From Relational Database to Event Log: Decisions with Quality Impact

Jans, Mieke; Soffer, Pnina

doi:10.1007/978-3-319-74030-0_46

Cited by 25 publications

(19 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most processes in practice, however, involve multiple inter-related entities. In such data, each event is directly or indirectly linked to multiple different case identifiers and is part of multiple behaviors or dynamics [11]. The data has multiple behavioral dimensions.…”

Section: Introductionmentioning

confidence: 99%

Multi-Dimensional Event Data in Graph Databases

Esser

Fahland

2021

J Data Semant

View full text Add to dashboard Cite

Process event data is usually stored either in a sequential process event log or in a relational database. While the sequential, single-dimensional nature of event logs aids querying for (sub)sequences of events based on temporal relations such as “directly/eventually-follows,” it does not support querying multi-dimensional event data of multiple related entities. Relational databases allow storing multi-dimensional event data, but existing query languages do not support querying for sequences or paths of events in terms of temporal relations. In this paper, we propose a general data model for multi-dimensional event data based on labeled property graphs that allows storing structural and temporal relations in a single, integrated graph-based data structure in a systematic way. We provide semantics for all concepts of our data model, and generic queries for modeling event data over multiple entities that interact synchronously and asynchronously. The queries allow for efficiently converting large real-life event data sets into our data model, and we provide 5 converted data sets for further research. We show that typical and advanced queries for retrieving and aggregating such multi-dimensional event data can be formulated and executed efficiently in the existing query language Cypher, giving rise to several new research questions. Specifically, aggregation queries on our data model enable process mining over multiple inter-related entities using off-the-shelf technology.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-Dimensional Event Data in Graph Databases

Esser

Fahland

2021

J Data Semant

View full text Add to dashboard Cite

show abstract

“…The OpenSLEX format makes use of a SQL schema to store all the information, and a Java API 4 is available for its integration in other tools. An evaluation of the use of OpenSLEX [6] in several environments tackles the data extraction and transformation phase and demonstrates its flexibility and potential to enable standard querying and advanced data analyses.…”

Section: Preliminariesmentioning

confidence: 99%

“…This problem is not new to the field. Some authors have studied how the choices made during the log building process can affect the log quality [4] and have developed procedures to minimize the negative impact. Other authors have tried to define metrics to assess different log properties from the structural point of view [5].…”

Section: Log Quality: Is My Log Interesting?mentioning

confidence: 99%

“…Therefore, it is surprising that there are only a few papers on these topics. Works like the one presented in [4] analyze the choices that users often need to make when building event logs from databases. Also, it proposes a set of guidelines to ensure that these choices do not negatively impact the quality of the resulting event log.…”

Section: Related Workmentioning

confidence: 99%

“…The log quality problem is concerned with identifying the properties that make an event log more suitable to be analyzed, i.e., the characteristics that increase the probability of obtaining valuable insights from the analysis of such an event log. The choices made during the log building process have an effect on the log quality [4]. Also, metrics to assess structural log properties have been proposed by some authors [5], which may be important to assess log quality.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

2019

View full text Add to dashboard Cite

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.

show abstract

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

Diba

Batoulis

Weidlich

et al. 2019

WIREs Data Min & Knowl

View full text Add to dashboard Cite

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.

show abstract

From Relational Database to Event Log: Decisions with Quality Impact

Cited by 25 publications

References 13 publications

Multi-Dimensional Event Data in Graph Databases

Multi-Dimensional Event Data in Graph Databases

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

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

Contact Info

Product

Resources

About