allocation across the organization (i.e., amount and distribution of resource capabilities among DMs and organizational processes). This closeness is based on three main characteristics of organizational performance: workload balance, communication requirements, and DM-DM dependence. These characteristics are affected, in turn, by the interactions and interdependencies of the organizational processes and the demands of the mission scenario. Invariably, coordination is essential to achieve good performance because the information required for decision making is often distributed. However, excessive DM-DM communication and coordination are harmful to performance, since they increase the processing workload/overhead that delays task execution. Performance improvements can be obtained by changing the structure and processes of an organization to decrease the requisite coordination, while balancing the levels of workload across the organization and reducing inter-DM dependence.
Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and typically there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, we propose a technique for process mining. This technique uses workflow logs to discover the workflow process as it is actually being executed. The process mining technique proposed in this paper can deal with noise and can also be used to validate workflow processes by uncovering and measuring the discrepancies between prescriptive models and actual process executions.
Business Process Management (BPM) systems provide a broad range of facilities to enact and manage operational business processes. Ideally, these systems should provide support for the complete BPM life-cycle: (re)design, configuration, execution, control, and diagnosis of processes. In the research presented, we evaluate the support provided by the FileNet P8 BPM Suite, which is consistently ranked as one of the leading commercial BPM systems. Taking realistic business scenarios as starting point, we completed a full pass through the BPM cycle with several tools from the FileNet P8 BPM Suite. We checked whether the expected support was provided by these tools and we also tested their interoperability. The outcome of our evaluation is that although strong support exists for the configuration, execution and control phase, process diagnosis and process redesign receive limited support. Interoperability exists between all phases, except between the diagnosis and the design phase.
Business processes automation requires the specification of process structures as well as the definition of resources involved in the execution of these processes. While the modeling of business processes and workflows is well researched, the link between the organizational elements and process activities is less well understood, and current developments in the web services choreography area completely neglect the organizational aspect of workflow applications. The purpose of this paper is to give an overview of the organizational aspects of workflow technology in the context of the workflow life cycle, to provide a review of existing work, and to develop guidelines for the design of a workflow-enabled organization, which can be used by both workflow vendors and users.
Information systems are facing conflicting requirements. On the one hand, systems need to be adaptive and self-managing to
deal with rapidly changing circumstances. On the other hand, legislation such as the Sarbanes-Oxley Act, is putting increasing
demands on monitoring activities and processes. As processes and systems become more flexible, both the need for, and the
complexity of monitoring increases. Our earlier work on process mining has primarily focused on process discovery, i.e., automatically constructing models describing knowledge extracted from event logs. In this paper, we focus on a different
problem complementing process discovery. Given an event log and some property, we want to verify whether the property holds. For this purpose we have developed a new language based on Linear Temporal Logic (LTL) and we
combine this with a standard XML format to store event logs. Given an event log and an LTL property, our LTL Checker verifies
whether the observed behavior matches the (un)expected/(un)desirable behavior.
KeywordsProcess mining-temporal logic-business process management-workflow management-data mining-Petri nets
For several reasons enterprises are frequently subject to organizational change. Respective adaptations may concern business processes, but also other components of an enterprise architecture. In particular, changes of organizational structures often become necessary.
The information about organizational entities and their relationships is maintained in organizational models. Therefore the quick and correct adaptation of these models is fundamental to adequately cope with changes. However, model changes alone are not sufficient to guarantee consistency. Since organizational models also provide the basis for defining access rules (e.g., actor assignments in workflow management systems or access rules in document–centered applications) this information has to be adapted accordingly (e.g., to avoid non-resolvable actor assignments). Current approaches do not adequately address this problem, which often leads to security gaps and delayed change adaptations.
In this paper we present a comprehensive approach for the controlled evolution of organizational models in cooperative information systems. First, we introduce a set of operators with well-defined semantics for defining and changing organizational models. Second, we present an advanced approach for the semi-automated adaptation of access rules when the underlying organizational model is changed. This includes a formal part concerning both the evolution of organizational models and the adaptation of related access rules.
Many of today’s information systems are driven by explicit process models. Workflow management systems, but also ERP, CRM, SCM, and B2B, are configured on the basis of a workflow model specifying the order in which tasks need to be executed. Creating a workflow design is a complicated time-consuming process and typically there are discrepancies between the actual workflow processes and the processes as perceived by the management. To support the design of workflows, we propose the use of workflow mining. Starting point for workflow mining is a so-called “workflow log” containing information about the workflow process as it is actually being executed. In this paper, we introduce the concept of workflow mining and present a common format for workflow logs. Then we discuss the most challenging problems and present some of the workflow mining approaches available today.
Process-aware Information Systems typically log events (e.g., in transaction logs or audit trails) related to the actual business
process executions. Proper analysis of these execution logs can yield important knowledge that can help organizations to improve
the quality of their services. Starting from a process model, which can be discovered by conventional process mining algorithms,
we analyze how data attributes influence the choices made in the process based on past process executions. Decision mining,
also referred to as decision point analysis, aims at the detection of data dependencies that affect the routing of a case.
In this paper we describe how machine learning techniques can be leveraged for this purpose, and we present a Decision Miner implemented within the ProM framework.
KeywordsBusiness Process Intelligence–Process Mining–Petri Nets–Decision Trees
The application of process mining techniques to real-life corporate environments has been of an ad-hoc nature so far, focused on proving the concept. One major reason for this rather slow adoption has been the complicated task of transforming real-life event log data to the MXML format used by advanced process mining tools, such as ProM. In this paper, the ProM Import Framework is presented, which has been designed to bridge this gap and,to build a stable foundation for the extraction of event log data from any given PAIS implementation. Its flexible and extensible architecture, adherence to open standards, and open source availability make,it a versatile contribution to the general BPI community.
In the main, the attention of workflow researchers and workflow developers has focussed on the process perspective, i.e., control-flow. As a result, issues associated with the resource perspective, i.e., the people and machines actually doing the work, have been largely neglected. Although the process perspective is of most significance, appropriate consideration of the resource perspective is essential for successful implementation of workflow technology. Previous work has identified recurring, generic constructs in the control-flow and data perspectives, and presented them in the form of control-flow and data patterns. The next logical step is to describe workflow resource patterns that capture the various ways in which resources are represented and utilised in workflows. These patterns include a number of distinct groupings such as push patterns ("the system pushes work to a worker") and pull patterns ("the worker pulls work from the system") to describe the many ways in which work can be distributed. By delineating these patterns in a form that is independent of specific workflow technologies and modelling languages, we are able to provide a comprehensive treatment of the resource perspective and we subsequently use these patterns as the basis for a detailed comparison of a number of commercially available workflow management systems.
Under the umbrella of buzzwords such as “Business Activity Monitoring” (BAM) and “Business Process Intelligence” (BPI) both
academic (e.g., EMiT, Little Thumb, InWoLvE, Process Miner, and MinSoN) and commercial tools (e.g., ARIS PPM, HP BPI, and
ILOG JViews) have been developed. The goal of these tools is to extract knowledge from event logs (e.g., transaction logs
in an ERP system or audit trails in a WFM system), i.e., to do process mining. Unfortunately, tools use different formats for reading/storing log files and present their results in different ways. This
makes it difficult to use different tools on the same data set and to compare the mining results. Furthermore, some of these
tools implement concepts that can be very useful in the other tools but it is often difficult to combine tools. As a result,
researchers working on new process mining techniques are forced to build a mining infrastructure from scratch or test their
techniques in an isolated way, disconnected from any practical applications. To overcome these kind of problems, we have developed
the ProM framework, i.e., an “pluggable” environment for process mining. The framework is flexible with respect to the input
and output format, and is also open enough to allow for the easy reuse of code during the implementation of new process mining
ideas. This paper introduces the ProM framework and gives an overview of the plug-ins that have been developed.
Workflow management systems support business processes and are driven by their models. These models cover different perspectives including the control-flow, resource, and data perspectives. This paper focuses on the resource perspective, i.e., the way the system distributes work based on the structure of the organization and capabilities/qualifications of people. Contemporary workflow management systems offer a wide variety of mechanisms to support the resource perspective. Because the resource perspective is essential for the applicability of such systems, it is important to better understand the mechanisms and their interactions. Our goal is not to evaluate and compare what different systems do, but to understand how they do it. We use Colored Petri Nets (CPNs) to model work distribution mechanisms. First, we provide a basic model that can be seen as a reference model of existing workflow management systems. This model is then extended for three specific systems (Staffware, FileNet, and FLOWer). Moreover, we show how more advanced work distribution mechanisms, referred to as resource patterns, can be modelled and analyzed.
In recent years, workflow management systems (WFMSs) have gained popularity in both research and commercial sectors. WFMSs are used to coordinate and streamline business processes. Very large WFMSs are often used in organizations with users in the range of several thousands and process instances in the range of tens and thousands. To simplify the complexity of security administration, it is common practice in many businesses to allocate a role for each activity in the process and then assign one or more users to each role—granting an authorization to roles rather than to users. Typically, security policies are expressed as constraints (or rules) on users and roles; separation of duties is a well-known constraint. Unfortunately, current role-based access control models are not adequate to model such constraints. To address this issue we (1) present a language to express both static and dynamic authorization constraints as clauses in a logic program; (2) provide formal notions of constraint consistency; and (3) propose algorithms to check the consistency of constraints and assign users and roles to tasks that constitute the workflow in such a way that no constraints are violated.
Today's workflow management systems offer work items to workers using rather primitive mechanisms. Although most workflow systems support a role-based distribution of work, they have problems dealing with unavailability of workers as a result of vacation or illness, overloading, context-dependent suitability, deadlines, and delegation. As a result, the work is offered to too few, too many, or even the wrong set of workers. Current practice is to offer a work item to one person, thus causing problems when the person is not present or too busy, or to offer it to a set of people sharing a given role, thus not incorporating the qualifications and preferences of people. Literature on work distribution is typically driven by considerations related to authorizations and permissions. However, workflow processes are operational processes where there is a highly dynamic trade-off between quality and performance. For example, an approaching deadline and an overloaded specialist may be the trigger to offer work items to less qualified workers. This paper addresses this problem by proposing a systematic approach to dynamically create a balance between quality and performance issues in workflow systems. We illustrate and evaluate the proposed approach with a realistic example and also compare how a workflow system would implement this scenario to highlight the shortcomings of current, state of the art workflow systems. Finally, a detailed simulation model is used to validate our approach.
esearch in organizational learning has demonstrated processes and occasionally performance implications of acquisition of declarative (know-what) and procedural (know-how) knowledge. However, considerably less attention has been paid to learned char- acteristics of relationships that affect the decision to seek information from other people. Based on a review of the social network, information processing, and organizational learn- ing literatures, along with the results of a previous qualitative study, we propose a formal model of information seeking in which the probability of seeking information from another person is a function of (1) knowing what that person knows; (2) valuing what that person knows; (3) being able to gain timely access to that person's thinking; and (4) perceiving that seeking information from that person would not be too costly. We also hypothesize that the knowing, access, and cost variables mediate the relationship between physical proximity and information seeking. The model is tested using two separate research sites to provide repli- cation. The results indicate strong support for the model and the mediation hypothesis (with the exception of the cost variable). Implications are drawn for the study of both transactive memory and organizational learning, as well as for management practice. (Information; Social Networks; Organizational Learning; Transactive Knowledge)
Workflow systems are often associated with business process re-engineering (BPR). This paper argues that the functional access control requirements in workflow systems are rooted in the scope of a BPR project. A framework for access control in workflow systems is developed. The framework suggests that existing role-based access control mechanisms can be used as a foundation in workflow systems. The framework separates the administration-time and the run-time aspects. Key areas that must be investigated to meet the functional requirements imposed by workflow systems on access control services are identified.
Process mining techniques allow for the discovery of knowledge based on so-called "event logs", i.e., a log recording the execution of activities in some business process. Many information systems provide such logs, e.g., most WFM, ERP, CRM, SCM, and B2B systems record transactions in a systematic way. Process mining techniques typically focus on performance and control-flow issues. However, event logs typically also log the performer, e.g., the person initiating or completing some activity. This paper focuses on mining social networks using this information. For example, it is possible to build a social network based on the hand-over of work from one performer to the next. By combining concepts from workflow management and social network analysis, it is possible to discover and analyze social networks. This paper defines metrics, presents a tool, and applies these to a real event log within the setting of a large Dutch organization.
We introduce policy resolution (PR) for workflow management
systems (WFMS) as service to assign work to agents. Policy resolution is
a framework for defining arbitrary role and organization models together
with operations suiting the needs of workflow management systems
Process mining techniques have recently received notable attention in the literature; for their ability to assist in the (re)design of complex processes by automatically discovering models that explain the events registered in some log traces provided as input. Following this line of research, the paper investigates an extension of such basic approaches, where the identification of different variants for the process is explicitly accounted for, based on the clustering of log traces. Indeed, modeling each group of similar executions with a different schema allows us to single out "conformant" models, which, specifically, minimize the number of modeled enactments that are extraneous to the process semantics. Therefore, a novel process mining framework is introduced and some relevant computational issues are deeply studied. As finding an exact solution to such an enhanced process mining problem is proven to require high computational costs, in most practical cases, a greedy approach is devised. This is founded on an iterative, hierarchical, refinement of the process model, where, at each step, traces sharing similar behavior patterns are clustered together and equipped with a specialized schema. The algorithm guarantees that each refinement leads to an increasingly sound mDdel, thus attaining a monotonic search. Experimental results evidence the validity of the approach with respect to both effectiveness and scalability.
Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and, typically, there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, we have developed techniques for discovering workflow models. The starting point for such techniques is a so-called "workflow log" containing information about the workflow process as it is actually being executed. We present a new algorithm to extract a process model from such a log and represent it in terms of a Petri net. However, we also demonstrate that it is not possible to discover arbitrary workflow processes. We explore a class of workflow processes that can be discovered. We show that the α-algorithm can successfully mine any workflow represented by a so-called SWF-net.
This paper reports on the development of social network analysis, tracing its origins in classical sociology and its more recent formulation in social scientific and mathematical work. It is argued that the concept of social network provides a powerful model for social structure, and that a number of important formal methods of social network analysis can be discerned. Social network analysis has been used in studies of kinship structure, social mobility, science citations, contacts among members of deviant groups, corporate power, international trade exploitation, class structure, and many other areas. A review of the formal models proposed in graph theory, multidimensional scaling, and algebraic topology is followed by extended illustrations of social network analysis in the study of community structure and interlocking directorships.
Most information systems log events (e.g., transaction logs, audit trails) to audit and monitor the processes they support.
At the same time, many of these processes have been explicitly modeled. For example, SAP R/3 logs events in transaction logs
and there are EPCs (Event-driven Process Chains) describing the so-called reference models. These reference models describe
how the system should be used. The coexistence of event logs and process models raises an interesting question: “Does the
event log conform to the process model and vice versa?”. This paper demonstrates that there is not a simple answer to this question. To tackle
the problem, we distinguish two dimensions of conformance: fitness (the event log may be the result of the process modeled) and appropriateness (the model is a likely candidate from a structural and behavioral point of view). Different metrics have been defined and
a Conformance Checker has been implemented within the ProM Framework.
The design of workflows is a complicated task. In those cases where the control flow between activities cannot be modeled in advance but simply occurs during enactment time (run time), we speak of ad-hoc processes. Ad-hoc processes allow for the flexibility needed in real-life business processes. Since ad-hoc processes are highly dynamic, they represent one of the most difficult challenges, both, technically and conceptually. Caramba is one of the few process-aware collaboration systems allowing for ad-hoc processes. Unlike in classical workflow systems, the users are no longer restricted by the system. Therefore, it is interesting to study the actual way people and organizations work. In this paper, we propose process mining techniques and tools to analyze ad-hoc processes. We introduce process mining, discuss the concept of mining in the context of ad-hoc processes, and demonstrate a concrete application of the concept using Caramba, process mining tools such as EMiT and MinSoN, and a newly developed extraction tool named Teamlog.
A thorough understanding of the way in which existing business processes currently practice is essential from the perspectives of both process reengineering and workflow management. In this paper, we present a framework and algorithms that derive the underlying process model from past executions. The process model employs a directed graph for representing the control dependencies among activities and associates a Boolean function on each edge to indicate the condition under which the edge is to be enabled. By modeling the execution of an activity as an interval, we have developed an algorithm that derives the directed graph in a faster, more accurate manner. This algorithm is further enhanced with a noise handling mechanism to tolerate noise, which frequently occur in the real world. Experimental results show that the proposed algorithm outperforms the existing ones in terms of efficiency and quality.
Understanding a process is a basic requirement for improv- ing or reengineering it. Process mining provides methods and techniques for capturing the process behaviour from history data of past executions. Promising control flow mining approaches have been already published. However, organisational aspects have been neglected so far. Especially the link between the process and the organisational structures is less un- derstood. Since most companies have an organisational model it can be used in combination with the audit trail data in order to derive mean- ingful sta assignment rules enabling a better understanding of the un- derlying process. Furthermore, knowledge about sta assignments can be used as input for further analysis (e.g. workload balance of actors or delta-analysis). In this paper, we introduce the problem of mining sta assignment rules and show that it can be interpreted as an inductive learning problem, particularly using a decision tree learning approach.
This chapter describes the business process cockpit (BPC), a tool that supports real-time monitoring, analysis, and management of business processes running on top of Hewlett-Packard Process Manager (HPPM), the Business Process Management Systems (BPMS) developed by Hewlett-Packard (HP). Business process management systems (BPMSs) are software platforms that support the definition, execution, and tracking of business processes. They are often used to support administrative and production processes as well as to implement complex web services, delivered by composing existing ones according to some process logic. BPMSs have the ability of logging information about the business processes they support, including for instance the start and completion time of each activity, its input and output data, the resource that executed it, as well as events (messages) sent or received.
Part I. Introduction: Networks, Relations, and Structure: 1. Relations and networks in the social and behavioral sciences 2. Social network data: collection and application Part II. Mathematical Representations of Social Networks: 3. Notation 4. Graphs and matrixes Part III. Structural and Locational Properties: 5. Centrality, prestige, and related actor and group measures 6. Structural balance, clusterability, and transitivity 7. Cohesive subgroups 8. Affiliations, co-memberships, and overlapping subgroups Part IV. Roles and Positions: 9. Structural equivalence 10. Blockmodels 11. Relational algebras 12. Network positions and roles Part V. Dyadic and Triadic Methods: 13. Dyads 14. Triads Part VI. Statistical Dyadic Interaction Models: 15. Statistical analysis of single relational networks 16. Stochastic blockmodels and goodness-of-fit indices Part VII. Epilogue: 17. Future directions.
In today's rapidly changing markets, organizations repeatedly change their structures. Existing workflow management systems reflect these structures in a separate organizational model and a process model (workflow model). However, these systems cannot cope with repetitive and frequent structural change of organizational and process structure and thus hamper continuous operation and quick reactions to turbulent markets. Change in the structure of the organizational model can cause orphaned references in the process model which leads to inconsistent workflow specifications. We describe the problem domain of structural change and classify it by eight categories. A powerful organizational meta model is introduced that allows to describe meta information about organizational structures. With this and with an underlying ontology, it enables workflow management systems to efficiently adapt to change. Finally, we show exemplarily how our approach is able to deal with the identified categories of structural change.
Saying that networks are important is stating the obvious. But harnessing the power of these seemingly invisible groups to achieve organizational goals is an elusive undertaking. Most efforts to promote collaboration are haphazard and built on the implicit philosophy that more connectivity is better. In truth, networks create relational demands that sap people's time and energy and can bog down entire organizations. It's crucial for executives to learn how to promote connectivity only where it benefits an organization or individual and to decrease unnecessary connections. In this article, the authors introduce three types of social networks, each of which delivers unique value. The customized response network excels at framing the ambiguous problems involved in innovation. Strategy consulting firms and new-product development groups rely on this format. By contrast, surgical teams and law firms rely mostly on the modular response network, which works best when components of the problem are known but the sequence of those components in the solution is unknown. And the routine response network is best suited for organizations like call centers, where the problems and solutions are fairly predictable but collaboration is still needed. Executives shouldn't simply hope that collaboration will spontaneously occur in the right places atthe right times in their organization. They need to develop a strategic, nuanced view of collaboration, and they must take steps to ensure that their companies support the types of social networks that best fit their goals. Drawing on examples from Novartis, the FAA, and Sallie Mae, the authors offer managers the tools they need to determine which network will deliver the best results for their organizations and which strategic investments will nurture the right degree of connectivity.
Since the 1970s, computer systems have featured multiple applications and served multiple users, leading to heightened awareness of data security issues. System administrators and software developers focused on different kinds of access control to ensure that only authorized users were given access to certain data or resources. One kind of access control that emerged is role-based access control (RBAC). A role is chiefly a semantic construct forming the basis of access control policy. With RBAC, system administrators create roles according to the job functions performed in a company or organization, grant permissions (access authorization) to those roles, and then assign users to the roles on the basis of their specific job responsibilities and qualifications. A role can represent specific task competency, such as that of a physician or a pharmacist. Or it can embody the authority and responsibility of, say, a project supervisor. Roles define both the specific individuals allowed to access resources and the extent to which resources are accessed. For example, an operator role might access all computer resources but not change access permissions; a security officer role might change permissions but have no access to resources; and an auditor role might access only audit trails. Roles are used for system administration in such network operating systems as Novell's NetWare and Microsoft's Windows NT. This article explains why RBAC is receiving renewed attention as a method of security administration and review, describes a framework of four reference models the authors have developed to better understand RBAC and categorize different implementations, and discusses the use of RBAC to manage itself. The authors' framework separates the administration of RBAC from its access control functions.
Modern enterprises increasingly use the workflow paradigm to prescribe how business processes should be performed. Processes are typically modeled as annotated activity graphs. We present an approach for a system that constructs process models from logs of past, unstructured executions of the given process. The graph so produced conforms to the dependencies and past executions present in the log. By providing models that capture the previous executions of the process, this technique allows easier introduction of a workflow system and evaluation and evolution of existing process models. We also present results from applying the algorithm to synthetic data sets as well as process logs obtained from an IBM Flowmark installation. 1 Introduction Organizations typically prescribe how business processes have to be performed, particularly when activities are complex and involve many people. A business process specifies the way in which the resources of an enterprise are used. The per...
Many software process methods and tools presuppose the existence of a formal model of a process. Unfortunately, developing a formal model for an on-going, complex process can be difficult, costly, and error prone. This presents a practical barrier to the adoption of process technologies, which would be lowered by automated assistance in creating formal models. To this end, we have developed a data analysis technique that we term process discovery. Under this technique, data describing process events are first captured from an on-going process and then used to generate a formal model of the behavior of that process. In this article we describe a Markov method that we developed specifically for process discovery, as well as describe two additional methods that we adopted from other domains and augmented for our purposes. The three methods range from the purely algorithmic to the purely statistical. We compare the methods and discuss their application in an industrial case study.
This paper describes three methods for process discovery that we have developed, implemented, and applied in an industrial case study. These methods span the range from purely algorithmic, to algorithmic and statistical, to purely statistical (neural net). We show that not only is process discovery possible, it is practical and effective in real-world situations. c fl 1996 Jonathan E. Cook and Alexander L. Wolf This work was supported in part by the National Science Foundation under grant CCR-93-02739 and the Air Force Material Command, Rome Laboratory, and the Advanced Research Projects Agency under Contract Number F3060294 -C-0253. The content of the information does not necessarily reflect the position or the policy of the Government and no official endorsement should be inferred. 1 Introduction
A social network view of organizational learning: relational and structural dimensions of ‘know who’
- Borgatti
S. Borgatti, R. Cross, A social network view of organizational learning: relational and structural dimensions of 'know who', Management Science 49 (2003) 432–445.
Lectures on Petri Nets I: Basic Models, volume 1491 of Lecture Notes in Computer Science
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