=Paper=
{{Paper
|id=Vol-3263/abstract-15
|storemode=property
|title=Extraction of Object-Centric Event Logs through Virtual Knowledge Graphs (Extended Abstract)
|pdfUrl=https://ceur-ws.org/Vol-3263/abstract-15.pdf
|volume=Vol-3263
|authors=Jing Xiong,Guohui Xiao,Tahir Emre Kalayci,Marco Montali,Zhenzhen Gu,Diego Calvanese
|dblpUrl=https://dblp.org/rec/conf/dlog/Xiong0KMGC22
}}
==Extraction of Object-Centric Event Logs through Virtual Knowledge Graphs (Extended Abstract)==
https://ceur-ws.org/Vol-3263/abstract-15.pdf
Extraction of Object-Centric Event Logs through
Virtual Knowledge Graphs
Extended Abstract
Jing Xiong1 , Guohui Xiao2,3 , Tahir Emre Kalayci4 , Marco Montali1,3 , Zhenzhen Gu1
and Diego Calvanese1,3,5
1
KRDB Research Centre for Knowledge and Data, Free University of Bozen-Bolzano, 39100 Bolzano, Italy
2
Department of Information Science and Media Studies, University of Bergen, 5007 Bergen, Norway
3
Ontopic S.R.L., 39100 Bolzano, Italy
4
Virtual Vehicle Research GmbH, 8010 Graz, Austria
5
Department of Computing Science, Umeå University, 901 87 Umeå, Sweden
Abstract
Process mining is a family of techniques that supports the analysis of operational processes based on
event logs. Among the existing event log formats, the IEEE standard eXtensible Event Stream (XES)
is the most widely adopted. In XES, each event must be related to a single case object, which may
lead to convergence and divergence problems. To solve such issues, object-centric approaches become
promising, where objects are the central notion, and one event may refer to multiple objects. In particular,
the Object-Centric Event Logs (OCEL) standard has been proposed recently. However, the crucial problem
of extracting OCEL logs from external sources is still largely unexplored. In this paper, we try to fill this
gap by leveraging the Virtual Knowledge Graph (VKG) approach to access data in relational databases.
We have implemented this approach in the OnProm system, extending it from XES to OCEL support.
The full version of this article has been submitted to an international conference.
Keywords
Process mining, object-centric event logs, virtual knowledge graph, ontology-based data access
1. Introduction
Process mining [1, 2] is a family of techniques relating the fields of data science and process
management to support the analysis of operational processes based on event logs. To perform
process mining, normally the algorithms and tools expect that the event logs are following
certain standards. However, in reality, most IT systems in companies and organizations do
not directly produce such logs, and the relevant information is spread in legacy systems, in
particular, relational databases. Hence, event log extraction from legacy systems is a key enabler
for process mining [3, 4, 5, 6].
DL 2022: 35th International Workshop on Description Logics, August 7–10, 2022, Haifa, Israel
$ jing.xiong@unibz.it (J. Xiong); guohui.xiao@uib.no (G. Xiao); emre.kalayci@v2c2.at (T. E. Kalayci);
montali@inf.unibz.it (M. Montali); zhenzhen.gu@unibz.it (Z. Gu); calvanese@inf.unibz.it (D. Calvanese)
0000-0002-3604-9645 (J. Xiong); 0000-0002-5115-4769 (G. Xiao); 0000-0001-6228-1221 (T. E. Kalayci);
0000-0002-8021-3430 (M. Montali); 0000-0002-7346-6093 (Z. Gu); 0000-0001-5174-9693 (D. Calvanese)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
There have been several proposals for the representation of event logs, e.g., eXtensible Event
Stream (XES) [7], JSON Support for XES (JXES) [8], Open SQL Log Exchange (OpenSLEX) [9],
and eXtensible Object-Centric (XOC) [10], where XES is the most adopted one, being the IEEE
standard for interoperability in event logs [11]. In XES (and other similar proposals), each event
is related to a single case object, which leads to problems with convergence (when an event is
related to multiple cases and occurs repetitively) and divergence (when multiple events are in
a single case and are hard to separate) [12]. To solve these issues, object-centric approaches
become promising, where objects are the central notion, and one event may refer to multiple
objects. In particular, along this direction, the Object-Centric Event Logs (OCEL) standard [13]
has been proposed recently.
To the best of our knowledge, the crucial problem of extracting OCEL logs from external
sources is still largely unexplored. The only exception is [14], where OCEL logs are extracted by
identifying the so-called master and relevant tables in the underlying database and building a
Graph of Relations (GoR). Though promising, this approach might be difficult to adopt when the
underlying tables are complex and the GoR is hard to model, because it does not separate the
storage level (i.e., the database) from the concept level (i.e., domain knowledge about events).
In this work, we try to fill this gap by leveraging the OnProm (http://onprom.inf.unibz.it/) frame-
work [4, 5] for extracting event logs from legacy information systems. OnProm v1 was already
relying on the technology of Virtual Knowledge Graphs (VKG) [15] to expose databases as
Knowledge Graphs that conform to a conceptual model, and to query this conceptual model
and eventually generate logs by using ontology and mapping-based query processing. It came
with a toolchain to process the conceptual model, and to automatically extract XES event logs,
by relying on the VKG system Ontop [16]. We present here OnProm v2, which we have modu-
larized so that it becomes easier to extend, and in which we have implemented OCEL-specific
features to extract OCEL logs.
2. The OnProm Framework for Event Log Extraction
We describe now the OnProm approach for event log extraction, as shown in Figure 1. To
extract from a legacy information system ℐ = ⟨ℛ, 𝒟⟩, with relational schema ℛ and database 𝒟,
event logs that conform to an event log standard 𝑋, OnProm works as follows:
(1) A domain ontology is a high-level abstraction of business logic concerned in a domain of
interest. The user can design a domain ontology 𝒯 using the standard ontology language
OWL 2 QL using any ontology editing tool, e.g., the Ontology Editor of the OnProm tool chain.
Then the user creates a VKG mapping ℳ (using, e.g., the Ontop plugin for Protégé [17]) to
declare how the instances of classes and properties in 𝒯 are populated from ℐ. This step
is only concerned with modeling the domain of interest and is agnostic to the event log
standard.
(2) OnProm assumes that for the event log standard 𝑋, a specific (domain-independent) event
ontology ℰ𝑋 is available. The Annotation Editor of OnProm imports ℰ𝑋 , and allows the
user to create annotations ℒ𝑋 , which are based on the classes in ℰ𝑋 , over the classes in 𝒯 .
(3) OnProm assumes that for the standard 𝑋 also a set of SPARQL queries for extracting the
log information is defined. By relying on a conceptual schema transformation approach [6]
Onprom
XES event ontology ℰXES XES annotation ℒXES OCEL annotation OCEL event ontology ℰOCEL
📌 📌 ℒOCEL 📌 📌
refers to 📌 refers to
📌 📌 📌
📌 📌 📌
VKG
Domain ontology 𝒯
XES log mapping specification OCEL log mapping specification
Mapping specification ℳ
Information system ℐ
DB schema ℛ
RDB 𝒟
Figure 1: OnProm event log extraction framework
and query reformulation of Ontop, using ℒ𝑋 , 𝒯 , ℳ, and ℛ, these SPARQL queries are
internally translated to SQL queries over ℐ. OnProm evaluates the generated SQL queries
to construct corresponding Java objects and serialize them into log files compliant with 𝑋.
As mentioned, OnProm v1 only supported the XES standard. In this work, we have first
modularized the system, by separating the above steps in different software components, so
as to make it more extensible. Then we have introduced OCEL-specific features in Steps (2)
and (3). Hence, OnProm v2 is now able to extract OCEL logs from relational databases.
Next we illustrate the functionality of OnProm for extracting OCEL logs through an example.
The OCEL event ontology ℰOCEL is a very simple ontology with only three classes: Object,
Event, and Attribute. We consider Dolibarr [18] v14, a popular open source Enterprise Resource
Planning (ERP) system. We have designed a Sale Orders domain ontology and the mapping in
the Ontop system (Figure 2). We have then used the Annotation Editor of OnProm to annotate
this ontology with ℰOCEL classes (Figure 3). Based on the provided information, OnProm is able
to extract OCEL logs automatically. Figure 4 shows a fragment of the extracted log in XML, and
its graphical visualization.
Acknowledgments
This research has been supported by the Wallenberg AI, Autonomous Systems and Software
Program (WASP) funded by the Knut and Alice Wallenberg Foundation, by the Italian PRIN
project HOPE, and by the EU H2020 project INODE, grant n. 863410.
Figure 2: Ontology and mappings shown in Ontop
Figure 3: OnProm Annotation Editor showing the annotated ontology of the Dolibarr ERP system
(a) OCEL XML serialization (b) OCEL graph
Figure 4: A fragment of the extracted OCEL log from the Dolibarr ERP system
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