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71
Robust Process Discovery with Artificial Negative Events
- The Journal of Machine Learning Research
"... Process discovery is the automated construction of structured process models from information system event logs. Such event logs often contain positive examples only. Without negative examples, it is a challenge to strike the right balance between recall and specificity, and to deal with problems su ..."
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Cited by 26 (1 self)
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Process discovery is the automated construction of structured process models from information system event logs. Such event logs often contain positive examples only. Without negative examples, it is a challenge to strike the right balance between recall and specificity, and to deal with problems such as expressiveness, noise, incomplete event logs, or the inclusion of prior knowledge. In this paper, we present a configurable technique that deals with these challenges by representing process discovery as a multi-relational classification problem on event logs supplemented with Artificially Generated Negative Events (AGNEs). This problem formulation allows using learning algorithms and evaluation techniques that are well-know in the machine learning community. Moreover, it allows users to have a declarative control over the inductive bias and language bias.
Process Compliance Measurement based on Behavioural Profiles
"... Abstract. Process compliance measurement is getting increasing attention in companies due to stricter legal requirements and market pressure for operational excellence. On the other hand, the metrics to quantify process compliance have only been defined recently. A major criticism points to the fact ..."
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Cited by 12 (2 self)
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Abstract. Process compliance measurement is getting increasing attention in companies due to stricter legal requirements and market pressure for operational excellence. On the other hand, the metrics to quantify process compliance have only been defined recently. A major criticism points to the fact that existing measures appear to be unintuitive. In this paper, we trace back this problem to a more foundational question: which notion of behavioural equivalence is appropriate for discussing compliance? We present a quantification approach based on behavioural profiles, which is a process abstraction mechanism. Behavioural profiles can be regarded as weaker than existing equivalence notions like trace equivalence, and they can be calculated efficiently. As a validation, we present a respective implementation that measures compliance of logs against a normative process model. This implementation is being evaluated in a case study with an international service provider. 1
Application of Process Mining in Healthcare – A Case Study in a Dutch Hospital
"... Abstract. To gain competitive advantage, hospitals try to streamline their processes. In order to do so, it is essential to have an accurate view of the “careflows” under consideration. In this paper, we apply process mining techniques to obtain meaningful knowledge about these flows, e.g., to disco ..."
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Abstract. To gain competitive advantage, hospitals try to streamline their processes. In order to do so, it is essential to have an accurate view of the “careflows” under consideration. In this paper, we apply process mining techniques to obtain meaningful knowledge about these flows, e.g., to discover typical paths followed by particular groups of patients. This is a non-trivial task given the dynamic nature of healthcare processes. The paper demonstrates the applicability of process mining using a real case of a gynecological oncology process in a Dutch hospital. Using a variety of process mining techniques, we analyzed the healthcare process from three different perspectives: (1) the control flow perspective, (2) the organizational perspective and (3) the performance perspective. In order to do so we extracted relevant event logs from the hospitals information system and analyzed these logs using the ProM framework. The results show that process mining can be used to provide new insights that facilitate the improvement of existing careflows. 1
Discovering Colored Petri Nets from Event Logs
- SOFTWARE TOOLS FOR TECHNOLOGY TRANSFER
"... Process-aware information systems typically log events (e.g., in transaction logs or audit trails) related to the actual execution of business processes. Analysis of these execution logs may reveal important knowledge that can help organizations to improve the quality of their services. Starting fro ..."
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Cited by 8 (5 self)
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Process-aware information systems typically log events (e.g., in transaction logs or audit trails) related to the actual execution of business processes. Analysis of these execution logs may reveal important knowledge that can help organizations to improve the quality of their services. Starting from a process model, which can be discovered by conventional process mining algorithms, we analyze how data attributes influence the choices made in the process based on past process executions using decision mining, also referred to as decision point analysis. In this paper we describe how the resulting model (including the discovered data dependencies) can be represented as a Colored Petri Net (CPN), and how further perspectives, such as the performance and organizational perspective, can be incorporated. We also present a CPN Tools Export plug-in implemented within the ProM framework. Using this plug-in, simulation models in ProM obtained via a combination of various process mining techniques can be exported to CPN Tools. We believe that the combination of automatic discovery of process models using ProM and the simulation capabilities of CPN Tools offers an innovative way to improve business processes. The discovered process model describes reality better than most hand-crafted simulation models. Moreover, the simulation models are constructed in such a way that it is easy to explore various redesigns.
Vanthienen J.: Process Mining as First-Order Classification Learning on Logs with Negative Events
- BPM 2007 Workshops, LNCS 4928
, 2008
"... Abstract. Process mining is the automated construction of process models from information system event logs. In this paper we identify three fundamental difficulties related to process mining: the lack of neg-ative information, the presence of history-dependent behavior and the presence of noise. Th ..."
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Cited by 7 (2 self)
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Abstract. Process mining is the automated construction of process models from information system event logs. In this paper we identify three fundamental difficulties related to process mining: the lack of neg-ative information, the presence of history-dependent behavior and the presence of noise. These difficulties can elegantly dealt with when pro-cess mining is represented as first-order classification learning on event logs supplemented with negative events. A first set of process discovery experiments indicates the feasibility of this learning technique. 1
Automatic Business Process Pattern Matching for Enterprise Services Design
- in: IEEE Congress on Services Part II, IEEE Computer Society
"... Abstract—Designing the adequate scope and granularity of services is critical for their effective reuse. Patterns at business process level are abstractions of common and reusable designs to operate businesses. Business Process (BP) patterns can capture expert process design knowledge and greatly be ..."
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Abstract—Designing the adequate scope and granularity of services is critical for their effective reuse. Patterns at business process level are abstractions of common and reusable designs to operate businesses. Business Process (BP) patterns can capture expert process design knowledge and greatly benefit the design of new enterprise services by guiding the definition of their scope and granularity. Identifying pattern instances in real and large documented business processes is a challenging task, requiring the analysis of the structure, semantics and behaviour associated to process descriptions. In this paper1 we present a solution to identify BP patterns based on a graph matching mechanism. Structural and semantics aspects, including natural language processing, are addressed. The approach moves one step further to increase automation during the design of process-centric enterprise services. We demonstrate the approach, discuss its limitations, novelty and practical benefits by using a case study based on the National Revenue Agency case at SOPOSE08. Index Terms—Service design, service reuse, service granularity, business process pattern, pattern matching. I.
Process mining software repositories
- Eindhoven University of Technology
, 2010
"... Abstract—Software developers ’ activities are in general recorded in software repositories such as version control systems, bug trackers and mail archives. While abundant information is usually present in such repositories, successful information extraction is often challenged by the necessity to si ..."
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Cited by 7 (0 self)
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Abstract—Software developers ’ activities are in general recorded in software repositories such as version control systems, bug trackers and mail archives. While abundant information is usually present in such repositories, successful information extraction is often challenged by the necessity to simultaneously analyze different repositories and to combine the information obtained. We propose to apply process mining techniques, origi-nally developed for business process analysis, to address this challenge. However, in order for process mining to become applicable, different software repositories should be combined, and “related ” software development events should be matched: e.g., mails sent about a file, modifications of the file and bug reports that can be traced back to it. The combination and matching of events has been im-plemented in FRASR (FRamework for Analyzing Software Repositories), augmenting the process mining framework ProM. FRASR has been successfully applied in a series of case studies addressing such aspects of the development process as roles of different developers and the way bug reports are handled. Keywords-Process mining, software repositories I.
What BPM technology can do for healthcare process support
- In: AIME’11 (2011
"... Abstract. Healthcare organizations are facing the challenge of deliver-ing personalized services to their patients in a cost-effective and efficient manner. This, in turn, requires advanced IT support for healthcare pro-cesses covering both organizational procedures and knowledge-intensive, dynamic ..."
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Cited by 6 (4 self)
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Abstract. Healthcare organizations are facing the challenge of deliver-ing personalized services to their patients in a cost-effective and efficient manner. This, in turn, requires advanced IT support for healthcare pro-cesses covering both organizational procedures and knowledge-intensive, dynamic treatment processes. Nowadays, required agility is often hin-dered by a lack of flexibility in hospital information systems. To over-come this inflexibility a new generation of information systems, denoted as process-aware information systems (PAISs), has emerged. In contrast to data- and function-centered information systems, a PAIS separates process logic from application code and thus provides an additional architectural layer. However, the introduction of process-aware hospi-tal information systems must neither result in rigidity nor restrict staff members in their daily work. This keynote presentation reflects on recent developments from the business process management (BPM) domain, which enable process adaptation, process flexibility, and process evolu-tion. These key features will be illustrated along existing BPM frame-works. Altogether, emerging BPM methods, concepts and technologies will contribute to further enhance IT support for healthcare processes. 1
Alaska Simulator Toolset for Conducting Controlled Experiments – Tool Paper –
"... Abstract. Alaska Simulator Toolset is an interactive software suite developed at the University of Innsbruck which allows to explore different approaches to process flexibility by using a familiar metaphor, i.e., travel planning and execution. In particular, Alaska Simulator Toolset is used for stud ..."
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Abstract. Alaska Simulator Toolset is an interactive software suite developed at the University of Innsbruck which allows to explore different approaches to process flexibility by using a familiar metaphor, i.e., travel planning and execution. In particular, Alaska Simulator Toolset is used for studying research questions in the context of business process management and other related fields. For this, Alaska Simulator Toolset provides integrated support of different approaches to process flexibility fostering their systematic comparison. Moreover, Alaska Simulator Toolset facilitates the design and execution of controlled experiments through experimental workflow support. Providing effective IT support for business processes has become an essential activity of enterprises in order to stay competitive in today’s market [1]. When assessing the usability of BPM approaches, however, enterprises have to rely on vendor promises or qualitative data rather than on empirical or experimental research [2]. This is rather surprising as these research methods have been
On the exploitation of process mining for security audits: The process discovery case
- in ACM SAC. ACM
"... This paper reports on the potential of process mining as a basis for security audits of business process and corresponding business process management systems. In particular, it focuses on process discovery as a means to reconstruct process-related structures from event logs, such as the process’ co ..."
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Cited by 5 (5 self)
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This paper reports on the potential of process mining as a basis for security audits of business process and corresponding business process management systems. In particular, it focuses on process discovery as a means to reconstruct process-related structures from event logs, such as the process’ control flow, social network and data flows. Based on this information, security analysis to determine the compliance with security and privacy requirements can be automated.
