Ontology-Based Data Access for Extracting Event Logs from Legacy Data: The onprom Tool and Methodology

Calvanese, Diego; Kalayci, Tahir Emre; Montali, Marco; Tinella, Stefano

doi:10.1007/978-3-319-59336-4_16

Cited by 45 publications

(23 citation statements)

References 9 publications

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“…The combination of the scores of the three individual predictors (Eqs. 16,17,18) in a single scoring function of log interestingness (Eq. 19) is what we call our Custom Predictor (CP).…”

Section: Evaluation Of Predictors' Accuracymentioning

confidence: 99%

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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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“…The combination of the scores of the three individual predictors (Eqs. 16,17,18) in a single scoring function of log interestingness (Eq. 19) is what we call our Custom Predictor (CP).…”

Section: Evaluation Of Predictors' Accuracymentioning

confidence: 99%

“…However, the existing techniques are ad-hoc solutions for ERP and SAP architectures and do not provide a general approach for event log building from databases. Another initiative for event log extraction is the onprom project [15][16][17]. The focus is on event log extraction by means of ontology-based data access (OBDA).…”

Section: Related Workmentioning

confidence: 99%

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

2019

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“…Efforts from the database area also have links to processes. In [7], the authors propose an approach based on describing event logs with annotations of a conceptual model of the data. The technique takes as an input (i) an ontology in the Web Ontology Language (OWL) language; (ii) an Ontology-based Data Access (OBDA) mapping specification; (iii) and the schema annotations specifying cases and events [8].…”

Section: Related Literaturementioning

confidence: 99%

“…Existing literature has addressed several challenges of automatic matching. Two main techniques are case matching [17,6] and mapping [3,7] of events to activities at different abstraction layers. Case matching approaches strive to reconstruct case identifiers compatible with eXtensible Event Stream (XES).…”

Section: Introductionmentioning

confidence: 99%

The Practice of Enterprise Modeling

Buchmann

Karagiannis

Kirikova

2018

Lecture Notes in Business Information Processing

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Process monitoring aims to provide transparency over operational aspects of a business process. In practice, it is a challenge that traces of business process executions span across a number of diverse systems. It is cumbersome manual engineering work to identify which attributes in unstructured event data can serve as case and activity identifiers for extracting and monitoring the business process. Approaches from literature assume that these identifiers are known a priori and data is readily available in formats like eXtensible Event Stream (XES). However, in practice this is hardly the case, specifically when event data from different sources are pooled together in event stores. In this paper, we address this research gap by inferring potential case and activity identifiers in a provenance agnostic way. More specifically, we propose a semi-automatic technique for discovering event relations that are semantically relevant for business process monitoring. The results are evaluated in an industry case study with an international telecommunication provider.

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“…Specifically, [24] and [32] perform semantic modelling and integration of the resulting process maps with annotated terms and then present the domain knowledge for the activities (i.e attributes or concepts) as defined within the ontology using process description languages such as the OWL [7] and SWRL [8]. Indeed, reasoning on ontological knowledge plays an important role in semantic representation of processes [33] by allowing for extraction and conversion of explicit information into some implicit information. For example, the intersection or union of classes, description of relationships and concepts assertions.…”

Section: Algorithmmentioning

confidence: 99%

The Application of a Semantic-Based Process Mining Framework on a Learning Process Domain

Okoye

Islam

Naeem

et al. 2018

Advances in Intelligent Systems and Computing

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The process mining (PM) field combines techniques from computational intelligence which has been lately considered to encompass artificial intelligence (AI) or even the latter, augmented intelligence (AIs) systems, and the data mining (DM) to process modelling in order to analyze event logs. To this end, this paper presents a semantic-based process mining framework (SPMaAF) that exhibits high level of accuracy and conceptual reasoning capabilities particularly with its application in real world settings. The proposed framework proves useful towards the extraction, semantic preparation, and transformation of events log from any domain process into minable executable formats-with focus on supporting the further process of discovering, monitoring and improvement of the extracted processes through semantic-based analysis of the discovered models. Practically, the implementation of the proposed framework demonstrates the main contribution of this paper; as it presents a Semantic-Fuzzy mining approach that makes use of labels (i.e. concepts) within event logs about a domain process using a case study of the Learning Process. The paper provides a method which aims to allow for mining and improved analysis of the resulting process models through semantic-labelling (annotation), representation (ontology) and reasoning (reasoner). Consequently, the series of experimentations and semantically motivated algorithms shows that the proposed framework and its main application in real-world has the capacity of enhancing the PM results or outcomes from the syntactic to a much more abstraction levels.

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Ontology-Based Data Access for Extracting Event Logs from Legacy Data: The onprom Tool and Methodology

Cited by 45 publications

References 9 publications

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

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

The Practice of Enterprise Modeling

The Application of a Semantic-Based Process Mining Framework on a Learning Process Domain

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