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Collaborative process maturing support by mining activity streams
Abstract
Usually, knowledge workers are said to not benefit from business process management (BPM) systems, since their main tasks are weakly structured and not representable by a workflow. However, not all of their tasks are equally weak structured, and with adaptive case management (ACM) solutions, a new category of tools came up to support those processes, even if they are weakly structured. This paper introduces an approach to support the creation of cases for ACM engines by mining activities from an activity stream and suggesting tasks that a knowledge worker can use to create a case. Furthermore, the approach supports maturing of the case towards a workflow by detecting repeating sequences in the execution of tasks and suggesting sub processes for the case which is possible with the case management model and notation (CMMN) together with the business process model and notation (BPMN). To allow for further enhancement of the cases, the ACM solution is extended with social collaboration features, so that people working on the case can comment and rate single tasks. The goal is to show that it is possible to establish ties from social activities and Web 2.0 to ACM and BPM. The presented solution uses a graph database as a basis for activity mining.
IT support for collaboration in knowledge-intensive processes has gone through quite some change since the beginning of the century and is providing more and more support for knowledge workers. Although single systems are getting easier to use, they are often not replacing former systems but accompany them, which makes the overall system landscape harder to oversee for knowledge workers. Future information systems should therefore combine the existing building blocks under a consistent user interface and assist the user in storing information at the right place by providing access to them directly from a process-specific user interface. This can be business or knowledge processes. This chapter discusses the development of digital collaboration solutions and shows how social software, and machine-understandability have changed them to better support knowledge processes. After that it is discussed how the Social Collaboration Hub, a BMBF-funded project, fulfills the requirements for future information systems.
There are many scenarios in which business processes will benefit from the integration of geographical information for its management. In this paper we discuss a set of requirements for ad-hoc geo-referenced Business Process Management (BPM), noting in particular the conflicts between spatial and task dependencies when coordinating activities. We suggest the predominance of spatial dependencies and propose the integration of process models in georeferencing tools. We analyse the communication needs of geo-referenced and BPM processes and suggest the adoption of microblogging platforms for coordination support. We also discuss the implementation of an ad-hoc georeferenced BPM tool, specify the microblogging messages needed to coordinate georeferenced activities, and discuss a preliminary formative evaluation of the proposed implementation.
After the human-oriented, technology-oriented (documents), process-oriented and social KM phases, KM support now needs integration of those beneficial technologies instead of hyping one and neglecting the other. This paper proposes a highly modular yet integrated approach to support KM instruments and shows how this approach is implemented based on open source collaboration systems in the SCHub project which represents an enterprise knowledge infrastructure (EKI). The applicability is shown with three KM instruments in the areas people, contents and processes. Social media and search functionality serve as an integration factor for the EKI.
Modern companies continue investing more and more in the creation, maintenance and change of software systems, but the proper specification and design of such systems continues to be a challenge. The majority of current approaches either ignore real user and system runtime behavior or consider it only informally. This leads to a rather prescriptive top-down approach to software development.
In this paper, we propose a bottom-up approach, which takes event logs (e.g., trace data) of a software system for the analysis of the user and system runtime behavior and for improving the software. We use well-established methods from the area of process mining for this analysis. Moreover, we suggest embedding process mining into the agile development lifecycle.
The goal of this position paper is to motivate the need for foundational research in the area of software process mining (applying process mining to software analysis) by showing the relevance and listing open challenges. Our proposal is based on our experiences with analyzing a big productive touristic system. This system was developed using agile methods and process mining could be effectively integrated into the development lifecycle.
Adaptive Case Management (ACM) is a new paradigm that facilitates the coordination of knowledge work through case handling. Current ACM systems, however, lack support of providing sophisticated user guidance for next step recommendations and predictions about the case future. In recent years, process mining research developed approaches to make recommendations and predictions based on event logs readily available in process-aware information systems. This paper builds upon those approaches and integrates them into an existing ACM solution. The research goal is to design and develop a prototype that gives next step recommendations and predictions based on process mining techniques in ACM systems. The models proposed, recommend actions that shorten the case running time, mitigate deadline transgressions, support case goals and have been used in former cases with similar properties. They further give case predictions about the remaining time, possible deadline violations, and whether the current case path supports given case goals. A final evaluation proves that the prototype is indeed capable of making proper recommendations and predictions. In addition, starting points for further improvement are discussed.
Adaptive Case Management is a Business Process Management approach that is quickly gaining the attention of practitioners and scientists. In an effort to examine how Subject-oriented Business Process Management relates to Adaptive Case Management, this contribution proposes extending an existing ACM approach inspired by multi-agent systems with the capability of defining temporal-logical dependencies between tasks using Subject-oriented Business Process Management.
Adaptivity is the ability to adjust behavior to changes in context. In enterprise ACM, the need for control, uniformity, and analysis meet end-user’s needs to adapt to situations at hand. The interplay of different layers of declaratively defined templates can meet these needs in a flexible and sustainable manner. A data centric architecture with a library of process snippets building on standard activities and actions and a model of the organization with roles, provides an operational quality system. By combining this with a standardized declarative modeling of the case objects with codes and soft typing, we achieve a flexible platform for domain specific enterprise ACM systems. The result is a case- and goal-driven approach to adaptivity in ACM, taking into account knowledge workers’ autonomy and the information context of the work. This paper discusses means of achieving adaptivity in ACM solutions and presents examples of practical realization by these means.
In Adaptive Case Management (ACM) systems, knowledge workers have the flexibility to deal with unpredictable situations. Compared with a classical BPM approach the extensive prescriptive process analysis and definitions are replaced by context-sensitive proposals, which is more suited for knowledge-intensive work. Thus, it is vital that ACM systems support knowledge workers with knowledge captured from previous work which can be ambiguous for the system. This paper proposes an approach to support knowledge workers based on the knowledge previously applied by others in the form of a User Trained Agent that learns from ad hoc actions taken by knowledge workers to suggest best next actions for the current situation. The proposed best next actions are analyzed for coherence.
Making implicit procedural knowledge explicit by business processes is an established means for analyzing and improving efficiency of organizational knowledge work. Such an explicit definition of processes is only to a limited extend applicable to individual knowledge work due to its high complexity and dynamics. Nevertheless, there are structured and repeating activities in individual knowledge work that are worth to capture. Dynamic and ad-hoc workflows allow for capturing these activities. However, they do not allow for synchronizing them with business processes. This hampers communication of best practices and transfer of knowledge between the knowledge workers. Our analysis shows that none of the existing models fully provides the desired functionality. Thus, we have developed our own approach the pattern system strukt and applied it at the example scenario of a real architectural office. The strukt prototype helps knowledge workers to organize their activities, collaborate with others, and synchronize one's very individual activities with the business processes.
Traditional Business Process Management (BPM) poses a number of limitations for the management of ad-hoc processes, where the execution paths are not designed a priori and evolve during enactment. Social BPM, which predicates to integrate social software into the BPM lifecycle, has emerged as an answer to such limitations. This paper presents a framework for social BPM in which social tagging is used to capture process knowledge emerging during the enactment and design of the processes. Process knowledge concerns both the type of activities chosen to fulfil a certain goal and the skills and experience of users in executing specific tasks. Such knowledge is exploited by recommendation tools to support the design and enactment of future process instances. We first provide an overview of our framework, introducing the concepts of role and task recommendations, which are supported by social tagging. These mechanisms are then elaborated further by an example. Eventually, we discuss a prototype of our framework enabling collaborative process design and execution.
Adaptive case management (ACM) is an increasingly popular approach for supporting knowledge-intensive business processes. Being a new approach, it still lacks a similar degree of standardization as the more established business process management (BPM) approaches. This paper introduces the new Case Management and Model and Notation (CMMN) standard. Based on a set of ACM requirements, the applicability of CMMN for modeling ACM cases is examined thoroughly and extensions improving the ACM support of CMMN are proposed.