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Abstract

Information systems have been widely adopted to support service processes in various domains, e.g., in the telecommunication, finance, and health sectors. Information recorded by systems during the operation of these processes provide an angle for operational process analysis, commonly referred to as process mining. In this work, we establish a queueing perspective in process mining to address the online delay prediction problem, which refers to the time that the execution of an activity for a running instance of a service process is delayed due to queueing effects. We present predictors that treat queues as first-class citizens and either enhance existing regression-based techniques for process mining or are directly grounded in queueing theory. In particular, our predictors target multi-class service processes, in which requests are classified by a type that influences their processing. Further, we introduce queue mining techniques that derive the predictors from event logs recorded by an information system during process execution. Our evaluation based on large real-world datasets, from the telecommunications and financial sectors, shows that our techniques yield accurate online predictions of case delay and drastically improve over predictors neglecting the queueing perspective.

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... However, integrating the set of possible predictors with queueing perspective variables (e.g. patient queues, etc.) may result in better prediction models, according to queueing theory and queue mining literature (Ang et al., 2015;Senderovich et al., 2015;Thiongane et al., 2016). Yet, queue-based information inside the process is often unavailable or difficult to extract from the event log ( Van der Aalst et al., 2011a;Senderovich et al., 2015). ...
... patient queues, etc.) may result in better prediction models, according to queueing theory and queue mining literature (Ang et al., 2015;Senderovich et al., 2015;Thiongane et al., 2016). Yet, queue-based information inside the process is often unavailable or difficult to extract from the event log ( Van der Aalst et al., 2011a;Senderovich et al., 2015). Process mining seems a valuable solution to overcome such limitations (Van der Aalst, 2011b, 2016Senderovich et al., 2015;Senderovich et al.,2016). ...
... Yet, queue-based information inside the process is often unavailable or difficult to extract from the event log ( Van der Aalst et al., 2011a;Senderovich et al., 2015). Process mining seems a valuable solution to overcome such limitations (Van der Aalst, 2011b, 2016Senderovich et al., 2015;Senderovich et al.,2016). By undertaking process mining, it is possible to gain insights into the hospital processes, extracting the actual patient-flow and the crowding level of the activities within the ED. ...
Article
Purpose The purpose of this study is twofold: exploring new queue-based variables enabled by process mining and evaluating their impact on the accuracy of waiting time prediction. Such queue-based predictors that capture the current state of the emergency department (ED) may lead to a significant improvement in the accuracy of the prediction models. Design/methodology/approach Alongside the traditional variables influencing ED waiting time, the authors developed new queue-based predictors exploiting process mining. Process mining techniques allowed the authors to discover the actual patient-flow and derive information about the crowding level of the activities. The proposed predictors were evaluated using linear and nonlinear learning techniques. The authors used real data from an ED. Findings As expected, the main results show that integrating the set of predictors with queue-based variables significantly improves the accuracy of waiting time prediction. Specifically, mean square error values were reduced by about 22 and 23 per cent by applying linear and nonlinear learning techniques, respectively. Practical implications Accurate estimates of waiting time can enable the ED systems to prevent overcrowding e.g. improving the routing of patients in EDs and managing more efficiently the resources. Providing accurate waiting time information also can lead to decreased patients’ dissatisfaction and elopement. Originality/value The novelty of the study relies on the attempt to derive queue-based variables reporting the crowding level of the activities within the ED through process mining techniques. Such information is often unavailable or particularly difficult to extract automatically, due to the characteristics of ED processes.
... The study was reviewed and approved by the Institutional Review Board and conducted in accordance with the Declaration of Helsinki. Phase I included assessing patient needs, using process mining [31,32] to dynamically create patient information, designing an initial user interface (UI), conducting laboratory evaluations of this design, and redesigning the UI. Phase II comprised deploying and testing the system, identifying barriers to adoption, and refining the design accordingly. ...
... We used process mining [31,32] tools (ie, process discovery and queue mining) to mine patient-related information stored in the medical databases of the hospital ED. We accessed all available information of patients in the ED for 39 months (2014-2017). ...
... To address the first challenge, we modeled ED patient journeys and presented them to patients, using an innovative, unique combination of operations research tools (ie, process discovery and queue mining [31,32]) and user-centered design methods. The interdisciplinary effort enabled translating medical and process-related information in ED medical records into real-time information regarding personal procedures. ...
Article
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Background: Medical care is highly complex in that it addresses patient-centered health goals that require the coordination of multiple care providers. Emergency department (ED) patients currently lack a sense of predictability about ED procedures. This increases frustration and aggression. Herein, we describe a system for providing real-time information to ED patients regarding the procedures in their ED medical journey. Objective: This study aimed to develop a system that provides patients with dynamically updated information about the specific procedures and expected waiting times in their personal ED journey, and to report initial evaluations of this system. Methods: To develop the myED system, we extracted information from hospital databases and translated it using process mining and user interface design into a language that is accessible and comprehensible to patients. We evaluated the system using a mixed methods approach that combined observations, interviews, and online records. Results: Interviews with patients, accompanying family members, and health care providers (HCPs) confirmed patients' needs for information about their personal ED journey. The system developed enables patients to access this information on their personal mobile phones through a responsive website. In the third month after deployment, 492 of 1614 (30.48%) patients used myED. Patients' understanding of their ED journey improved significantly (F8,299=2.519; P=.01), and patients showed positive reactions to the system. We identified future challenges, including achieving quick engagement without delaying medical care. Salient reasons for poor system adoption were patients' medical state and technological illiteracy. HCPs confirmed the potential of myED and identified means that could improve patient experience and staff cooperation. Conclusions: Our iterative work with ED patients, HCPs, and a multidisciplinary team of developers yielded a system that provides personal information to patients about their ED journey in a secure, effective, and user-friendly way. MyED communicates this information through mobile technology. This improves health care by addressing patients' psychological needs for information and understanding, which are often overlooked. We continue to test and refine the system and expect to find positive effects of myED on patients' ED experience and hospital operations.
... An effective approach to solve a time prediction problem is to formulate it as a supervised learning task, where future time points are predicted based on raw event data (Senderovich et al. 2015). This data is commonly available in the form of event logs, recordings of the behavior of a system, which contain temporal information. ...
... Fourth, we use the snapshot predictor, which predicts time-to-physician and length-of-stay, respectively, based on the wait time of the most recent patient that finished waiting. This result is considered the state of the art in delay prediction for single-station queues (Senderovich et al. 2015). ...
... The authors assume full knowledge of the patient flow process and use this knowledge to manually define queueing features (e.g., the number of patients waiting for a physician) that are inserted into a Lasso regression model for feature selection. Similarly, Senderovich et al. (2015) proposed a single-station queueing model that is heavily based on process knowledge to generate predictive features. In our work, we do not assume a-priori knowledge of the process and the events that we observe in the event log. ...
Article
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Time prediction is an essential component of decision making in various Artificial Intelligence application areas, including transportation systems, healthcare, and manufacturing. Predictions are required for efficient resource allocation and scheduling, optimized routing, and temporal action planning. In this work, we focus on time prediction in congested systems, where entities share scarce resources. To achieve accurate and explainable time prediction in this setting, features describing system congestion (e.g., workload and resource availability), must be considered. These features are typically gathered using process knowledge, (i.e., insights on the interplay of a system’s entities). Such knowledge is expensive to gather and may be completely unavailable. In order to automatically extract such features from data without prior process knowledge, we propose the model of congestion graphs, which are grounded in queueing theory. We show how congestion graphs are mined from raw event data using queueing theory based assumptions on the information contained in these logs. We evaluate our approach on two real-world datasets from healthcare systems where scarce resources prevail: an emergency department and an outpatient cancer clinic. Our experimental results show that using automatic generation of congestion features, we get an up to 23% improvement in terms of relative error in time prediction, compared to common baseline methods. We also detail how congestion graphs can be used to explain delays in the system.
... Results on a real-life case study show that combining these techniques improves the prediction accuracy. In [44] and [45], Senderovich et al. apply queuing theory to predict possible delays in business process executions. The proposed approaches refine traditional techniques based on transition systems to take into account queueing effects. ...
... Also queuing models can be used for prediction because if a process follows a queuing context and queuing measures (e.g., arrival rate, departure rate) can be accurately estimated and fit the process actual execution, the movement of a queuing item can be reliably predicted. Queueing theory and regression-based techniques are combined for delay prediction in [44,45]. ...
... While some machine learning-based predictive process monitoring approaches train a single predictor on the whole event log, others employ a multiple predictor approach by dividing the prefix traces in the historical log into several buckets and fitting a separate predictor for each bucket. [19] Folino et al. [20,21] Rogge-Solti and Weske [42] Rogge-Solti and Weske [41] Senderovich et al. [45] Senderovich et al. [44] Cesario et al. [8] Bevacqua et al. [3] de Leoni et al. [12] Pika et al. [37,38], de Leoni et al. [11] Polato et al. [40] van der Spoel et al. [58], Polato et al. [39], Ceci et al. [7] Senderovich et al. [43] van der Spoel et al. [58] Tax et al. [50] Metzger et al. [30] Navarin et al. [33] Verenich et al. [60], Metzger et al. [30] To this end, Teinemaa et al. [51] surveyed several bucketing methods out of which three have been utilized in the primary methods: ...
Article
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Predictive business process monitoring methods exploit historical process execution logs to generate predictions about running instances (called cases) of a business process, such as the prediction of the outcome, next activity, or remaining cycle time of a given process case. These insights could be used to support operational managers in taking remedial actions as business processes unfold, e.g., shifting resources from one case onto another to ensure the latter is completed on time. A number of methods to tackle the remaining cycle time prediction problem have been proposed in the literature. However, due to differences in their experimental setup, choice of datasets, evaluation measures, and baselines, the relative merits of each method remain unclear. This article presents a systematic literature review and taxonomy of methods for remaining time prediction in the context of business processes, as well as a cross-benchmark comparison of 16 such methods based on 17 real-life datasets originating from different industry domains.
... Accurate and robust estimation of performance measures, such as cycle times and queuelengths, is crucial for locating bottlenecks and understanding system dynamics [2]. In heavily loaded processes where cases compete over scarce resources, the use of queue mining, which is the discovery of the queueing perspective in process mining [3], plays a key role in performance analytics [4]. ...
... Specifically, M/M/1 queues satisfy the following three assumptions: (1) arrival is according to a Poisson process, (2) service times are exponentially distributed with some mean 1 µ and are independent of each other and of inter-arrival times, and (3) the dispatching policy is First-Come-First-Served. These assumptions lead to the following best predictor [4]: ...
... The MM1 dataset presents a sterile setting, for which using φ M should suffice to achieve the best prediction for q(t) [4]. While this holds for MAE and MARE, we see that using Φ does provide a slight improvement in RMSE. ...
... Many studies have been conducted in order to deal with various prediction tasks such as predicting the remaining processing time [52][53][54]63,69], predicting the outcomes of a process [18,35,50,72], and predicting future events [19,24,63] (cf. [11,15,41,42,49,58]). An overview of various works in the area of predictive business process monitoring can be found in [20,36]. ...
... The work by [54,55] proposes a technique for predicting the remaining processing time using stochastic Petri nets. The works by [41,49,58,59] focus on predicting delays in process execution. In [58,59], the authors use queueing theory to address the problem of delay prediction, while [41] explores the delay prediction in the domain of transport and logistics process. ...
... The works by [41,49,58,59] focus on predicting delays in process execution. In [58,59], the authors use queueing theory to address the problem of delay prediction, while [41] explores the delay prediction in the domain of transport and logistics process. In [25], the authors present an ad hoc predictive clustering approach for predicting process performance. ...
Article
Full-text available
Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of predictions. Hence, it is important to have a means for properly specifying the desired prediction tasks, and a mechanism to deal with these various prediction tasks. Although there have been many studies in this area, they mostly focus on a specific prediction task. This work introduces a language for specifying the desired prediction tasks, and this language allows us to express various kinds of prediction tasks. This work also presents a mechanism for automatically creating the corresponding prediction model based on the given specification. Differently from previous studies, instead of focusing on a particular prediction task, we present an approach to deal with various prediction tasks based on the given specification of the desired prediction tasks. We also provide an implementation of the approach which is used to conduct experiments using real-life event logs.
... This observation is explained by time-dependent levels of congestion that influence sojourn times. While this effect becomes smaller when averaging over long time periods, for a single estimation task, such as predicting the sojourn time for a specific case, knowing the congestion level can dramatically improve prediction [21,22]. ...
... Congestion mining has received notable attention in recent business process management literature. Queue mining, which is a set of process mining methods for eliciting the load and congestion from execution data was first introduced in [7] for single-typed cases, and extended in [21] to business processes with several types of cases. Furthermore, in a work by [33], the impact of resources on delays was investigated. ...
Article
Analysing performance of business processes is an important vehicle to improve their operation. Specifically, an accurate assessment of sojourn times and remaining times enables bottleneck analysis and resource planning. Recently, methods to create respective performance models from event logs have been proposed. These works have several limitations, though: They either consider control-flow and performance information separately, or rely on an ad-hoc selection of temporal relations between events. In this paper, we introduce the Temporal Network Representation (TNR) of a log. It is based on Allen's interval algebra, comprises the pairwise temporal relations for activity executions, and potentially incorporates the context in which these relations have been observed. We demonstrate the usefulness of the TNR for detecting (unrecorded) delays and for probabilistic mining of variants when modelling the performance of a process. In order to compare different models from the performance perspective, we further develop a framework for measuring performance fitness. Under this framework, TNR-based process discovery is guaranteed to dominate existing techniques in measuring performance characteristics of a process. In addition, we show how contextual information in terms of the congestion levels of the process can be mined in order to further improve capabilities for performance analysis. To illustrate the practical value of the proposed models, we evaluate our approaches with three real-life datasets. Our experiments show that the TNR yields an improvement in performance fitness over state-of-the-art algorithms, while congestion learning is able to accurately reconstruct congestion levels from event data.
... One ISC implementation option on top of a Rete rule engine is provided in [33]. ISC are and can be used in various applications including instance batching [34,35], instance queuing [36,37], and security [32]. [11] aim at predicting inter-case features in process monitoring which can be a first step towards prediction on ISC compliance. ...
... In the next step, function filter identifies false positives that happened by chance by returning event labels whose relative occurrences are above a threshold γ 1 ∈ [0, 1] (lines [23][24][25][26][27]. Choosing, e.g., γ 1 = 0.9 means that at least 90% of all observed events must have occurred simultaneously. Based on set filteredEventLabels only events with these labels are retained in the final ISC candidate list (simActivities, lines [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43] stating that A and A ′ must be executed simultaneously. Regarding ISC P , it is likely that more than two different events occur simultaneously (imagine a third process in the running example with another event A ′′ ). ...
Article
Full-text available
Instance spanning constraints (ISC) are the instrument to establish controls across multiple instances of one or several processes. A multitude of applications crave for ISC support. Consider, for example, the bundling and unbundling of cargo across several instances of a logistics process or dependencies between examinations in different medical treatment processes. Non-compliance with ISC can lead to severe consequences and penalties, e.g., dangerous effects due to undesired drug interactions. ISC might stem from regulatory documents, extracted by domain experts. Another source for ISC are process execution logs. Process execution logs store execution information for process instances, and hence, inherently, the effects of ISC. Discovering ISC from process execution logs can support ISC design and implementation (if the ISC was not known beforehand) and the validation of the ISC during its life time. This work contributes a categorization of ISC as well as four discovery algorithms for ISC candidates from process execution logs. The discovered ISC candidates are put into context of the associated processes and can be further validated with domain experts. The algorithms are prototypically implemented and evaluated based on artificial and real-world process execution logs. The results facilitate ISC design as well as validation and hence contribute to a digitalized ISC and compliance management.
... Ordinal classification problems are often treated as multi-class classification problems, in which the target class exhibits some form of ordinal ordering. These problems commonly address realworld applications for which expert-systems and machine-learning algorithm were developed, such as automatic classification of severity of diseases ( Nabi et al., 2019 ), portfolio investment by expected returns ( Altuntas and Dereli, 2015 ) or performance prediction of queueing systems ( Senderovich et al., 2015 ). In these problems, it is important to take into account the value deviation among the different classes, since the magnitude of potential classification error could results in critical consequences ( Gaudette et al., 2009 ), such as the detection of the level of congestive heart failure ( Masetic and Subasi, 2016 ) or prediction of load level in emergency services ( Senderovich et al., 2015 ;Mouroo et al., 2017 ;Sanit-in and Saikaew, 2019 ). ...
... These problems commonly address realworld applications for which expert-systems and machine-learning algorithm were developed, such as automatic classification of severity of diseases ( Nabi et al., 2019 ), portfolio investment by expected returns ( Altuntas and Dereli, 2015 ) or performance prediction of queueing systems ( Senderovich et al., 2015 ). In these problems, it is important to take into account the value deviation among the different classes, since the magnitude of potential classification error could results in critical consequences ( Gaudette et al., 2009 ), such as the detection of the level of congestive heart failure ( Masetic and Subasi, 2016 ) or prediction of load level in emergency services ( Senderovich et al., 2015 ;Mouroo et al., 2017 ;Sanit-in and Saikaew, 2019 ). Most of the ordinal classification methods in the literature assume monotonic behavior, according to which the class values follow a monotonicity constraint with respect to the classifying attributes ( Marsala and Petturiti, 2013 ;Ben-David, 1995 ;Ben-David et al., 1989 ;Zhu et al., 2017 ;Verbeke et al. , 2017 ). ...
... To distinguish between sequential batching and regular queue handling, Martin et al. [6] assume that all cases need to be present in the queue before the resource starts processing a batch. When the arrival of a case at a task is recorded, as is the case in a Q-log in the queue mining field [28], this information can be immediately retrieved from the data. ...
... The arrival time differs from the start time of a task instance when queues are formed because of limited resources to perform a particular task. In a Q-log, which is an event log containing queue-related events such as queue arrival events [28], arrival times are explicitly recorded in the event log. Hence, they can just be included when the event log is converted to a task log and no further efforts are required. ...
Article
Organizations carry out a variety of business processes in order to serve their clients. Usually supported by information technology and systems, process execution data is logged in an event log. Process mining uses this event log to discover the process' control-flow, its performance, information about the resources, etc. A common assumption is that the cases are executed independently of each other. However, batch work-the collective execution of cases for specific activities-is a common phenomenon in operational processes to save costs or time. Existing research has mainly focused on discovering individual batch tasks. However, beyond this narrow setting, batch processing may consist of the execution of several linked tasks. In this work, we present a novel algorithm which can also detect parallel, sequential and concurrent batching over several connected tasks, i.e., subprocesses. The proposed algorithm is evaluated on synthetic logs generated by a business process simulator, as well as on a real-world log obtained from a hospital's digital whiteboard system. The evaluation shows that batch processing at the subprocess level can be reliably detected.
... (F) [114], [123], [113], [115], [49], [14], [130], [128], [50] Control, Design, and Uncertainty Quantification: ...
... In [113] the work was extended to handle the queue mining paradigm in view of partial information. In [115] customers with different priorities were incorporated as part of the queue mining process. Further, in [116] a resource-driven perspective was employed with an application to an outpatient clinic. ...
Preprint
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We present a broad literature survey of parameter and state estimation for queueing systems. Our approach is based on various inference activities, queueing models, observations schemes, and statistical methods. We categorize these into branches of research that we call estimation paradigms. These include: the classical sampling approach, inverse problems, inference for non-interacting systems, inference with discrete sampling, inference with queueing fundamentals, queue inference engine problems, Bayesian approaches, online prediction, implicit models, and control, design, and uncertainty quantification. For each of these estimation paradigms, we outline the principles and ideas, while surveying key references. We also present various simple numerical experiments. In addition to some key references mentioned here, a periodically-updated comprehensive list of references dealing with parameter and state estimation of queues will be kept in an accompanying annotated bibliography.
... However, when observing a queue, n a and n s are often not fixed and can be dependent on the sequences {A i } and {S i }. Complications may arise, not only due [14,49,50,[113][114][115][116]123,128,130] Control, Design, and Uncertainty Quantification: This paradigm deals with work related to parameter and state estimation where control and design decisions based on inferred values are to be made [8,10,17,48,74,87] to censoring, but also due to the dependency structure of the various quantities. Here are some possibilities: ...
... In [113], the work was extended to handle the queue mining paradigm in view of partial information. In [114], customers with different priorities were incorporated as part of the queue mining process. Further, in [116] a resource-driven perspective was employed with an application to an outpatient clinic. ...
Article
Full-text available
We present a broad literature survey of parameter and state estimation for queueing systems. Our approach is based on various inference activities, queueing models, observations schemes, and statistical methods. We categorize these into branches of research that we call estimation paradigms. These include: the classical sampling approach, inverse problems, inference for non-interacting systems, inference with discrete sampling, inference with queueing fundamentals, queue inference engine problems, Bayesian approaches, online prediction, implicit models, and control, design, and uncertainty quantification. For each of these estimation paradigms, we outline the principles and ideas, while surveying key references. We also present various simple numerical experiments. In addition to some key references mentioned here, a periodically updated comprehensive list of references dealing with parameter and state estimation of queues will be kept in an accompanying annotated bibliography.
... Such use case aims at predicting the outcome of active cases, i.e., cases that are uncompleted and, therefore, still ongoing [24]. Learning from an event log of historical cases, predictive monitoring techniques are able to predict the remaining time of an ongoing case [73], delays [65], next activities [66], waiting times [7], outcomes [68], risks [14], costs [72], or performance indicators [17]. Since hyperparameter configuration in predictive process monitoring is crucial and often difficult for users, some works provide methods to support hyperparameter optimization in predictive process monitoring [23]. ...
... [58] What are the predicted delays of an ongoing case? [65] What are the next activity/activities of an ongoing case? [66] What is are the predicted waiting times of an ongoing case? ...
Chapter
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In the last years, process mining has significantly matured and has increasingly been applied by companies in industrial contexts. However, with the growing number of process mining methods, practitioners might find it difficult to identify which ones to apply in specific contexts and to understand the specific business value of each process mining technique. This paper’s main objective is to develop a business-oriented framework capturing the main process mining use cases and the business-oriented questions they can answer. We conducted a Systematic Literature Review (SLR) and we used the review and the extracted data to develop a framework that (1) classifies existing process mining use cases connecting them to specific methods implementing them, and (2) identifies business-oriented questions that process mining use cases can answer. Practitioners can use the framework to navigate through the available process mining use cases and to identify the process mining methods suitable for their needs.
... Some information systems, such as telecommunication [10] and computing networks [11,12], as well as trunked mobile radio systems and air traffic [13][14][15] are also amenable to the channel description. ...
... where θ(t) is the Heaviside function. The stationary open probability, p o (λ), and flux, j(λ), can be obtained from Eqs. (10) and (11) ...
Preprint
We model a particulate flow of constant velocity through confined geometries, ranging from a single channel to a bundle of $N_c$ identical coupled channels, under conditions of reversible blockage. Quantities of interest include the exiting particle flux (or throughput) and the probability that the bundle is open. For a constant entering flux, the bundle evolves through a transient regime to a steady state. We present analytic solutions for the stationary properties of a single channel with capacity $N\le 3$ and for a bundle of channels each of capacity $N = 1$. For larger values of $N$ and $N_c$, the system's steady state behavior is explored by numerical simulation. Depending on the deblocking time, the exiting flux either increases monotonically with intensity or displays a maximum at a finite intensity. For large $N$ we observe an abrupt change from a state with few blockages to one in which the bundle is permanently blocked and the exiting flux is due entirely to the release of blocked particles. We also compare the relative efficiency of coupled and uncoupled bundles. For $N=1$ the coupled system is always more efficient, but for $N>1$ the behavior is more complex.
... For their method's machine-learning model, they used Random Forest. The Queuing Theory proposed by Senderovich et al. [9] predicts a delay in an ongoing process. As in van der Aalst et al. [3], the authors also used annotated transition systems in this prediction task. ...
... Of all the related work that we show, we focus on the problem of remainingtime prediction. We also focus on the latest research, narrowing down the literature to only three studies: those of Tax et al. [13], Navarin et al. [15], and Senderovich et al. [9]. The method by Tax et al. [13] has several problems, such as the amount of time required for the test process and the lack of consideration of data attributes. ...
Article
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Event logs generated by Process-Aware Information Systems (PAIS) provide many opportunities for analysis that are expected to help organizations optimize their business processes. The ability to monitor business processes proactively can allow an organization to achieve, maintain or enhance competitiveness in the market. Predictive Business Process Monitoring (PBPM) can provide measures such as the prediction of the remaining time of an ongoing process instance (case) by taking past activities in running process instances into account, as based on the event logs of previously completed process instances. With the prediction provided, we expect that organizations can respond quickly to deviations from the desired process. In the context of the growing popularity of deep learning and the need to utilize heterogeneous representation of data; in this study, we derived a new deep-learning approach that utilizes two types of data representation based on a parallel-structure model, which consists of a convolutional neural network (CNN) and a multi-layer perceptron (MLP) with an embedding layer, to predict the remaining time. Conducting experiments with real-world datasets, we compared our proposed method against the existing deep-learning approach to confirm its utility for the provision of more precise prediction (as indicated by error metrics) relative to the baseline method.
... Previous studies focusing on queue length-based delay predictors include Whitt (1999b); Ibrahim & Whitt (2011b) and few other studies discussing delay history-based predictors include Armony et al. (2009) ;Ibrahim & Whitt (2009a, 2011b. For more complex systems, machine learning based delay predictors such as artificial neural networks and decision tree based predictors that are trained on queue log data were proposed in Senderovich et al. (2014Senderovich et al. ( , 2015 and Thiongane et al. (2020). Baldwa et al. (2020) propose a combined simulation and machine learning method for realtime delay prediction for complex queuing systems where adequate queue log data for training a machine learning predictor is not maintained. ...
Preprint
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In this work, we demonstrate how real-time delay prediction can be used to determine patient diversion across a healthcare facility network. Specifically, we consider the case of a primary healthcare facility network within the Indian context. We develop a discrete-event simulation of a network of nine primary healthcare centers, each of which provides outpatient care and limited inpatient and childbirth care. We consider diversion for childbirth patients because we observe that under normal operating conditions, a significant proportion of childbirth patients experience substantial wait times before admission. We also use the simulation to determine the conditions under which inpatients may benefit from diversion. We propose diversion mechanisms based on real-time predictions of delays at the facilities from which the patient is being diverted as well as at the facility to which diversion is planned. This involves generating real-time delay predictions not only at the point in time at which the patient arrives at the facility of origin, but also at the point in time in the near future at which the patient may arrive at the facility considered for diversion. We demonstrate the implementation of these diversion mechanisms using actual delays and delay estimates generated by delay predictors based on system state information such as queue length and elapsed service time. As part of this, we develop a novel approximate real-time delay predictor for the queuing system represented by the childbirth and inpatient care processes, and compare its performance to existing delay predictors for this queuing system. With regard to simulation experiments, we first investigate the conditions under which generation of delay predictions in the near future is relevant. We then simulate our proposed diversion mechanism and show that the proportion of patients waiting longer than a threshold wait time decreases with diversion, and that the extent to which operational outcomes become equitably distributed across the PHC networks depends upon the accuracy of the delay predictor. To our knowledge, our study is the first to investigate the use of real-time delay prediction in implementing diversion mechanisms across networks of healthcare facilities.
... ─ remaining time prediction [24][25][26][27] ─ prediction of activity delays [28] ─ risk prediction [29,30] ─ cost prediction [31] These studies use different machine learning approaches such as decision trees [21], support vector machines [16], Markov models [12], evolutionary algorithms [14] among others. ...
Preprint
The contemporary process-aware information systems possess the capabilities to record the activities generated during the process execution. To leverage these process specific fine-granular data, process mining has recently emerged as a promising research discipline. As an important branch of process mining, predictive business process management, pursues the objective to generate forward-looking, predictive insights to shape business processes. In this study, we propose a conceptual framework sought to establish and promote understanding of decision-making environment, underlying business processes and nature of the user characteristics for developing explainable business process prediction solutions. Consequently, with regard to the theoretical and practical implications of the framework, this study proposes a novel local post-hoc explanation approach for a deep learning classifier that is expected to facilitate the domain experts in justifying the model decisions. In contrary to alternative popular perturbation-based local explanation approaches, this study defines the local regions from the validation dataset by using the intermediate latent space representations learned by the deep neural networks.To validate the applicability of the proposed explanation method, the real-life process log data delivered by the Volvo IT Belgium's incident management system are used.The adopted deep learning classifier achieves a good performance with the Area Under the ROC Curve of 0.94. The generated local explanations are also visualized and presented with relevant evaluation measures that are expected to increase the users' trust in the black-box-model.
... When arrival times are recorded in an event log, as is the case in a Q-log [45], they can just be included in the first conversion step and no further efforts are required. When, to the contrary, arrival times are unknown, they can be imputed using a suitable heuristic. ...
Article
Knowing the availability of human resources for a business process is required, e.g., when allocating resources to work items, or when analyzing the process using a simulation model. In this respect, it should be taken into account that staff members are not permanently available and that they can be involved in multiple processes within the company. Consequently, it is far from trivial to specify their availability for the single process from, e.g., generic timetables. To this end, this paper presents a new method to automatically retrieve resource availability calendars from event logs containing process execution information. The retrieved resource availability calendars are the first to take into account (i) the temporal dimension of availability, i.e. the time of day at which a resource is available, and (ii) intermediate availability interruptions (e.g. due to a break). Empirical evaluation using synthetic data shows that the method’s key outputs closely resemble their equivalents in reality.
... The approaches to predictive monitoring apply a wide number of techniques to perform these predictions. Some of them rely on explicit representations of the process model such as a Transition System [6], [7], a Probabilistic Finite Automaton [8], [9] or a Petri Net [10], [11]. Other approaches do not extract a process model but, instead, extract feature vectors from partial traces to train a machine learning model. ...
Preprint
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Predictive monitoring of business processes is concerned with the prediction of ongoing cases on a business process. Lately, the popularity of deep learning techniques has propitiated an ever-growing set of approaches focused on predictive monitoring based on these techniques. However, the high disparity of process logs and experimental setups used to evaluate these approaches makes it especially difficult to make a fair comparison. Furthermore, it also difficults the selection of the most suitable approach to solve a specific problem. In this paper, we provide both a systematic literature review of approaches that use deep learning to tackle the predictive monitoring tasks. In addition, we performed an exhaustive experimental evaluation of 10 different approaches over 12 publicly available process logs.
... As illustrated in the table, most of the existing works belong to Time-Based Prediction category [6]- [9], [15]- [22]. In this category, prediction of the remaining time is the most popular goal [6]- [8], [16], [18]- [20], [39]. ...
Conference Paper
Predictive business process monitoring is concerned with predicting the process-related Key Performance Indicators (KPIs) and forecasting the future behavior of the process in realtime. Despite the amount of work contributed by researches to this field of research, the performance of existing solutions is not desirable for practical settings. Indeed, these approaches are typically context-unaware and lack generality. However, in real-life use cases, business processes are not isolated from the surrounding working environment, and thus they are influenced by many contextual events, such as events generated by IoT devices. To the best of our knowledge, there is no comprehensive study addressing the integration of contextual events with the process prediction. This paper proposes a holistic context-aware methodology for predictive process monitoring by incorporating IoT data. Moreover, we present a systematic method to integrate the contextual events in the runtime process using Business Process Management System (BPMS) capabilities. We also introduce a predictive model based on Deep Neural Networks (DNN) to forecast the next activity. Finally, we evaluate our solution using a case study in the aviation industry.
... In the domain of performance analysis, there is a range of techniques to extract and analyze the process performance characteristics from event logs. For example, some existing approaches are discriminative process performance (positive vs negative outcomes) [12], animation-based techniques [13] [14], discover collections of queues [15], and event abstraction. Among the approaches, most of them utilize the entire process or activities with respect to the performance measures such as cycle time, processing time, and waiting time including the distribution of performance measures alongside the aggregate statistics [16]. ...
Article
Business process performance mining offers various analysis views of the performance of a process in a given period of time. However, the analysis of the engineering-to-order (ETO) production system is limited. Stage-based process performance mining has been around to monitor and analyze the process in the predefined stages level. In the ETO production system, it requires more flexibility to analyze the stages. Meanwhile, previous work assumed that the cases should follow the sequence of all predefined stages which is not the case in the ETO production system. This study extends the stage-based process performance analysis by relaxing the definition of the stages, that is referred as a relax-stage-based process performance mining. It emphasizes the flexibility to analyze the process through three different stages: mandatory, necessary, and optional. The concept has been tested in a log data of shipbuilding company. The proof-of-concept of relax-stage-based process performance mining is shown by displaying the time-domain and frequency-domain process performance analysis, including the constructed process model.
... Some information systems, such as telecommunication [10] and computing networks [11,12], as well as trunked mobile radio systems and air traffic [13][14][15] are also amenable to the channel description. ...
Article
We model a particulate flow of constant velocity through confined geometries, ranging from a single channel to a bundle of Nc identical coupled channels, under conditions of reversible blockage. Quantities of interest include the exiting particle flux (or throughput) and the probability that the bundle is open. For a constant entering flux, the bundle evolves through a transient regime to a steady state. We present analytic solutions for the stationary properties of a single channel with capacity N≤3 and for a bundle of channels each of capacity N=1. For larger values of N and Nc, the system's steady state behavior is explored by numerical simulation. Depending on the deblocking time, the exiting flux either increases monotonically with intensity or displays a maximum at a finite intensity. For large N we observe an abrupt change from a state with few blockages to one in which the bundle is permanently blocked and the exiting flux is due entirely to the release of blocked particles. We also compare the relative efficiency of coupled and uncoupled bundles. For N=1 the coupled system is always more efficient, but for N>1 the behavior is more complex.
... For enhancement, the extraction of additional information from an event log, we want to highlight the sub fields predictive process monitoring [15], i.e. the construction of models to predict properties of running process instances, and queue mining [19], the analysis of queueing effects in resource driven business processes. For both fields, it is necessary to process events individually to give an online prediction. ...
Conference Paper
Full-text available
Information systems record data while executing business processes. This data can be analyzed, by process mining, to gain knowledge about the business processes underlying the information systems. Data recorded by the information systems is often personal data belonging to individuals such as customers or process workers. Such data has become a strong focus of recent regulations like the GDPR. These new legal developments force organizations that process personal data to ensure a certain level of privacy. Unlike in other fields of data science, in the field of process mining there are no existing solutions to guarantee such privacy. This research aims to provide such solutions that enable organizations to do process mining while giving privacy guarantees to individuals, such as employees, that contribute their data. In this work, we present privacy challenges in the area of process mining and outline privacy guarantees we aim to provide for process mining. We want to follow the design science paradigm to achieve our goals. We describe our preliminary results, an algorithm, called PRETSA, to sanitize event logs for privacy-aware process discovery and show the next steps we want to take in our research .
... Resource prediction (CMF3.1 and CMF3.2) in connection with temporal prediction is mostly seen from a scheduling perspective, i.e., how to determine and avoid potential temporal problems such as bottlenecks by assigning resources [89,94]. Other approaches utilize resources as features for temporal prediction [31]. ...
Preprint
Business process compliance is a key area of business process management and aims at ensuring that processes obey to compliance constraints such as regulatory constraints or business rules imposed on them. Process compliance can be checked during process design time based on verification of process models and at runtime based on monitoring the compliance states of running process instances. For existing compliance monitoring approaches it remains unclear whether and how compliance violations can be predicted, although predictions are crucial in order to prepare and take countermeasures in time. This work, hence, analyzes existing literature from compliance and SLA monitoring as well as predictive process monitoring and provides an updated framework of compliance monitoring functionalities. For each compliance monitoring functionality we elicit prediction requirements and analyze their coverage by existing approaches. Based on this analysis, open challenges and research directions for predictive compliance and process monitoring are elaborated.
... Log-normal distributions were introduced instead due to their eligibility in queueing theory, as mentioned in Refs. [38,39,40]. Discrete inter-arrival and service times were calculated in our simulation as described in Appendix A. ...
Preprint
In a queueing system involving multiple service windows, choice behavior is a significant concern. This paper incorporates the choice of service windows into a queueing model with a floor represented by discrete cells. We contrived a logit-based choice algorithm for agents considering the numbers of agents and the distances to all service windows. Simulations were conducted with various parameters of agent choice preference for these two elements and for different floor configurations, including the floor length and the number of service windows. We investigated the model from the viewpoint of transit times and entrance block rates. The influences of the parameters on these factors were surveyed in detail and we determined that there are optimum floor lengths that minimize the transit times. In addition, we observed that the transit times were determined almost entirely by the entrance block rates. The results of the presented model are relevant to understanding queueing systems including the choice of service windows and can be employed to optimize facility design and floor management.
... Whilst this reallocation may solve the problem of meeting demand in the service event that receives additional capacity, it tends to cause a lack of capacity in the other event, thereby relocating a delay from one service event to another. Although all these research contributions confirm the importance of time in service production and delivery, they do not address the fact that delays in the execution of service events tend to be accumulated and relocated (Senderovich et al. 2015). That is, these research contributions fail to consider the fact that delays in one service event cause delays in another subsequent or parallel service event, which then cause delays in other services, and so on (Butcher and Kayani 2008). ...
Article
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Delays constitute a key challenge in the management of service operations, causing substantial quality and cost issues. Delays in one service event can cause delays in another service event and so on, which creates challenges in the management of complex services. Assuming a lower-triangular matrix formalism, we develop a novel approach to modelling such chains of delays in complex service operations such as health care and software development. This approach can enable service managers to identify, understand, predict and control delays. Our research provides a novel theoretical contribution to the literature on service delays.
... RTM approaches based on queuing models [15] and supervised learning [14] utilized the inter-case dimension in predictions. They create features on the basis of queuing theory like case priority and open cases of similar type. ...
Preprint
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Process mining techniques use event data to describe business processes, where the provided insights are used for predicting processes' future states (Predictive Process Monitoring). Remaining Time Prediction of process instances is an important task in the field of Predictive Process Monitoring (PPM). Existing approaches have two key limitations in developing Remaining Time Prediction Models (RTM): (1) The features used for predictions lack process context, and the created models are black-boxes. (2) The process instances are considered to be in isolation, despite the fact that process states, e.g., the number of running instances, influence the remaining time of a single process instance. Recent approaches improve the quality of RTMs by utilizing process context related to batching-at-end inter-case dynamics in the process, e.g., using the time to batching as a feature. We propose an approach that decreases the previous approaches' reliance on user knowledge for discovering fine-grained process behavior. Furthermore, we enrich our RTMs with the extracted features for multiple performance patterns (caused by inter-case dynamics), which increases the interpretability of models. We assess our proposed remaining time prediction method using two real-world event logs. Incorporating the created inter-case features into RTMs results in more accurate and interpretable predictions.
... ─ remaining time prediction [24][25][26][27] ─ prediction of activity delays [28] ─ risk prediction [29,30] ─ cost prediction [31] These studies use different machine learning approaches such as decision trees [21], support vector machines [16], Markov models [12], evolutionary algorithms [14] among others. ...
Chapter
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The contemporary process-aware information systems possess the capabilities to record the activities generated during the process execution. To leverage these process specific fine-granular data, process mining has recently emerged as a promising research discipline. As an important branch of process mining, predictive business process management, pursues the objective to generate forward-looking, predictive insights to shape business processes. In this study, we propose a conceptual framework sought to establish and promote understanding of decision-making environment, underlying business processes and nature of the user characteristics for developing explainable business process prediction solutions. Consequently, with regard to the theoretical and practical implications of the framework, this study proposes a novel local post-hoc explanation approach for a deep learning classifier that is expected to facilitate the domain experts in justifying the model decisions. In contrary to alternative popular perturbation-based local explanation approaches, this study defines the local regions from the validation dataset by using the intermediate latent space representations learned by the deep neural networks. To validate the applicability of the proposed explanation method, the real-life process log data delivered by the Volvo IT Belgium’s incident management system are used. The adopted deep learning classifier achieves a good performance with the area under the ROC Curve of 0.94. The generated local explanations are also visualized and presented with relevant evaluation measures which are expected to increase the users’ trust in the black-box model.
... Completion time of the next activity can be predicted by training an LSTM neural network [13], or by learning process models with arbitrary probability density functions for time delays through nonparametric regression from event logs [14] that can also be used for learning simulation models to predict performance [15], [16]. Competing for shared resources can be taken into account through simulation models or with queuing models [17]. Using only features of a single case, these models cannot predict PPIs for non-isolated cases. ...
Conference Paper
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Predictive performance analysis is crucial for supporting operational processes. Prediction is challenging when cases are not isolated but influence each other by competing for resources (spaces, machines, operators). The so-called performance spectrum maps a variety of performance-related measures within and across cases over time. We propose a novel prediction approach that uses the performance spectrum for feature selection and extraction to pose machine learning problems used for performance prediction in non-isolated cases. Although the approach is general, we focus on material handling systems as a primary example. We report on a feasibility study conducted for the material handling systems of a major European airport. The results show that the use of the performance spectrum enables much better predictions than baseline approaches.
... Predictive model is a model used to predict and calculate waiting time, delay time and service time for clients. Predictive modeling uses the statistics of queuing method information to predict outcomes [23]. Regularly prediction involves future events, therefore predictive modeling can be applied to many types of unknown event, regardless of when it occurred [15]. ...
Article
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p>COVID-19 is a pandemic crisis that has introduced new norm to the world where we are not encouraged to be in 3C areas, namely crowded place, confined space, and close conservation. We must also ensure that we are at least one meter apart from one another at all time even while queuing. The queuing process can be seen at any organization that offer services. Adhering to the new norm can be a challenge for organization such as banks, hospitals, and government offices when the number of clients waiting in queue increases while in confined space. On the client’s side, they must go through the queue process of obtaining a queue number ticket and then wait to be served in confined and sometimes crowded space every time they require a service. Thequeue process will be repeated at different premise. This study proposes real-time multi-organizationsC19-SmartQ system which use predictive modelling to generate single or consecutive queue number tickets for any client requiring services from two different organizations located within the same building. C19-SmartQsystemmanages queues thus administer social distancing and streamline queues to reduce waiting periods and improve service efficiency. To ensure operability of C19-SmartQ system, itwas tested on the functionality and web server speed performance. The web server speed performance results show that data transfer and web loading were stable since there was only an increase of 0.2 seconds or 0.08% as the number of users per session increases. In the future, the system can be designed to accommodate queuing for more organizations located within the same building. Machine learning can also be integrated in the system to improve the predictive modelling based on current environment at each organization.</p
... [14] use three inter-case properties -the number of parallel events, the number of used resources, and the number of cases. Specifically considering resources as resulting in queues, [20] propose queue mining for predicting delays. The control flow perspective is addressed in combination with the time perspective by [19] and [21]. ...
Chapter
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Predictive monitoring in business processes has gained attention in recent years. It uses a predictive model, learned from event logs, to predict the variables of interest for a running process instance (case). An example of such a variable considered here is the remaining time to complete the running case. Prediction usually relies on the properties of individual cases. Recently, the effects of the case’s environment, particularly cases that are executed in parallel to it, have been incorporated into prediction as inter-case properties. Furthermore, it has been recognized that, when different variants of the process exist, variant information should be considered by the predictive model. However, different prediction approaches use inter-case properties and variant information differently, and there is still no clear and agreed-upon manner in which these are considered for prediction. This paper proposes a conceptual framework that suggests categories of inter-case properties related to cases within a time window. Moreover, the framework considers the possible variant-awareness of these properties and suggests how variant information should be addressed in a predictive model. Reported experimentation supports our proposals.
... The DH predictors can be used for multi-skill systems but they often give large prediction errors for those systems . Senderovich et al. (2015) proposed predictors for a multi-skill call center with multiple call types but a single type (or group) of agents that can handle all call types. Thiongane et al. (2015) proposed data-based delay predictors that can be used for more general multi-skill call centers or service systems. ...
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Predictive business process monitoring methods exploit historical process execution logs to provide predictions about running instances of a process. These predictions enable process workers and managers to preempt performance issues or compliance violations. A number of approaches have been proposed to predict quantitative process performance indicators for running instances of a process, including remaining cycle time, cost, or probability of deadline violation. However, these approaches adopt a black‐box approach, insofar as they predict a single scalar value without decomposing this prediction into more elementary components. In this paper, we propose a white‐box approach to predict performance indicators of running process instances. The key idea is to first predict the performance indicator at the level of activities and then to aggregate these predictions at the level of a process instance by means of flow analysis techniques. The paper develops this idea in the context of predicting the remaining cycle time of ongoing process instances. The proposed approach has been evaluated on real‐life event logs and compared against several baselines. We propose an explainable predictive process monitoring method by extracting a BPMN process model from the event log, predicting a performance indicator at the level of activities, and then aggregating these predictions at the level of the whole process via flow analysis techniques. The paper develops this idea in the context of predicting the remaining execution time of ongoing process instances, by decomposing it into the predicted execution time of each activity that is to be executed.
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In this paper, we consider a general class of a queuing system with multiple job types and flexible service facility. We use a stochastic control policy to determine the performance loss in multi-class M/M/1 queue. The considered system is originally a Markov decision processes (MDP). The author showed how to compute performance bounds for the stochastic control policy of MDP with an average cost criteria. In practice, many authors used heuristic control policies due to some hardness in computing and running mathematically optimal policies. The authors found bounds on performance in order to an optimal policy where the goal of this job is to compute the difference of optimality and a specific policy. In other words, this study shows that, the optimal bounds of the average queue length for any non-idling policies can be found by a factor of service rates.
Chapter
Based on data from real call centers, we develop, test, and compare forecasting methods to predict the waiting time of a call upon its arrival to the center, or more generally of a customer arriving to a service system. We are interested not only in estimating the expected waiting time, but also its probability distribution (or density), conditional on the current state of the system (e.g., the current time, queue sizes, set of agents at work, etc.). We do this in a multiskill setting, with different call types, agents with different sets of skills, and arbitrary rules for matching each call to an agent. Our approach relies on advanced regression and automatic learning techniques such as spline regression, random forests, and artificial neural networks. We also explain how we select the input variables for the predictors.
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Instance Spanning Constraints (ISC) establish controls across multiple instances of one or several business process types. Consider, e.g., medical treatments during which drug-drug interactions might occur. Different treatments are likely to be modeled in separate processes, but yet have to be coordinated in order to avoid harm for patients. ISC typically stem from regulatory documents and must be integrated into business processes. In order to facilitate ISC integration, we provide six ISC patterns which are based on a real-world ISC collection as well as a categorization of ISC. The presented ISC patterns are formalized using Proclets based on timed colored workflow nets. This formalization choice results from an elaborated requirements analysis and enables the synchronization of instances of one or several process types while employing well-known process modeling approaches. The ISC patterns are evaluated through their application to i) selected business processes and ii) existing approaches for batching and security in business processes.
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Business process time prediction aims to predict the time of the running process instance, including the elapsed time and the predicted remaining time. Existing time prediction methods rarely take the cycles of business process into consideration. However, cycles of business process are one of the main factors affecting the accuracy of time prediction. To address this issue, a new transition-driven time prediction for business processes with cycles is proposed. First of all, on the one hand, a Petri net from the event log is mined to obtain the reachability graph, and then the transition division sequence is obtained from the reachability graph of a Petri net. On the other hand, the prefixes are generated by the event log, then, the prefix is feature-encoded and the corresponding business process time is calculated. Second, all prefixes are partitioned according to the activity of the last event of the prefix based on the transition division sequence. Finally, different autoencoders are applied to different transition divisions to reduce dimensionality, and transfer learning is performed by different deep neural networks. Furthermore, by extensive experimental evaluation using the publicly available synthetic event logs and real-life event logs, we show that the proposed method outperforms existing baseline methods.
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Service providers often share delay information, in the form of delay announcements, with their customers. In practice, simple delay announcements, such as average waiting times or a weighted average of previously delayed customers, are often used. Our goal in this paper is to gain insight into when such announcements perform well. Specifically, we compare the accuracies of two announcements: (i) a static announcement that does not exploit real-time information about the state of the system and (ii) a dynamic announcement, specifically the last-to-enter-service (LES) announcement, which equals the delay of the last customer to have entered service at the time of the announcement. We propose a novel correlation-based approach that is theoretically appealing because it allows for a comparison of the accuracies of announcements across different queueing models, including multiclass models with a priority service discipline. It is also practically useful because estimating correlations is much easier than fitting an entire queueing model. Using a combination of queueing-theoretic analysis, real-life data analysis, and simulation, we analyze the performance of static and dynamic announcements and derive an appropriate weighted average of the two which we demonstrate has a superior performance using both simulation and data from a call center. This paper was accepted by Vishal Gaur, operations management.
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Quantitative business process analysis is a powerful approach for analysing timing properties of a business process, such as the expected waiting time of customers or the utilization rate of resources. Multiple techniques are available for quantitative business process analysis, which all have their own advantages and disadvantages. This paper presents a novel technique, based on queueing models, that combines the advantages of existing techniques, in that it leads to accurate analyses, is computationally inexpensive, and feature complete with respect to its support for basic process modelling constructs. An extensive quantitative evaluation has been performed that compares the presented queueing model to existing queueing models from literature. This evaluation shows that the presented model outperforms existing models with one order of magnitude on accuracy. The resulting queueing model can be used for fast and accurate timing predictions of business process models. These properties are useful in optimization scenarios.
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As an important task in business process management, remaining time prediction for business process instances has attracted extensive attentions. However, most of the traditional remaining time prediction approaches only take into account formal process models and cannot handle large-scale event logs in an effective manner. Although machine learning and deep learning have been recently applied to the remaining time prediction task, these approaches cannot incorporate domain knowledge naturally. To overcome these weaknesses of existing studies, we propose a remaining execution time prediction approach based on a novel auto-encoded transition system, which can enhance the complementarity of process modeling and deep learning techniques. Through auto-encoding the event-level and state-level features, the proposed approach can represent process instances in a comprehensive and compact form. Furthermore, a transfer learning strategy is proposed to train the remaining time prediction model so as to avoid overfitting and improve the accuracy of prediction. We conduct extensive experiments on four real-world datasets to verify the effectiveness of the proposed approach. The results show its superiority over several state-of-the-art approaches.
Chapter
Process mining techniques use event data to describe business processes, where the provided insights are used for predicting processes’ future states ( Predictive Process Monitoring ). Remaining Time Prediction of process instances is an important task in the field of Predictive Process Monitoring (PPM). Existing approaches have two key limitations in developing Remaining Time Prediction Models (RTM): (1) The features used for predictions lack process context, and the created models are black-boxes. (2) The process instances are considered to be in isolation, despite the fact that process states, e.g., the number of running instances, influence the remaining time of a single process instance. Recent approaches improve the quality of RTMs by utilizing process context related to batching-at-end inter-case dynamics in the process, e.g., using the time to batching as a feature. We propose an approach that decreases the previous approaches’ reliance on user knowledge for discovering fine-grained process behavior. Furthermore, we enrich our RTMs with the extracted features for multiple performance patterns (caused by inter-case dynamics), which increases the interpretability of models. We assess our proposed remaining time prediction method using two real-world event logs. Incorporating the created inter-case features into RTMs results in more accurate and interpretable predictions.
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Process Mining techniques have been gaining attention, especially as concerns the discovery of predictive process models. Traditionally focused on workflows, they usually assume that process tasks are clearly specified, and referred to in the logs. This limits however their application to many real-life BPM environments (e.g. issue tracking systems) where the traced events do not match any predefined task, but yet keep lots of context data. In order to make the usage of predictive process mining to such logs more effective and easier, we devise a new approach, combining the discovery of different execution scenarios with the automatic abstraction of log events. The approach has been integrated in a prototype system, supporting the discovery, evaluation and reuse of predictive process models. Tests on real-life data show that the approach achieves compelling prediction accuracy w.r.t. state-of-the-art methods, and finds interesting activities’ and process variants’ descriptions.
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Predicting run-time performances is a hot issue in ticket resolution processes. Recent efforts to take account for the sequence of resolution steps, suggest that predictive Process Mining (PM) techniques could be applied in this field, if suitably adapted to the peculiarities of ticket systems. In particular, the performances of a ticket instance usually depend on which kinds of experts worked on it (more than on the mere sequence of resolution tasks), while relevant information about ticket cases is stored in the form of text fields, which are usually disregarded by PM approaches. Instead of relying on a-priori experts groups, we devise an ad-hoc method for clustering experts according to their real working patterns, based on log data. Regarding the discovered groups as abstractions for log events, we also perform a predictive clustering of ticket cases, while using context data as input attributes for splitting the tickets. In this way, different (context-dependent) execution scenarios are recognized for the process, and equipped with more accurate performance predictors. The approach was validated on a real application scenario, where it showed better results than state-of-the-art solutions.
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Credible queueing models of human services acknowledge human characteristics. A prevalent one is the ability of humans to abandon their wait, for example while waiting to be answered by a telephone agent, waiting for a physician’s checkup at an emergency department, or waiting for the completion of an internet transaction. Abandonments can be very costly, to either the service provider (a forgone profit) or the customer (deteriorating health after leaving without being seen by a doctor), and often to both. Practically, models that ignore abandonment can lead to either over- or under-staffing; and in well-balanced systems (e.g., well-managed telephone call centers), the “fittest (needy) who survive” and reach service are rewarded with surprisingly short delays. Theoretically, the phenomenon of abandonment is interesting and challenging, in the context of Queueing Theory and Science as well as beyond (e.g., Psychology). Last, but not least, queueing models with abandonment are more robust and numerically stable, when compared against their abandonment-ignorant analogues. For our relatively narrow purpose here, abandonment of customers, while queueing for service, is the operational manifestation of customer patience, perhaps impatience, or (im)patience for short. This (im)patience is the focus of the present paper. It is characterized via the distribution of the time that a customer is willing to wait, and its dynamics are characterized by the hazard-rate of that distribution. We start with a framework for comprehending impatience, distinguishing the times that a customer expects to wait, is required to wait (offered wait), is willing to wait (patience time), actually waits and felt waiting. We describe statistical methods that are used to infer the (im)patience time and offered wait distributions. Then some useful queueing models, as well as their asymptotic approximations, are discussed. In the main part of the paper, we discuss several “data-based pictures” of impatience. Each “picture” is associated with an important phenomenon. Some theoretical and practical problems that arise from these phenomena, and existing models and methodologies that address these problems, are outlined. The problems discussed cover statistical estimation of impatience, behavior of overloaded systems, dependence between patience and service time, and validation of queueing models. We also illustrate how impatience changes across customers (e.g., VIP vs. regular customers), during waiting (e.g., in response to announcements) and through phases of service (e.g., after experiencing the answering machine over the phone). Our empirical analysis draws data from repositories at the Technion SEELab, and it utilizes SEEStat—its online Exploratory Data Analysis environment. SEEStat and most of our data are internet-accessible, which enables reproducibility of our research.
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An important interface between stochastic models and actual systems comes in estimating values for model parameters using “real world” data. This interface between models and systems is studied for one of the most elementary stochastic systems, the M/M/1 queue. Estimating arrival rates and service rates results in a notable discrepancy between the state distribution for the model (estimated parameters) and the state distribution for the actual system (known parameters). Also, the expected number of customers in the model is infinite regardless of the (unknown) value of the actual traffic intensity. The truth of this assertion is obvious if one allows estimated traffic intensities to equal or exceed one. However, it is shown that the mean for the model is infinite even if the estimated traffic intensity is restricted to be strictly less than one.
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Discovering predictive models for run-time support is an emerging topic in Process Mining research, which can effectively help optimize business process enactments. However, making accurate estimates is not easy especially when considering fine-grain performance measures (e.g., processing times) on a complex and flexible business process, where performance patterns change over time, depending on both case properties and context factors (e.g., seasonality, workload). We try to face such a situation by using an ad-hoc predictive clustering approach, where different context-related execution scenarios are discovered and modeled accurately via distinct state-aware performance predictors. A readable predictive model is obtained eventually, which can make performance forecasts for any new running process case, by using the predictor of the cluster it is estimated to belong to. The approach was implemented in a system prototype, and validated on a real-life context. Test results confirmed the scalability of the approach, and its efficacy in predicting processing times and associated SLA violations.
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We present optimal mean square predictors for queue lengths and delays in the stationary GI/M/m queue, based on a queue length measurement. The development specifies interrelationships among these predictors and numerical examples demonstrate basic properties of the predictors.
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Both empirical evidence and logic suggest that there is a strong negative correlation between waiting time and a customer's evaluation of the quality of a service. The evaluation of service, in turn, is related strongly to customers' loyalty and other important outcomes. A conceptual model, based on field theory, is developed. The model integrates key variables derived from recent studies of consumer waiting behavior. Also incorporated are relevant constructs from the extant services literature, including the roles of the disconfirmation of consumers' wait time expectations, prior service encounters, and the quality of the customer's encounter with the contact employee. Finally, data from actual consumers in a natural queueing context are used to test the theoretical framework. Analysis of the data, with the use of multiple regression, demonstrates a strong pattern of support for the field-theory-based hypotheses, confirming the important role of queue wait management in customer evaluations of service quality. © 1998 John Wiley & Sons, Inc.
Chapter
Our note1 is dedicated to the Palm/Erlang-A Queue. This is the simplest practice- worthy queueing model, that accounts for customers' impatience while waiting. The model is gaining importance in support of the stang of call centers, which is a central step in their Service-Engineering. We discuss computations of performance measures, both theoretical and software-based (via the 4CallCenter software). Then several examples of Palm/Erlang- A applications are presented, mostly motivated by and based on real call center data.
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The more challenging case of transient analysis of Markov chains is investigated in Chapter 5. The chapter introduces symbolic solutions in simple cases such as small or very regular state spaces. In general, numerical techniques are more suitable and are therefore covered in detail. Uniformization and some variants thereof are introduced as the method of choice for transient analysis in most cases. Particular emphasis is given to stiffness tolerant uniformization that can be of practical relevance in many modeling scenarios where relatively rare and fast events occur concurrently. As an alternative a method for aggregation/disaggregation of stiff Markov chains is introduced for a computation of approximate transient state probabilities. The method is based on a distinction of fast recurrent and fast transient sets of states that can be aggregated with relatively small error. All steps are illustrated by a detailed example model of server breakdown and repair. In addition to numerical results an error analysis is provided as well.
Book
Coloured Petri Nets (CPN) is a graphical language for modelling and validating concurrent and distributed systems, and other systems in which concurrency plays a major role. The development of such systems is particularly challenging because of inherent intricacies like possible nondeterminism and the immense number of possible execution sequences. In this textbook Jensen and Kristensen introduce the constructs of the CPN modelling language and present the related analysis methods in detail. They also provide a comprehensive road map for the practical use of CPN by showcasing selected industrial case studies that illustrate the practical use of CPN modelling and validation for design, specification, simulation, verification and implementation in various application domains. Their presentation primarily aims at readers interested in the practical use of CPN. Thus all concepts and constructs are first informally introduced through examples and then followed by formal definitions (which may be skipped). The book is ideally suitable for a one-semester course at an advanced undergraduate or graduate level, and through its strong application examples can also serve for self-study. An accompanying website offers additional material such as slides, exercises and project proposals.
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This paper describes a course on modelling and validation of concurrent systems given by the authors at the Department of Computer Science, University of Aarhus. The course uses Coloured Petri Nets (CPNs) as the formal modelling language for concurrency, and exposes students to the benefits and applications of modelling for designing and reasoning about the behaviour of concurrent systems. After the course the participants will have detailed knowledge of CPNs and practical experience with modelling and validation of concurrent systems. The course emphasises the practical use of modelling and validation and has less focus on the formal foundation of CPNs. The course is based on a new textbook on CPNs.
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The stochastic processes which occur in the theory of queues are in general not Markovian and special methods are required for their analysis. In many cases the problem can be greatly simplified by restricting attention to an imbedded Markov chain. In this paper some recent work on single-server queues is first reviewed from this standpoint, and the method is then applied to the analysis of the following many-server queuing-system: Input: the inter-arrival times are independently and identically distributed in an arbitrary manner. Queue-discipline: "first come, first served." Service-mechanism: a general number, $s$, of servers; negative-exponential service-times. If $Q$ is the number of people waiting at an instant just preceding the arrival of a new customer, and if $w$ is the waiting time of an arbitrary customer, then it will be shown that the equilibrium distribution of $Q$ is a geometric series mixed with a concentration at $Q = 0$ and that the equilibrium distribution of $w$ is a negative-exponential distribution mixed with a concentration at $w = 0$. (In the particular case of a single server this property of the waiting-time distribution was first discovered by W. L. Smith.) The paper concludes with detailed formulae and numerical results for the following particular cases: Numbers of servers: s = 1, 2 and 3. Types of input: (i) Poissonian and (ii) regular.
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