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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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Improving the Performance of Process Discovery Algorithms by Instance Selection
Computer Science and Information Systems, 2020Co-Authors: Mohammadreza Fani Sani, S. J. Van Zelst, Wil M. P. Van Der AalstAbstract:Process Discovery algorithms automatically discover Process models based on event data that is captured during the execution of business Processes. These algorithms tend to use all of the event data to discover a Process model. When dealing with large event logs, it is no longer feasible using standard hardware in limited time. A straightforward approach to overcome this problem is to down-size the event data by means of sampling. However, little research has been conducted on selecting the right sample, given the available time and characteristics of event data. This paper evaluates various subset selection methods and evaluates their performance on real event data. The proposed methods have been implemented in both the ProM and the RapidProM platforms. Our experiments show that it is possible to considerably speed up Discovery using instance selection strategies. Furthermore, results show that applying biased selection of the Process instances compared to random sampling will result in simpler Process models with higher quality. Keywords: Process mining, Process Discovery, subset selection, event log preProcessing, performance enhancement
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Business Process Management Workshops - Evaluating the Effectiveness of Interactive Process Discovery in Healthcare: A Case Study
Business Process Management Workshops, 2019Co-Authors: Elisabetta Benevento, Mohammadreza Fani Sani, Prabhakar M. Dixit, Davide Aloini, Wil M. P. Van Der AalstAbstract:This work aims at investigating the effectiveness and suitability of Interactive Process Discovery, an innovative Process Mining technique, to model healthcare Processes in a data-driven manner. Interactive Process Discovery allows the analyst to interactively discover the Process model, exploiting his domain knowledge along with the event log. In so doing, a comparative evaluation against the traditional automated Discovery techniques is carried out to assess the potential benefits that domain knowledge brings in improving both the quality and the understandability of the Process model. The comparison is performed by using a real dataset from an Italian Hospital, in collaboration with the medical staff. Preliminary results show that Interactive Process Discovery allows to obtain an accurate and fully compliant with clinical guidelines Process model with respect to the automated Discovery techniques. Discovering an accurate and comprehensible Process model is an important starting point for subsequent Process analysis and improvement steps, especially in complex environments, such as healthcare.
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guided Process Discovery a pattern based approach
Information Systems, 2018Co-Authors: Felix Mannhardt, Wil M. P. Van Der Aalst, Massimiliano De Leoni, Hajo A. Reijers, Pieter J ToussaintAbstract:Abstract Process mining techniques analyze Processes based on events stored in event logs. Yet, low-level events recorded by information systems may not directly match high-level activities that make sense to Process stakeholders. This results in discovered Process models that cannot be easily understood. To prevent such situations from happening, low-level events need to be translated into high-level activities that are recognizable by stakeholders. This paper proposes the Guided Process Discovery method (GPD). Low-level events are grouped based on behavioral activity patterns, which capture domain knowledge on the relation between high-level activities and low-level events. Events in the resulting abstracted event log correspond to instantiations of high-level activities. We validate Process models discovered on the abstracted event log by checking conformance between the low-level event log and an expanded model in which the high-level activities are replaced by activity patterns. The method was tested using two real-life event logs. We show that the Process models discovered with the GPD method are more comprehensible and can be used to answer Process questions, whereas Process models discovered using standard Process Discovery techniques do not provide the insights needed.
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An Integrated Framework for Process Discovery Algorithm Evaluation.
arXiv: Software Engineering, 2018Co-Authors: Toon Jouck, Alfredo Bolt, Benoît Depaire, Massimiliano De Leoni, Wil M. P. Van Der AalstAbstract:Process mining offers techniques to exploit event data by providing insights and recommendations to improve business Processes. The growing amount of algorithms for Process Discovery has raised the question of which algorithms perform best on a given event log. Current evaluation frameworks for empirically evaluating Discovery techniques depend on the notation used (behavioral identical models may give different results) and cannot provide more general statements about populations of models. Therefore, this paper proposes a new integrated evaluation framework that uses a classification approach to make it modeling notation independent. Furthermore, it is founded on experimental design to ensure the generalization of results. It supports two main evaluation objectives: benchmarking Process Discovery algorithms and sensitivity analysis, i.e. studying the effect of model and log characteristics on a Discovery algorithm's accuracy. The framework is designed as a scientific workflow which enables automated, extendable and shareable evaluation experiments. An extensive experiment including four Discovery algorithms and six control-flow characteristics validates the relevance and flexibility of the framework. Ultimately, the paper aims to advance the state-of-the-art for evaluating Process Discovery techniques.
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SIMPDA - DB-XES: Enabling Process Discovery in the Large
Lecture Notes in Business Information Processing, 2018Co-Authors: Alifah Syamsiyah, Boudewijn F. Van Dongen, Wil M. P. Van Der AalstAbstract:Dealing with the abundance of event data is one of the main Process Discovery challenges. Current Process Discovery techniques are able to efficiently handle imported event log files that fit in the computer’s memory. Once data files get bigger, scalability quickly drops since the speed required to access the data becomes a limiting factor. This paper proposes a new technique based on relational database technology as a solution for scalable Process Discovery. A relational database is used both for storing event data (i.e. we move the location of the data) and for pre-Processing the event data (i.e. we move some computations from analysis-time to insertion-time). To this end, we first introduce DB-XES as a database schema which resembles the standard XES structure, we provide a transparent way to access event data stored in DB-XES, and we show how this greatly improves on the memory requirements of the state-of-the-art Process Discovery techniques. Secondly, we show how to move the computation of intermediate data structures to the database engine, to reduce the time required during Process Discovery. The work presented in this paper is implemented in ProM tool, and a range of experiments demonstrates the feasibility of our approach.
Hajo A. Reijers - One of the best experts on this subject based on the ideXlab platform.
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All That Glitters Is Not Gold: Towards Process Discovery Techniques with Guarantees.
arXiv: Software Engineering, 2020Co-Authors: Jan Martijn E. M. Van Der Werf, Artem Polyvyanyy, Bart R. Van Wensveen, Matthieu J. S. Brinkhuis, Hajo A. ReijersAbstract:The aim of a Process Discovery algorithm is to construct from event data a Process model that describes the underlying, real-world Process well. Intuitively, the better the quality of the event data, the better the quality of the model that is discovered. However, existing Process Discovery algorithms do not guarantee this relationship. We demonstrate this by using a range of quality measures for both event data and discovered Process models. This paper is a call to the community of IS engineers to complement their Process Discovery algorithms with properties that relate qualities of their inputs to those of their outputs. To this end, we distinguish four incremental stages for the development of such algorithms, along with concrete guidelines for the formulation of relevant properties and experimental validation. We will also use these stages to reflect on the state of the art, which shows the need to move forward in our thinking about algorithmic Process Discovery.
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guided Process Discovery a pattern based approach
Information Systems, 2018Co-Authors: Felix Mannhardt, Wil M. P. Van Der Aalst, Massimiliano De Leoni, Hajo A. Reijers, Pieter J ToussaintAbstract:Abstract Process mining techniques analyze Processes based on events stored in event logs. Yet, low-level events recorded by information systems may not directly match high-level activities that make sense to Process stakeholders. This results in discovered Process models that cannot be easily understood. To prevent such situations from happening, low-level events need to be translated into high-level activities that are recognizable by stakeholders. This paper proposes the Guided Process Discovery method (GPD). Low-level events are grouped based on behavioral activity patterns, which capture domain knowledge on the relation between high-level activities and low-level events. Events in the resulting abstracted event log correspond to instantiations of high-level activities. We validate Process models discovered on the abstracted event log by checking conformance between the low-level event log and an expanded model in which the high-level activities are replaced by activity patterns. The method was tested using two real-life event logs. We show that the Process models discovered with the GPD method are more comprehensible and can be used to answer Process questions, whereas Process models discovered using standard Process Discovery techniques do not provide the insights needed.
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data driven Process Discovery revealing conditional infrequent behavior from event logs
Conference on Advanced Information Systems Engineering, 2017Co-Authors: Felix Mannhardt, Hajo A. Reijers, Massimiliano De Leoni, Wil M. P. Van Der AalstAbstract:Process Discovery methods automatically infer Process models from event logs. Often, event logs contain so-called noise, e.g., infrequent outliers or recording errors, which obscure the main behavior of the Process. Existing methods filter this noise based on the frequency of event labels: infrequent paths and activities are excluded. However, infrequent behavior may reveal important insights into the Process. Thus, not all infrequent behavior should be considered as noise. This paper proposes the Data-aware Heuristic Miner (DHM), a Process Discovery method that uses the data attributes to distinguish infrequent paths from random noise by using classification techniques. Data- and control-flow of the Process are discovered together. We show that the DHM is, to some degree, robust against random noise and reveals data-driven decisions, which are filtered by other Discovery methods. The DHM has been successfully tested on several real-life event logs, two of which we present in this paper.
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CAiSE - Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs
Advanced Information Systems Engineering, 2017Co-Authors: Felix Mannhardt, Hajo A. Reijers, Massimiliano De Leoni, Wmp Wil Van Der AalstAbstract:Process Discovery methods automatically infer Process models from event logs. Often, event logs contain so-called noise, e.g., infrequent outliers or recording errors, which obscure the main behavior of the Process. Existing methods filter this noise based on the frequency of event labels: infrequent paths and activities are excluded. However, infrequent behavior may reveal important insights into the Process. Thus, not all infrequent behavior should be considered as noise. This paper proposes the Data-aware Heuristic Miner (DHM), a Process Discovery method that uses the data attributes to distinguish infrequent paths from random noise by using classification techniques. Data- and control-flow of the Process are discovered together. We show that the DHM is, to some degree, robust against random noise and reveals data-driven decisions, which are filtered by other Discovery methods. The DHM has been successfully tested on several real-life event logs, two of which we present in this paper.
Josep Carmona - One of the best experts on this subject based on the ideXlab platform.
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Incorporating negative information to Process Discovery of complex systems
Information Sciences, 2018Co-Authors: Hernán Ponce De León, Josep Carmona, Lucio Nardelli, Seppe Vanden BrouckeAbstract:The Discovery of a formal Process model from event logs describing real Process executions is a challenging problem that has been studied from several angles. Most of the contributions consider the extraction of a model as a one-class supervised learning problem where only a set of Process instances is available. Moreover, the majority of techniques cannot generate complex models, a crucial feature in some areas like manufacturing. In this paper we present a fresh look at Process Discovery where undesired Process behaviors can also be taken into account. This feature may be crucial for deriving Process models which are less complex, fitting and precise, but also good on generalizing the right behavior underlying an event log. The technique is based on the theory of convex polyhedra and satisfiability modulo theory (SMT) and can be combined with other Process Discovery approach as a post Processing step to further simplify complex models. We show in detail how to apply the proposed technique in combination with a recent method that uses numerical abstract domains. Experiments performed in a new prototype implementation show the effectiveness of the technique and the ability to be combined with other Discovery techniques.
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Encoding Process Discovery problems in SMT
Software & Systems Modeling, 2016Co-Authors: Marc Solé, Josep CarmonaAbstract:Information systems, which are responsible for driving many Processes in our lives (health care, the web, municipalities, commerce and business, among others), store information in the form of logs which is often left unused. Process mining, a discipline in between data mining and software engineering, proposes tailored algorithms to exploit the information stored in a log, in order to reason about the Processes underlying an information system. A key challenge in Process mining is Discovery: Given a log, derive a formal Process model that can be used afterward for a formal analysis. In this paper, we provide a general approach based on satisfiability modulo theories (SMT) as a solution for this challenging problem. By encoding the problem into the logical/arithmetic domains and using modern SMT engines, it is shown how two separate families of Process models can be discovered. The theory of this paper is accompanied with a tool, and experimental results witness the significance of this novel view of the Process Discovery problem.
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ATVA - Unfolding-Based Process Discovery
Automated Technology for Verification and Analysis, 2015Co-Authors: Hernán Ponce De León, Josep Carmona, César Rodríguez, Keijo Heljanko, Stefan HaarAbstract:This paper presents a novel technique for Process Discovery. In contrast to the current trend, which only considers an event log for discovering a Process model, we assume two additional inputs: an independence relation on the set of logged activities, and a collection of negative traces. After deriving an intermediate net unfolding from them, we perform a controlled folding giving rise to a Petri net which contains both the input log and all independence-equivalent traces arising from it. Remarkably, the derived Petri net cannot execute any trace from the negative collection. The entire chain of transformations is fully automated. A tool has been developed and experimental results are provided that witness the significance of the contribution of this paper.
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BPM - Incorporating Negative Information in Process Discovery
Lecture Notes in Computer Science, 2015Co-Authors: Hernán Ponce-de-león, Josep Carmona, Seppe Vanden BrouckeAbstract:The Discovery of a formal Process model from event logs describing real Process executions is a challenging problem that has been studied from several angles. Most of the contributions consider the extraction of a model as a semi-supervised problem where only positive information is available. In this paper we present a fresh look at Process Discovery where also negative information can be taken into account. This feature may be crucial for deriving Process models which are not only simple, fitting and precise, but also good on generalizing the right behavior underlying an event log. The technique is based on numerical abstract domains and Satisfiability Modulo Theories SMT, and can be combined with any Process Discovery technique. As an example, we show in detail how to supervise a recent technique that uses numerical abstract domains. Experiments performed in our prototype implementation show the effectiveness of the techniques and the ability to improve the results produced by selected Discovery techniques.
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Unfolding-Based Process Discovery
arXiv: Logic in Computer Science, 2015Co-Authors: Hernán Ponce-de-león, Josep Carmona, César Rodríguez, Keijo Heljanko, Stefan HaarAbstract:This paper presents a novel technique for Process Discovery. In contrast to the current trend, which only considers an event log for discovering a Process model, we assume two additional inputs: an independence relation on the set of logged activities, and a collection of negative traces. After deriving an intermediate net unfolding from them, we perform a controlled folding giving rise to a Petri net which contains both the input log and all independence-equivalent traces arising from it. Remarkably, the derived Petri net cannot execute any trace from the negative collection. The entire chain of transformations is fully automated. A tool has been developed and experimental results are provided that witness the significance of the contribution of this paper.
Jochen De Weerdt - One of the best experts on this subject based on the ideXlab platform.
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Fodina: A robust and flexible heuristic Process Discovery technique
Decision Support Systems, 2017Co-Authors: Seppe Vanden Broucke, Jochen De WeerdtAbstract:Abstract In this paper, we present Fodina, a Process Discovery technique with a strong focus on robustness and flexibility. To do so, we improve upon and extend an existing Process Discovery algorithm, namely Heuristics Miner. We have identified several drawbacks which impact the reliability of existing heuristic-based Process Discovery techniques and therefore propose a new algorithm which is shown to be better performing in terms of Process model quality, adds the ability to mine duplicate tasks, and allows for flexible configuration options.
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BPM - A Stability Assessment Framework for Process Discovery Techniques
Lecture Notes in Computer Science, 2016Co-Authors: Pieter De Koninck, Jochen De WeerdtAbstract:An extensive amount of work has addressed the evaluation of Process Discovery techniques and the Process models they discover based on concepts like fitness, precision, generalization and simplicity. In this paper, we claim that stability could be considered as an important supplementary evaluation dimension for Process Discovery next to accuracy and comprehensibility, with ties to the generalization concept. As such, our core contribution is a new framework to measure stability of Process Discovery techniques. In this paper, the design choices of the different components of the framework are explained. Furthermore, using an experimental evaluation involving both artificial and real-life event logs, the appropriateness and relevance of the stability assessment framework is demonstrated.
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Active Trace Clustering for Improved Process Discovery
IEEE Transactions on Knowledge and Data Engineering, 2013Co-Authors: Jochen De Weerdt, Seppe Vanden Broucke, Jan Vanthienen, Bart BaesensAbstract:Process Discovery is the learning task that entails the construction of Process models from event logs of information systems. Typically, these event logs are large data sets that contain the Process executions by registering what activity has taken place at a certain moment in time. By far the most arduous challenge for Process Discovery algorithms consists of tackling the problem of accurate and comprehensible knowledge Discovery from highly flexible environments. Event logs from such flexible systems often contain a large variety of Process executions which makes the application of Process mining most interesting. However, simply applying existing Process Discovery techniques will often yield highly incomprehensible Process models because of their inaccuracy and complexity. With respect to resolving this problem, trace clustering is one very interesting approach since it allows to split up an existing event log so as to facilitate the knowledge Discovery Process. In this paper, we propose a novel trace clustering technique that significantly differs from previous approaches. Above all, it starts from the observation that currently available techniques suffer from a large divergence between the clustering bias and the evaluation bias. By employing an active learning inspired approach, this bias divergence is solved. In an assessment using four complex, real-life event logs, it is shown that our technique significantly outperforms currently available trace clustering techniques.
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a multi dimensional quality assessment of state of the art Process Discovery algorithms using real life event logs
Information Systems, 2012Co-Authors: Jochen De Weerdt, Jan Vanthienen, Manu De Backer, Bart BaesensAbstract:Process mining is the research domain that is dedicated to the a posteriori analysis of business Process executions. The techniques developed within this research area are specifically designed to provide profound insight by exploiting the untapped reservoir of knowledge that resides within event logs of information systems. Process Discovery is one specific subdomain of Process mining that entails the Discovery of control-flow models from such event logs. Assessing the quality of discovered Process models is an essential element, both for conducting Process mining research as well as for the use of Process mining in practice. In this paper, a multi-dimensional quality assessment is presented in order to comprehensively evaluate Process Discovery techniques. In contrast to previous studies, the major contribution of this paper is the use of eight real-life event logs. For instance, we show that evaluation based on real-life event logs significantly differs from the traditional approach to assess Process Discovery techniques using artificial event logs. In addition, we provide an extensive overview of available Process Discovery techniques and we describe how discovered Process models can be assessed regarding both accuracy and comprehensibility. The results of our study indicate that the HeuristicsMiner algorithm is especially suited in a real-life setting. However, it is also shown that, particularly for highly complex event logs, knowledge Discovery from such data sets can become a major problem for traditional Process Discovery techniques.
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Process Discovery in event logs: An application in the telecom industry
Applied Soft Computing, 2011Co-Authors: Stijn Goedertier, Jochen De Weerdt, David Martens, Jan Vanthienen, Bart BaesensAbstract:The abundant availability of data is typical for information-intensive organizations. Usually, discerning knowledge from vast amounts of data is a challenge. Similarly, discovering business Process models from information system event logs is definitely non-trivial. Within the analysis of event logs, Process Discovery, which can be defined as the automated construction of structured Process models from such event logs, is an important learning task. However, the Discovery of these Processes poses many challenges. First of all, human-centric Processes are likely to contain a lot of noise as people deviate from standard procedures. Other challenges are the Discovery of so-called non-local, non-free choice constructs, duplicate activities, incomplete event logs and the inclusion of prior knowledge. In this paper, we present an empirical evaluation of three state-of-the-art Process Discovery techniques: Genetic Miner, AGNEs and HeuristicsMiner. Although the detailed empirical evaluation is the main contribution of this paper to the literature, an in-depth discussion of a number of different evaluation metrics for Process Discovery techniques and a thorough discussion of the validity issue are key contributions as well.
Bart Baesens - One of the best experts on this subject based on the ideXlab platform.
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IEEE Congress on Evolutionary Computation - Declarative Process Discovery with evolutionary computing
2014 IEEE Congress on Evolutionary Computation (CEC), 2014Co-Authors: Seppe Vanden Broucke, Jan Vanthienen, Bart BaesensAbstract:The field of Process mining deals with the extraction of knowledge from event logs. One task within the area of Process mining entails the Discovery of Process models to represent real-life behavior as observed in day-to-day business activities. A large number of such Process Discovery algorithms have been proposed during the course of the past decade, among which techniques to mine declarative Process models (e.g. Declare and AGNEs Miner) as well as evolutionary based techniques (e.g. Genetic Miner and Process Tree Miner). In this paper, we present the initial results of a newly proposed evolutionary based Process Discovery algorithm which aims to discover declarative Process models, hence combining these two classes (declarative and genetic) of Discovery techniques. To do so, we herein use a language bias similar to the one found in AGNEs Miner to allow for the conversion from a set of declarative control-flow based constraints (determining the conditions which have to be satisfied to enable to execution of an activity) to a procedural Process model, i.e. a Petri net, though this language bias can be extended to include data-based constraints as well.
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Active Trace Clustering for Improved Process Discovery
IEEE Transactions on Knowledge and Data Engineering, 2013Co-Authors: Jochen De Weerdt, Seppe Vanden Broucke, Jan Vanthienen, Bart BaesensAbstract:Process Discovery is the learning task that entails the construction of Process models from event logs of information systems. Typically, these event logs are large data sets that contain the Process executions by registering what activity has taken place at a certain moment in time. By far the most arduous challenge for Process Discovery algorithms consists of tackling the problem of accurate and comprehensible knowledge Discovery from highly flexible environments. Event logs from such flexible systems often contain a large variety of Process executions which makes the application of Process mining most interesting. However, simply applying existing Process Discovery techniques will often yield highly incomprehensible Process models because of their inaccuracy and complexity. With respect to resolving this problem, trace clustering is one very interesting approach since it allows to split up an existing event log so as to facilitate the knowledge Discovery Process. In this paper, we propose a novel trace clustering technique that significantly differs from previous approaches. Above all, it starts from the observation that currently available techniques suffer from a large divergence between the clustering bias and the evaluation bias. By employing an active learning inspired approach, this bias divergence is solved. In an assessment using four complex, real-life event logs, it is shown that our technique significantly outperforms currently available trace clustering techniques.
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a multi dimensional quality assessment of state of the art Process Discovery algorithms using real life event logs
Information Systems, 2012Co-Authors: Jochen De Weerdt, Jan Vanthienen, Manu De Backer, Bart BaesensAbstract:Process mining is the research domain that is dedicated to the a posteriori analysis of business Process executions. The techniques developed within this research area are specifically designed to provide profound insight by exploiting the untapped reservoir of knowledge that resides within event logs of information systems. Process Discovery is one specific subdomain of Process mining that entails the Discovery of control-flow models from such event logs. Assessing the quality of discovered Process models is an essential element, both for conducting Process mining research as well as for the use of Process mining in practice. In this paper, a multi-dimensional quality assessment is presented in order to comprehensively evaluate Process Discovery techniques. In contrast to previous studies, the major contribution of this paper is the use of eight real-life event logs. For instance, we show that evaluation based on real-life event logs significantly differs from the traditional approach to assess Process Discovery techniques using artificial event logs. In addition, we provide an extensive overview of available Process Discovery techniques and we describe how discovered Process models can be assessed regarding both accuracy and comprehensibility. The results of our study indicate that the HeuristicsMiner algorithm is especially suited in a real-life setting. However, it is also shown that, particularly for highly complex event logs, knowledge Discovery from such data sets can become a major problem for traditional Process Discovery techniques.
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Process Discovery in event logs: An application in the telecom industry
Applied Soft Computing, 2011Co-Authors: Stijn Goedertier, Jochen De Weerdt, David Martens, Jan Vanthienen, Bart BaesensAbstract:The abundant availability of data is typical for information-intensive organizations. Usually, discerning knowledge from vast amounts of data is a challenge. Similarly, discovering business Process models from information system event logs is definitely non-trivial. Within the analysis of event logs, Process Discovery, which can be defined as the automated construction of structured Process models from such event logs, is an important learning task. However, the Discovery of these Processes poses many challenges. First of all, human-centric Processes are likely to contain a lot of noise as people deviate from standard procedures. Other challenges are the Discovery of so-called non-local, non-free choice constructs, duplicate activities, incomplete event logs and the inclusion of prior knowledge. In this paper, we present an empirical evaluation of three state-of-the-art Process Discovery techniques: Genetic Miner, AGNEs and HeuristicsMiner. Although the detailed empirical evaluation is the main contribution of this paper to the literature, an in-depth discussion of a number of different evaluation metrics for Process Discovery techniques and a thorough discussion of the validity issue are key contributions as well.
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Robust Process Discovery with Artificial Negative Events
Journal of Machine Learning Research, 2009Co-Authors: Stijn Goedertier, David Martens, Jan Vanthienen, Bart BaesensAbstract: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.