XES, XESame, and ProM 6

Verbeek, H. M. W.; Buijs, Jcam Joos; Dongen, Boudewijn F. van; Aalst, Wil M. P. van der

doi:10.1007/978-3-642-17722-4_5

Cited by 198 publications

(140 citation statements)

References 12 publications

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“…The starting point for process mining is an event log. XES (eXtensible Event Stream) [30,31] has been developed as the standard for storing, exchanging and analyzing event logs.…”

Section: Process Mining and Xesmentioning

confidence: 99%

Conformance checking based on multi-perspective declarative process models

Burattin

Maggi

Sperduti

2016

Expert Systems with Applications

139

112

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Process mining is a family of techniques that aim at analyzing business process execution data recorded in event logs. Conformance checking is a branch of this discipline embracing approaches for verifying whether the behavior of a process, as recorded in a log, is in line with some expected behaviors provided in the form of a process model. The majority of these approaches require the input process model to be procedural (e.g., a Petri net). However, in turbulent environments, characterized by high variability, the process behavior is less stable and predictable. In these environments, procedural process models are less suitable to describe a business process. Declarative specifications, working in an open world assumption, allow the modeler to express several possible execution paths as a compact set of constraints. Any process execution that does not contradict these constraints is allowed. One of the open challenges in the context of conformance checking with declarative models is the capability of supporting multi-perspective specifications. In this paper, we close this gap by providing a framework for conformance checking based on MP-Declare, a multi-perspective version of the declarative process modeling language Declare. The approach has been implemented in the process mining tool ProM and has been experimented in three real life case studies.

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“…The starting point for process mining is an event log. XES (eXtensible Event Stream) [30,31] has been developed as the standard for storing, exchanging and analyzing event logs.…”

Section: Process Mining and Xesmentioning

confidence: 99%

Conformance checking based on multi-perspective declarative process models

Burattin

Maggi

Sperduti

2016

Expert Systems with Applications

139

112

View full text Add to dashboard Cite

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“…For performing the case study we implemented the correlation algorithm described in Section 3 in a plug-in 7 for ProM 6 8 [23]. We will further on refer to the plug-in as correlation tool.…”

Section: Methodsmentioning

confidence: 99%

Mining Inter-organizational Business Process Models from EDI Messages: A Case Study from the Automotive Sector

Engel

Aalst

Zapletal

et al. 2012

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

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Abstract. Traditional standards for Electronic Data Interchange (EDI), such as EDIFACT and ANSI X12, have been employed in Business-toBusiness (B2B) e-commerce for decades. Due to their wide industry coverage and long-standing establishment, they will presumably continue to play an important role for some time. EDI systems are typically not "process-aware", i.e., messages are standardized but processes simply "emerge". However, to improve performance and to enhance the control, it is important to understand and analyze the "real" processes supported by these systems. In the case study presented in this paper we uncover the inter-organizational business processes of an automotive supplier company by analyzing the EDIFACT messages that it receives from its business partners. We start by transforming a set of observed messages to an event log, which requires that the individual messages are correlated to process instances. Thereby, we make use of the specific structure of EDIFACT messages. Then we apply process mining techniques to uncover the inter-organizational business processes. Our results show that inter-organizational business process models can be derived by analyzing EDI messages that are exchanged in a network of organizations.

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“…This is often done in an ad-hoc manner. Tools such as XESame [49] and ProMimport [34] provide some support, but still the event logs need to be constructed by querying the database and converting database records (row in tables) into events. Moreover, the "regular tables" in a database only provide the current state of the information system.…”

Section: Guidelines For Loggingmentioning

confidence: 99%

Extracting Event Data from Databases to Unleash Process Mining

Aalst

2015

Management for Professionals

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Abstract. Increasingly organizations are using process mining to understand the way that operational processes are executed. Process mining can be used to systematically drive innovation in a digitalized world. Next to the automated discovery of the real underlying process, there are process-mining techniques to analyze bottlenecks, to uncover hidden inefficiencies, to check compliance, to explain deviations, to predict performance, and to guide users towards "better" processes. Dozens (if not hundreds) of process-mining techniques are available and their value has been proven in many case studies. However, process mining stands or falls with the availability of event logs. Existing techniques assume that events are clearly defined and refer to precisely one case (i.e. process instance) and one activity (i.e., step in the process). Although there are systems that directly generate such event logs (e.g., BPM/WFM systems), most information systems do not record events explicitly. Cases and activities only exist implicitly. However, when creating or using process models "raw data" need to be linked to cases and activities. This paper uses a novel perspective to conceptualize a database view on event data. Starting from a class model and corresponding object models it is shown that events correspond to the creation, deletion, or modification of objects and relations. The key idea is that events leave footprints by changing the underlying database. Based on this an approach is described that scopes, binds, and classifies data to create "flat" event logs that can be analyzed using traditional process-mining techniques.

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XES, XESame, and ProM 6

Cited by 198 publications

References 12 publications

Conformance checking based on multi-perspective declarative process models

Conformance checking based on multi-perspective declarative process models

Mining Inter-organizational Business Process Models from EDI Messages: A Case Study from the Automotive Sector

Extracting Event Data from Databases to Unleash Process Mining

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