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

Aalst, Wil M. P. van der

doi:10.1007/978-3-319-02922-1_1

Cited by 67 publications

(39 citation statements)

References 57 publications

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“…A logical next step is to develop tool support for specific database management systems. Moreover, we would like to relate this to our work on process cubes [3] for comparative process mining.…”

Section: Resultsmentioning

confidence: 99%

“…Hence, we can create a conventional event log with a rich set of attributes. However, as process-mining techniques mature it becomes interesting to compare different groups of process instances [3]. Instead of creating one event log, it is often insightful to create multiple event logs.…”

Section: Classify: Relate Process Instances To Processesmentioning

confidence: 99%

“…Note that class ⊆ PI × CL does not require a strict partitioning of the process instances, e.g., a case may belong to multiple classes. In [3], the notion of process cubes was proposed to allow for comparative process mining. In a process cube events are organized using different dimensions.…”

Section: Classify: Relate Process Instances To Processesmentioning

confidence: 99%

“…Also Nezhad et al [40] developed techniques for event correlation and process discovery from web service interaction logs. Most closely related seem to be the work on artifact-centric process mining [12,30,31], process model repositories [46], event log extraction ( [49,34]), and process cubes [3]. However, none of these approaches define an event model on top of a class model.…”

Section: Related Workmentioning

confidence: 99%

“…Just like computer science emerged as a new discipline from mathematics when computers became abundantly available, we now see the birth of data science as a new discipline driven by the torrents of data available in our increasingly digitalized world. 3 The demand for data scientists is rapidly increasing. However, the focus on data analysis should not obscure process-orientation.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Classify: Relate Process Instances To Processesmentioning

confidence: 99%

Section: Classify: Relate Process Instances To Processesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Extracting Event Data from Databases to Unleash Process Mining

Aalst

2015

Management for Professionals

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Extraction, correlation, and abstraction of event data for process mining

Diba

Batoulis

Weidlich

et al. 2019

WIREs Data Min & Knowl

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Process mining provides a rich set of techniques to discover valuable knowledge of business processes based on data that was recorded in different types of information systems. It enables analysis of end-to-end processes to facilitate process reengineering and process improvement. Process mining techniques rely on the availability of data in the form of event logs. In order to enable process mining in diverse environments, the recorded data need to be located and transformed to event logs. The journey from raw data to event logs suitable for process mining can be addressed by a variety of methods and techniques, which are the focus of this article. In particular, techniques proposed in the literature to support the creation of event logs from raw data are reviewed and classified. This includes techniques for identification and extraction of the required event data from diverse sources as well as their correlation and abstraction.

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