Distributed Process Discovery and Conformance Checking

Aalst, Wil M. P. van der

doi:10.1007/978-3-642-28872-2_1

Cited by 29 publications

(27 citation statements)

References 29 publications

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“…The process cube notion is closely related to divide-and-conquer approaches in process mining where huge event logs are partitioned into smaller sublogs to improve performance and scalability. In principle, process cubes can also be used to decompose challenging process mining problems into smaller problems using the techniques described in [3,5,4]. These techniques may be used to speed-up OLAP operations.…”

Section: Process Mining Data-oriented Analysismentioning

confidence: 99%

“…For example, the two types of merging and splitting process cells illustrated by Figure 5 are process-specific and do not correspond to existing OLAP notions. Techniques for merging and splitting process cells are related to divide-andconquer approaches for process mining (e.g., to distribute process discovery or conformance checking) [3].…”

Section: Process Cubesmentioning

confidence: 99%

“…Process cubes partition event data and therefore may enable divide-andconquer approaches that decompose the event log based on splitting/merging cells horizontally/vertically [3]. This was already illustrated using Figure 5.…”

Section: Distributed Process Miningmentioning

confidence: 99%

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Process Cubes: Slicing, Dicing, Rolling Up and Drilling Down Event Data for Process Mining

Aalst

2013

Lecture Notes in Business Information Processing

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Abstract. Recent breakthroughs in process mining research make it possible to discover, analyze, and improve business processes based on event data. The growth of event data provides many opportunities but also imposes new challenges. Process mining is typically done for an isolated well-defined process in steady-state. However, the boundaries of a process may be fluid and there is a need to continuously view event data from different angles. This paper proposes the notion of process cubes where events and process models are organized using different dimensions. Each cell in the process cube corresponds to a set of events and can be used to discover a process model, to check conformance with respect to some process model, or to discover bottlenecks. The idea is related to the well-known OLAP (Online Analytical Processing) data cubes and associated operations such as slice, dice, roll-up, and drill-down. However, there are also significant differences because of the process-related nature of event data. For example, process discovery based on events is incomparable to computing the average or sum over a set of numerical values. Moreover, dimensions related to process instances (e.g. cases are split into gold and silver customers), subprocesses (e.g. acquisition versus delivery), organizational entities (e.g. backoffice versus frontoffice), and time (e.g., 2010, 2011, 2012, and 2013) are semantically different and it is challenging to slice, dice, roll-up, and drill-down process mining results efficiently.

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Section: Process Mining Data-oriented Analysismentioning

confidence: 99%

Section: Process Cubesmentioning

confidence: 99%

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Process Cubes: Slicing, Dicing, Rolling Up and Drilling Down Event Data for Process Mining

Aalst

2013

Lecture Notes in Business Information Processing

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“…Traditional process mining approaches however have difficulties coping with this sheer amount of data (i.e. the number of events), as most interesting algorithms are linear in the size of the event log and exponential in the number of different activities [3]. In order to provide a solution to this problem, techniques for decomposed process mining [3][4][5] have been proposed.…”

Section: Introductionmentioning

confidence: 99%

“…the number of events), as most interesting algorithms are linear in the size of the event log and exponential in the number of different activities [3]. In order to provide a solution to this problem, techniques for decomposed process mining [3][4][5] have been proposed. Decomposed process mining aims to decompose the process mining problem at hand into smaller problems that can be handled by existing process discovery and conformance checking techniques.…”

Section: Introductionmentioning

confidence: 99%

Finding Suitable Activity Clusters for Decomposed Process Discovery

Hompes

Verbeek

Aalst

2015

Lecture Notes in Business Information Processing

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Abstract. Event data can be found in any information system and provide the starting point for a range of process mining techniques. The widespread availability of large amounts of event data also creates new challenges. Existing process mining techniques are often unable to handle "big event data" adequately. Decomposed process mining aims to solve this problem by decomposing the process mining problem into many smaller problems which can be solved in less time, using less resources, or even in parallel. Many decomposed process mining techniques have been proposed in literature. Analysis shows that even though the decomposition step takes a relatively small amount of time, it is of key importance in finding a high-quality process model and for the computation time required to discover the individual parts. Currently there is no way to assess the quality of a decomposition beforehand. We define three quality notions that can be used to assess a decomposition, before using it to discover a model or check conformance with. We then propose a decomposition approach that uses these notions and is able to find a high-quality decomposition in little time.

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Efficient Declarative-Based Process Mining Using an Enhanced Framework

Ferilli

Angelastro

2020

Complex Pattern Mining

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Distributed Process Discovery and Conformance Checking

Cited by 29 publications

References 29 publications

Process Cubes: Slicing, Dicing, Rolling Up and Drilling Down Event Data for Process Mining

Process Cubes: Slicing, Dicing, Rolling Up and Drilling Down Event Data for Process Mining

Finding Suitable Activity Clusters for Decomposed Process Discovery

Efficient Declarative-Based Process Mining Using an Enhanced Framework

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