Discovering workflow nets using integer linear programming
TL;DR: An ILP-based process discovery approach, based on region theory, that guarantees to discover relaxed sound workflow nets and devise a filtering algorithm that is able to cope with the presence of infrequent, exceptional behaviour.
Abstract: Process mining is concerned with the analysis, understanding and improvement of business processes. Process discovery, i.e. discovering a process model based on an event log, is considered the most challenging process mining task. State-of-the-art process discovery algorithms only discover local control flow patterns and are unable to discover complex, non-local patterns. Region theory based techniques, i.e. an established class of process discovery techniques, do allow for discovering such patterns. However, applying region theory directly results in complex, overfitting models, which is less desirable. Moreover, region theory does not cope with guarantees provided by state-of-the-art process discovery algorithms, both w.r.t. structural and behavioural properties of the discovered process models. In this paper we present an ILP-based process discovery approach, based on region theory, that guarantees to discover relaxed sound workflow nets. Moreover, we devise a filtering algorithm, based on the internal working of the ILP-formulation, that is able to cope with the presence of infrequent, exceptional behaviour. We have extensively evaluated the technique using different event logs with different levels of exceptional behaviour. Our experiments show that the presented approach allows us to leverage the inherent shortcomings of existing region-based approaches. The techniques presented are implemented and readily available in the HybridILPMiner package in the open-source process mining tool-kits ProM (
http://promtools.org
) and RapidProM (
http://rapidprom.org
).
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TL;DR: The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.
Abstract: Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy, and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures, and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering 12 publicly-available real-life event logs, 12 proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.
225 citations
Cites background or methods from "Discovering workflow nets using int..."
...[75]), while [26], [90] were tested on artificial logs and [51] on synthetic logs only....
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...[26], [104], [105] propose an improvement of the ILP miner implemented in [25]....
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...ILP Miner [25] Hybrid ILP Miner [26]...
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Posted Content•
TL;DR: In this paper, a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering twelve publicly-available real-life event logs, twelve proprietary real life event logs and nine quality metrics, is presented.
Abstract: Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering twelve publicly-available real-life event logs, twelve proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.
127 citations
01 Jan 2011
TL;DR: The importance of maintaining a proper alignment between event log and process model is elaborated on and their application to conformance checking and performance analysis is elaborated.
Abstract: Process mining techniques use event data to discover process models, to check the conformance of prede?ned process models, and to extend such models with information about bottlenecks, decisions, and resource usage. These techniques are driven by observed events rather than hand-made models. Event logs are used to learn and enrich process models. By replaying history on the model, it is possible to establish a precise relationship between events and model elements. This relationship can be used to check conformance and to analyze performance. For example, it is possible to diagnose deviations from the modeled behavior. The severity of each deviation can be quanti?ed. Moreover, the relationship established during replay and the timestamps in the event log can be combined to show bottlenecks. These examples illustrate the importance of maintaining a proper alignment between event log and process model. Therefore, we elaborate on the realization of such alignments and their application to conformance checking and performance analysis.
95 citations
01 Jan 2018
TL;DR: A novel general purpose filtering method that exploits observed conditional probabilities between sequences of activities and accurately removes irrelevant behaviour and, indeed, improves process discovery results.
Abstract: Process discovery, one of the key challenges in process mining, aims at discovering process models from process execution data stored in event logs. Most discovery algorithms assume that all data in an event log conform to correct execution of the process, and hence, incorporate all behaviour in their resulting process model. However, in real event logs, noise and irrelevant infrequent behaviour are often present. Incorporating such behaviour results in complex, incomprehensible process models concealing the correct and/or relevant behaviour of the underlying process. In this paper, we propose a novel general purpose filtering method that exploits observed conditional probabilities between sequences of activities. The method has been implemented in both the ProM toolkit and the RapidProM framework. We evaluate our approach using real and synthetic event data. The results show that the proposed method accurately removes irrelevant behaviour and, indeed, improves process discovery results.
61 citations
14 Mar 2019
TL;DR: This thesis explores, develop and analyse process mining techniques that are able to handle streaming event data and identifies three main process mining types of analysis, i.e. process discovery, conformance checking and process enhancement.
Abstract: Modern information systems allow us to track, often in great detail, the execution of processes within companies. Consider for example luggage handling in airports, manufacturing processes of products and goods, or processes related to service provision, all of these processes generate traces of valuable event data. Such event data are typically stored in a company’s information system and describe the execution of the process at hand. In recent years, the field of process mining has emerged. Process mining techniques aim to translate the data captured during the process execution, i.e. the event data, into actionable insights. As such, we identify three main process mining types of analysis, i.e. process discovery, conformance checking and process enhancement. In process discovery, we aim to discover a process model, i.e. a formal behavioural description, which describes the process as captured by the event data. In conformance checking, we aim to assess to what degree the event data is in correspondence with a given reference model, i.e. a model describing how the process ought to be executed. Finally, within process enhancement, the main goal is to improve the view of the process, i.e. by enhancing process models on the basis of facts derived from event data. Recent developments in information technology allow us to capture data at increasing rates, yielding enormous volumes of data, both in terms of size and velocity. In the context of process mining, this relates to the advent of real-time, online, streams of events that result in data sets that are no longer efficiently analysable by commodity hardware. Such types of data pose both opportunities and challenges. On the one hand, it allows us to get actionable insights into the process, at the moment it is being executed. On the other hand, conventional process mining techniques do not allow us to gain these insights, as they are not designed to cope with such a new type of data. As a consequence, new methods, techniques and tools are needed to allow us to apply process mining techniques and analyses on streams of event data of arbitrary size. In this thesis, we explore, develop and analyse process mining techniques that are able to handle streaming event data. The premise of streaming event data, is the fact that we assume the stream of events under consideration to be of infinite size. As such, efficient techniques to temporarily store and use relevant recent subsets of event data
60 citations
References
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01 Apr 1989
TL;DR: The author proceeds with introductory modeling examples, behavioral and structural properties, three methods of analysis, subclasses of Petri nets and their analysis, and one section is devoted to marked graphs, the concurrent system model most amenable to analysis.
Abstract: Starts with a brief review of the history and the application areas considered in the literature. The author then proceeds with introductory modeling examples, behavioral and structural properties, three methods of analysis, subclasses of Petri nets and their analysis. In particular, one section is devoted to marked graphs, the concurrent system model most amenable to analysis. Introductory discussions on stochastic nets with their application to performance modeling, and on high-level nets with their application to logic programming, are provided. Also included are recent results on reachability criteria. Suggestions are provided for further reading on many subject areas of Petri nets. >
10,755 citations
"Discovering workflow nets using int..." refers methods in this paper
...In this paper we consider workflow nets (WF-nets) [27], based on Petri nets [22], to describe process models....
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TL;DR: This paper introduces workflow management as an application domain for Petri nets, presents state-of-the-art results with respect to the verification of workflows, and highlights some Petri-net-based workflow tools.
Abstract: Workflow management promises a new solution to an age-old problem: controlling, monitoring, optimizing and supporting business processes. What is new about workflow management is the explicit representation of the business process logic which allows for computerized support. This paper discusses the use of Petri nets in the context of workflow management. Petri nets are an established tool for modeling and analyzing processes. On the one hand, Petri nets can be used as a design language for the specification of complex workflows. On the other hand, Petri net theory provides for powerful analysis techniques which can be used to verify the correctness of workflow procedures. This paper introduces workflow management as an application domain for Petri nets, presents state-of-the-art results with respect to the verification of workflows, and highlights some Petri-net-based workflow tools.
2,862 citations
"Discovering workflow nets using int..." refers background or methods in this paper
...In this paper we consider workflow nets (WF-nets) [27], based on Petri nets [22], to describe process models....
[...]
...Definition 1 (Workflow net [27]) Let N = (P, T, F, λ) be a Petri net....
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...The resultingmodels are hierarchically structured sound workflow nets [27]....
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01 Jul 2003
TL;DR: In this paper, the authors describe a number of workflow patterns addressing what they believe identify comprehensive workflow functionality and provide the basis for an in-depth comparison of commercial workflow management systems.
Abstract: Differences in features supported by the various contemporary commercial workflow management systems point to different insights of suitability and different levels of expressive power. The challenge, which we undertake in this paper, is to systematically address workflow requirements, from basic to complex. Many of the more complex requirements identified, recur quite frequently in the analysis phases of workflow projects, however their implementation is uncertain in current products. Requirements for workflow languages are indicated through workflow patterns. In this context, patterns address business requirements in an imperative workflow style expression, but are removed from specific workflow languages. The paper describes a number of workflow patterns addressing what we believe identify comprehensive workflow functionality. These patterns provide the basis for an in-depth comparison of a number of commercially available workflow management systems. As such, this paper can be seen as the academic response to evaluations made by prestigious consulting companies. Typically, these evaluations hardly consider the workflow modeling language and routing capabilities, and focus more on the purely technical and commercial aspects.
2,553 citations
TL;DR: A new algorithm is presented to extract a process model from a so-called "workflow log" containing information about the workflow process as it is actually being executed and represent it in terms of a Petri net.
Abstract: Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and, typically, there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, we have developed techniques for discovering workflow models. The starting point for such techniques is a so-called "workflow log" containing information about the workflow process as it is actually being executed. We present a new algorithm to extract a process model from such a log and represent it in terms of a Petri net. However, we also demonstrate that it is not possible to discover arbitrary workflow processes. We explore a class of workflow processes that can be discovered. We show that the /spl alpha/-algorithm can successfully mine any workflow represented by a so-called SWF-net.
1,953 citations
"Discovering workflow nets using int..." refers background or methods in this paper
...If exceptional behaviour is present in an event log, the conventional ILP-based process discovery algorithm produces a WF-net that allows for all exceptional behaviour....
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...Because of this, applying ILP-based process discovery as-is on real data often yields, despite its potential, unsatisfactory results....
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...When applying ILP-based process discovery based on event log L ′1 with sequence encoding filtering and κ0....
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...In this sectionwe highlight themain cause of ILP-based discovery’s inability to handle infrequent behaviour and we devise a filtering mechanism that exploits the nature of the underlying body of constraints....
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...Additionally, we presented the sequence encoding filtering technique which enables us to apply filtering exceptional behaviour within the ILP-based process discovery algorithm....
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