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Wil M. P. Van Der Aalst - One of the best experts on this subject based on the ideXlab platform.
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privacy preserving data publishing in Process Mining
arXiv: Cryptography and Security, 2021Co-Authors: Majid Rafiei, Wil M. P. Van Der AalstAbstract:Process Mining aims to provide insights into the actual Processes based on event data. These data are often recorded by information systems and are widely available. However, they often contain sensitive private information that should be analyzed responsibly. Therefore, privacy issues in Process Mining are recently receiving more attention. Privacy preservation techniques obviously need to modify the original data, yet, at the same time, they are supposed to preserve the data utility. Privacy-preserving transformations of the data may lead to incorrect or misleading analysis results. Hence, new infrastructures need to be designed for publishing the privacy-aware event data whose aim is to provide metadata regarding the privacy-related transformations on event data without revealing details of privacy preservation techniques or the protected information. In this paper, we provide formal definitions for the main anonymization operations, used by privacy models in Process Mining. These are used to create an infrastructure for recording the privacy metadata. We advocate the proposed privacy metadata in practice by designing a privacy extension for the XES standard and a general data structure for event data which are not in the form of standard event logs.
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tlkc privacy model for Process Mining
Research Challenges in Information Science, 2020Co-Authors: Majid Rafiei, Miriam Wagner, Wil M. P. Van Der AalstAbstract:Process Mining aims to provide insights into the actual Processes based on event data. These data are widely available and often contain private information about individuals. Consider for example health-care information systems recording highly sensitive data related to diagnosis and treatment activities. Process Mining should reveal insights in the form of annotated models, yet, at the same time, should not reveal sensitive information about individuals. In this paper, we discuss the challenges regarding directly applying existing well-known privacy-preserving techniques to event data. We introduce the TLKC-privacy model for Process Mining that provides privacy guarantees in terms of group-based anonymization. It extends and customizes the LKC-privacy model presented to deal with high-dimensional, sparse, and sequential trajectory data. Experiments on real-life event data demonstrate that our privacy model maintains a high utility for Process discovery and performance analyses while preserving the privacy of the cases.
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an open source integration of Process Mining features into the camunda workflow engine data extraction and challenges
ICPM Doctoral Consortium Tools, 2020Co-Authors: Alessandro Berti, Wil M. P. Van Der Aalst, David Zang, Magdalena LangAbstract:Process Mining provides techniques to improve the performance and compliance of operational Processes. Although sometimes the term "workflow Mining" is used, the application in the context of Workflow Management (WFM) and Business Process Management (BPM) systems is limited. The main reason is that WFM/BPM systems control the Process, leaving less room for flexibility and the corresponding deviations. However, as this paper shows, it is easy to extract event data from systems like Camunda, one of the leading open-source WFM/BPM systems. Moreover, although the respective Process engines control the Process flow, Process Mining is still able to provide valuable insights, such as the analysis of the performance of the paths and the Mining of the decision rules. This demo paper presents a Process Mining connector to Camunda that extracts event logs and Process models, allowing for the application of existing Process Mining tools. We also analyzed the added value of different Process Mining techniques in the context of Camunda. We discuss a subset of Process Mining techniques that nicely complements the Process intelligence capabilities of Camunda. Through this demo paper, we hope to boost the use of Process Mining among Camunda users.
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academic view development of the Process Mining discipline
2020Co-Authors: Wil M. P. Van Der AalstAbstract:This chapter reflects on the adoption of traditional Process Mining techniques and the expansion of scope, discussed with five trends. An inconvenient truth explains why—despite considerable progress in Process Mining research—commercial tools tend to not use the state-of-the-art and make “short-cuts” instead that seem harmless at first, but inevitably lead to problems at a later stage. Seven novel challenges provide an outlook on open research topics. In a final appeal, the term of “Process hygiene” is coined to make Process Mining the “new normal.”
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a practitioner s guide to Process Mining limitations of the directly follows graph
Procedia Computer Science, 2019Co-Authors: Wil M. P. Van Der AalstAbstract:Abstract Process Mining techniques use event data to show what people, machines, and organizations are really doing. Process Mining provides novel insights that can be used to identify and address performance and compliance problems. In recent years, the adoption of Process Mining in practice increased rapidly. It is interesting to see how ideas first developed in open-source tools like ProM, get transferred to the dozens of available commercial Process Mining tools. However, these tools still resort to producing Directly-Follows Graphs (DFGs) based on event data rather than using more sophisticated notations also able to capture concurrency. Moreover, to tackle complexity, DFGs are seamlessly simplified by removing nodes and edges based on frequency thresholds. Process-Mining practitioners tend to use such simplified DFGs actively. Despite their simplicity, these DFGs may be misleading and users need to know how these Process models are generated before interpreting them. In this paper, we discuss the pitfalls of using simple DFGs generated by commercial tools. Practitioners conducting a Process-Mining project need to understand the risks associated with the (incorrect) use of DFGs and frequency-based simplification. Therefore, we put these risks in the spotlight.
R Jagadeesh Chandra P Bose - One of the best experts on this subject based on the ideXlab platform.
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trace alignment in Process Mining opportunities for Process diagnostics
Business Process Management, 2010Co-Authors: R Jagadeesh Chandra P Bose, Wil M. P. Van Der AalstAbstract:Process Mining techniques attempt to extract non-trivial knowledge and interesting insights from event logs. Process Mining provides a welcome extension of the repertoire of business Process analysis techniques and has been adopted in various commercial BPM systems (BPM|one, Futura Reflect, ARIS PPM, Fujitsu, etc.). Unfortunately, traditional Process discovery algorithms have problems dealing with lessstructured Processes. The resulting models are difficult to comprehend or even misleading. Therefore, we propose a new approach based on trace alignment. The goal is to align traces in a way that event logs can be explored easily. Trace alignment can be used in a preProcessing phase where the event log is investigated or filtered and in later phases where detailed questions need to be answered. Hence, it complements existing Process Mining techniques focusing on discovery and conformance checking.
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abstractions in Process Mining a taxonomy of patterns
Business Process Management, 2009Co-Authors: R Jagadeesh Chandra P Bose, Wil M. P. Van Der AalstAbstract:Process Mining refers to the extraction of Process models from event logs. Real-life Processes tend to be less structured and more flexible. Traditional Process Mining algorithms have problems dealing with such unstructured Processes and generate spaghetti-like Process models that are hard to comprehend. One reason for such a result can be attributed to constructing Process models from raw traces without due pre-Processing. In an event log, there can be instances where the system is subjected to similar execution patterns/behavior. Discovery of common patterns of invocation of activities in traces (beyond the immediate succession relation) can help in improving the discovery of Process models and can assist in defining the conceptual relationship between the tasks/activities. In this paper, we characterize and explore the manifestation of commonly used Process model constructs in the event log and adopt pattern definitions that capture these manifestations, and propose a means to form abstractions over these patterns. We also propose an iterative method of transformation of traces which can be applied as a pre-Processing step for most of today's Process Mining techniques . The proposed approaches are shown to identify promising patterns and conceptually-valid abstractions on a real-life log. The patterns discussed in this paper have multiple applications such as trace clustering, fault diagnosis/anomaly detection besides being an enabler for hierarchical Process discovery.
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context aware trace clustering towards improving Process Mining results
SIAM International Conference on Data Mining, 2009Co-Authors: R Jagadeesh Chandra P Bose, Wil M. P. Van Der AalstAbstract:Process Mining refers to the extraction of Process models from event logs. Real-life Processes tend to be less structured and more flexible. Traditional Process Mining algorithms have problems dealing with such unstructured Processes and generate spaghetti-like Process models that are hard to comprehend. An approach to overcome this is to cluster Process instances (a Process instance is manifested as a trace and an event log corresponds to a multi-set of traces) such that each of the resulting clusters correspond to a coherent set of Process instances that can be adequately represented by a Process model. In this paper, we propose a context aware approach to trace clustering based on generic edit distance. It is well known that the generic edit distance framework is highly sensitive to the costs of edit operations. We define an automated approach to derive the costs of edit operations. The method proposed in this paper outperforms contemporary approaches to trace clustering in Process Mining. We evaluate the goodness of the formed clusters using established fitness and comprehensibility metrics defined in the context of Process Mining. The proposed approach is able to generate clusters such that the Process models mined from the clustered traces show a high degree of fitness and comprehensibility when compared to contemporary approaches.
A J M M Weijters - One of the best experts on this subject based on the ideXlab platform.
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Data From Configuration Management Tools As Sources For Software Process Mining
2013Co-Authors: Jos J M Trienekens, Rj Rob Kusters, J Jana Samalik, A J M M WeijtersAbstract:Process Mining has proven to be a valuable approach that provides new and objective insights into Processes within organizations. Based on sets of well-structured data, the underlying ‘actual’ Processes can be extracted and Process models can be constructed automatically, i.e., the Process model can be ‘mined’. Successful Process Mining depends on the availability of well-structured and suitable data. This paper investigates the potential of software configuration management (SCM) and SCM- tools for software Process Mining. In a validation section, data collected by a SCM tool in practice are used to apply Process-Mining techniques on a particular software Process, i.e., a Change Control Board (CCB) Process in a large industrial company. Application of Process Mining techniques revealed that although people tend to believe that formally specified and well-documented Processes are followed, the ‘actual’ Process in practice is different. Control-flow discovery revealed that in the CCB Process in most of the cases, i.e., 70%, an important CCB task ‘Analysis’ was skipped.
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The Need for a Process Mining Evaluation Framework in Research and Practice
Business Process Management Workshops, 2008Co-Authors: A. Rozinat, C.W. Günther, Ana Karla Alves De Medeiros, A J M M Weijters, Wil M. P. Van Der AalstAbstract:Although there has been much progress in developing Process Mining algorithms in recent years, no effort has been put in developing a common means of assessing the quality of the models discovered by these algorithms. In this paper, we motivate the need for such an evaluation mechanism, and outline elements of an evaluation framework that is intended to enable (a) Process Mining researchers to compare the performance of their algorithms, and (b) end users to evaluate the validity of their Process Mining results.
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Process Mining based on clustering a quest for precision
Business Process Management, 2007Co-Authors: Ana Karla Alves De Medeiros, A J M M Weijters, Wil M. P. Van Der Aalst, Antonella Guzzo, Gianluigi Greco, Boudewijn F Van Dongen, Domenico SaccaAbstract:Process Mining techniques attempt to extract non-trivial and useful information from event logs recorded by information systems. For example, there are many Process Mining techniques to automatically discover a Process model based on some event log. Most of these algorithms perform well on structured Processes with little disturbances. However, in reality it is difficult to determine the scope of a Process and typically there are all kinds of disturbances. As a result, Process Mining techniques produce spaghetti-like models that are difficult to read and that attempt to merge unrelated cases. To address these problems, we use an approach where the event log is clustered iteratively such that each of the resulting clusters corresponds to a coherent set of cases that can be adequately represented by a Process model. The approach allows for different clustering and Process discovery algorithms. In this paper, we provide a particular clustering algorithm that avoids over-generalization and a Process discovery algorithm that is much more robust than the algorithms described in literature [1]. The whole approach has been implemented in ProM.
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Business Process Mining: An industrial application
Information Systems, 2007Co-Authors: Wil M. P. Van Der Aalst, A K A. K. Alves De Medeiros, B. F. Van Dongen, Hajo A. Reijers, M Song, A J M M Weijters, H M W VerbeekAbstract:Contemporary information systems (e.g., WfM, ERP, CRM, SCM, and B2B systems) record business events in so-called event logs. Business Process Mining takes these logs to discover Process, control, data, organizational, and social structures. Although many researchers are developing new and more powerful Process Mining techniques and software vendors are incorporating these in their software, few of the more advanced Process Mining techniques have been tested on real-life Processes. This paper describes the application of Process Mining in one of the provincial offices of the Dutch National Public Works Department, responsible for the construction and maintenance of the road and water infrastructure. Using a variety of Process Mining techniques, we analyzed the Processing of invoices sent by the various subcontractors and suppliers from three different perspectives: (1) the Process perspective, (2) the organizational perspective, and (3) the case perspective. For this purpose, we used some of the tools developed in the context of the ProM framework. The goal of this paper is to demonstrate the applicability of Process Mining in general and our algorithms and tools in particular.
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Process Mining with the HeuristicsMiner Algorithm
Cirp Annals-manufacturing Technology, 2006Co-Authors: A J M M Weijters, Wil M. P. Van Der Aalst, Ana Karla Alves De MedeirosAbstract:The basic idea of Process Mining is to extract knowledge from event logs recorded by an information system. Until recently, the information in these event logs was rarely used to analyze the underly- ing Processes. Process Mining aims at improving this by providing tech- niques and tools for discovering Process, organizational, social, and per- formance information from event logs. Fuelled by the omnipresence of event logs in transactional information systems (cf. WFM, ERP, CRM, SCM, and B2B systems), Process Mining has become a vivid research area 1, 2. In this paper we introduce the challenging Process Mining domain and discuss a heuristics driven Process Mining algorithm; the so-called HeuristicsMiner in detail. HeuristicsMiner is a practical ap- plicable Mining algorithm that can deal with noise, and can be used to express the main behavior (i.e. not all details and exceptions) registered in an event log. In the experimental section of this paper we introduce benchmark material (12.000 different event logs) and measurements by which the performance of Process Mining algorithms can be measured.
Wil M P Van Der Aalst - One of the best experts on this subject based on the ideXlab platform.
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supporting confidentiality in Process Mining using abstraction and encryption
SIMPDA, 2018Co-Authors: Majid Rafiei, Leopold Von Waldthausen, Wil M P Van Der AalstAbstract:Process Mining aims to bridge the gap between data science and Process science by providing a variety of powerful data-driven analyses techniques on the basis of event data. These techniques encompass automatically discovering Process models, detecting and predicting bottlenecks, and finding Process deviations. In Process Mining, event data containing the full breadth of resource information allows for performance analysis and discovering social networks. On the other hand, event data are often highly sensitive, and when the data contain private information, privacy issues arise. Surprisingly, there has currently been little research toward security methods and encryption techniques for Process Mining. Therefore, in this paper, using abstraction, we propose an approach that allows us to hide confidential information in a controlled manner while ensuring that the desired Process Mining results can still be obtained. We show how our approach can support confidentiality while discovering control-flow and social networks. A connector method is applied as a technique for storing associations between events securely. We evaluate our approach by applying it on real-life event logs.
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Scientific workflows for Process Mining: building blocks, scenarios, and implementation
International Journal on Software Tools for Technology Transfer, 2016Co-Authors: Alfredo Bolt, Massimiliano Leoni, Wil M P Van Der AalstAbstract:Over the past decade Process Mining has emerged as a new analytical discipline able to answer a variety of questions based on event data. Event logs have a very particular structure; events have timestamps, refer to activities and resources, and need to be correlated to form Process instances. Process Mining results tend to be very different from classical data Mining results, e.g., Process discovery may yield end-to-end Process models capturing different perspectives rather than decision trees or frequent patterns. A Process-Mining tool like ProM provides hundreds of different Process Mining techniques ranging from discovery and conformance checking to filtering and prediction. Typically, a combination of techniques is needed and, for every step, there are different techniques that may be very sensitive to parameter settings. Moreover, event logs may be huge and may need to be decomposed and distributed for analysis. These aspects make it very cumbersome to analyze event logs manually. Process Mining should be repeatable and automated . Therefore, we propose a framework to support the analysis of Process Mining workflows. Existing scientific workflow systems and data Mining tools are not tailored towards Process Mining and the artifacts used for analysis (Process models and event logs). This paper structures the basic building blocks needed for Process Mining and describes various analysis scenarios. Based on these requirements we implemented RapidProM , a tool supporting scientific workflows for Process Mining. Examples illustrating the different scenarios are provided to show the feasibility of the approach.
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Process Mining as the superglue between data science and enterprise computing
Enterprise Distributed Object Computing, 2014Co-Authors: Wil M P Van Der AalstAbstract:Process Mining provides new ways to utilize the abundance of data in enterprises. Suddenly many organizations realize thatsurvival is not possible without exploiting available data intelligently. A new profession is emerging: the data scientist. Justlike computer science emerged as a new discipline from mathematics when computers became abundantly available, we nowsee the birth of data science as a new discipline driven by the torrents of data available today. Process Mining will be anintegral part of the data scientist's toolbox. Also enterprise computing will need to focus on Process innovation through theintelligent use of event data.This keynote talk will focus on challenges related to 'Process Mining in the large', i.e., dealing with many Processes, manyactors, many data sources, and huge amounts of data at the same time. By adequately addressing these challenges (e.g., usingProcess cubes) we get a new kind of superglue that will impact the future of enterprise computing.
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Process Mining in healthcare data challenges when answering frequently posed questions
Knowledge Representation for Health-Care, 2012Co-Authors: R Ronny S Mans, Wil M P Van Der Aalst, Rob J B Vanwersch, A J MolemanAbstract:In hospitals, huge amounts of data are recorded concerning the diagnosis and treatments of patients. Process Mining can exploit such data and provide an accurate view on healthcare Processes and show how they are really executed. In this paper, we describe the different types of event data found in current Hospital Information Systems (HISs). Based on this classification of available data, open problems and challenges are discussed that need to be solved in order to increase the uptake of Process Mining in healthcare.
Matzner Martin - One of the best experts on this subject based on the ideXlab platform.
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A Process Mining Software Comparison
2021Co-Authors: Viner Daniel, Stierle Matthias, Matzner MartinAbstract:www.ProcessMining-software.com is a dedicated website for Process Mining software comparison and was developed to give practitioners and researchers an overview of commercial tools available on the market. Based on literature review and experimental tool testing, a set of criteria was developed in order to assess the tools' functional capabilities in an objective manner. With our publicly accessible website, we intend to increase the transparency of tool functionality. Being an academic endeavour, the non-commercial nature of the study ensures a less biased assessment as compared with reports from analyst firms
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Creating Humanistic Value with Process Mining for Improving Work Conditions - A Sociotechnical Perspective
AIS Electronic Library (AISeL), 2020Co-Authors: Tang Willi, Matzner MartinAbstract:Process Mining has emerged as a specific approach to data Mining which leverages historical Process data from Process-aware information systems to derive insights and visual representations of an organisations business Processes. It affords compa-nies the ability to identify critical paths. Research has so far focused on analysing the instrumental value of Process Mining on firm performance and how to improve and extend this set of tools for more accurate and comprehensive predictions. However, humanistic outcomes of Process Mining usage (e.g., job satisfaction, workload) have largely been neglected. In this research-in-progress report, we analyse Process min-ing from a sociotechnical perspective. We use work systems theory to conceptualise how Process Mining can be used to improve work conditions through the alignment of Processes and employees within a work system
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A Process Mining Software Comparison
2020Co-Authors: Viner Daniel, Stierle Matthias, Matzner MartinAbstract:www.ProcessMining-software.com is a dedicatedwebsite for Process Mining software comparison and was de-veloped to give practitioners and researchers an overview ofcommercial software available on the market. Based on literaturereview and experimental software testing, a set of criteria wasdeveloped in order to assess the tools’ functional capabilitiesin an objective manner. With our publicly accessible website,we intend to increase the transparency of software functionality.Being an academic endeavour, the non-commercial nature of thestudy ensures a less biased assessment as compared with reportsfrom analyst firms
