Deducing Case IDs for Unlabeled Event Logs

Bayomie, Dina; Helal, Iman M. A.; Awad, Ahmed; Ezat, Ehab; ElBastawissi, Ali

doi:10.1007/978-3-319-42887-1_20

Cited by 20 publications

(13 citation statements)

References 13 publications

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“…When the process involves many different entities, the construction of the view is not easy. While there are some methods to automatically infer a/some case notion(s) from unstructured data [2,15,6], in most cases the specification happens manually. Moreover, the data extractions and transformations may be time-consuming and one quickly loses the overview.…”

Section: Introductionmentioning

confidence: 99%

Extracting Multiple Viewpoint Models from Relational Databases

Berti

Aalst

2020

Lecture Notes in Business Information Processing

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Much time in process mining projects is spent on finding and understanding data sources and extracting the event data needed. As a result, only a fraction of time is spent actually applying techniques to discover, control and predict the business process. Moreover, current process mining techniques assume a single case notion. However, in reallife processes often different case notions are intertwined. For example, events of the same order handling process may refer to customers, orders, order lines, deliveries, and payments. Therefore, we propose to use Multiple Viewpoint (MVP) models that relate events through objects and that relate activities through classes. The required event data are much closer to existing relational databases. MVP models provide a holistic view on the process, but also allow for the extraction of classical event logs using different viewpoints. This way existing process mining techniques can be used for each viewpoint without the need for new data extractions and transformations. We provide a toolchain allowing for the discovery of MVP models (annotated with performance and frequency information) from relational databases. Moreover, we demonstrate that classical process mining techniques can be applied to any selected viewpoint.

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Section: Introductionmentioning

confidence: 99%

Extracting Multiple Viewpoint Models from Relational Databases

Berti

Aalst

2020

Lecture Notes in Business Information Processing

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“…In (Bayomie, Helal, Awad, Ezat, & ElBastawissi, ) the authors correlate events to cases using the names and completion timestamps of the events. Furthermore, they employ the corresponding process model containing activities that have the same name as the events, and heuristic information about the execution duration of the activities in the model.…”

Section: Event Correlationmentioning

confidence: 99%

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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“…However, they do not handle complex structures such as loops and parallelism. The approach proposed in [20] makes use of a reference process model and heuristic information about the execution time of the different activities within the process in order to deduct case ids on unlabeled logs. Another approach called Infer Case Id (ICI) is proposed in [21,22].…”

Section: Related Workmentioning

confidence: 99%

Case notion discovery and recommendation: automated event log building on databases

2019

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Process mining techniques use event logs as input. When analyzing complex databases, these event logs can be built in many ways. Events need to be grouped into traces corresponding to a case. Different groupings provide different views on the data. Building event logs is usually a time-consuming, manual task. This paper provides a precise view on the case notion on databases, which enables the automatic computation of event logs. Also, it provides a way to assess event log quality, used to rank event logs with respect to their interestingness. The computational cost of building an event log can be avoided by predicting the interestingness of a case notion, before the corresponding event log is computed. This makes it possible to give recommendations to users, so they can focus on the analysis of the most promising process views. Finally, the accuracy of the predictions and the quality of the rankings generated by our unsupervised technique are evaluated in comparison to the existing regression techniques as well as to state-of-the-art learning to rank algorithms from the information retrieval field. The results show that our prediction technique succeeds at discovering interesting event logs and provides valuable recommendations to users about the perspectives on which to focus the efforts during the analysis.

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Deducing Case IDs for Unlabeled Event Logs

Cited by 20 publications

References 13 publications

Extracting Multiple Viewpoint Models from Relational Databases

Extracting Multiple Viewpoint Models from Relational Databases

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

Case notion discovery and recommendation: automated event log building on databases

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