TY  - THES
A1  - Baier, Thomas
T1  - Matching events and activities
T1  - Zuordnung von Ereignissen zu Aktivitäten
BT  - preprocessing event logs for process analysis
BT  - Vorverarbeitung von Ereignislogs für die Prozessanalyse
N2  - Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during process execution. Aiming at a better process understanding and improvement, this event data can be used to analyze processes using process mining techniques. Process models can be automatically discovered and the execution can be checked for conformance to specified behavior. Moreover, existing process models can be enhanced and annotated with valuable information, for example for performance analysis. While the maturity of process mining algorithms is increasing and more tools are entering the market, process mining projects still face the problem of different levels of abstraction when comparing events with modeled business activities. Mapping the recorded events to activities of a given process model is essential for conformance checking, annotation and understanding of process discovery results. Current approaches try to abstract from events in an automated way that does not capture the required domain knowledge to fit business activities. Such techniques can be a good way to quickly reduce complexity in process discovery. Yet, they fail to enable techniques like conformance checking or model annotation, and potentially create misleading process discovery results by not using the known business terminology.

In this thesis, we develop approaches that abstract an event log to the same level that is needed by the business. Typically, this abstraction level is defined by a given process model. Thus, the goal of this thesis is to match events from an event log to activities in a given process model. To accomplish this goal, behavioral and linguistic aspects of process models and event logs as well as domain knowledge captured in existing process documentation are taken into account to build semiautomatic matching approaches. The approaches establish a pre--processing for every available process mining technique that produces or annotates a process model, thereby reducing the manual effort for process analysts. While each of the presented approaches can be used in isolation, we also introduce a general framework for the integration of different matching approaches.


The approaches have been evaluated in case studies with industry and using a large industry process model collection and simulated event logs. The evaluation demonstrates the effectiveness and efficiency of the approaches and their robustness towards nonconforming execution logs.
N2  - Heutzutage werden Geschäftsprozesse verstärkt durch IT Services unterstützt, welche große Mengen an Ereignisdaten während der Prozessausführung generieren. Mit dem Ziel eines besseren Prozessverständnisses und einer möglichen Verbesserung können diese Daten mit Hilfe von Process–Mining–Techniken analysiert werden. Prozessmodelle können dabei automatisiert erstellt werden und die Prozessausführung kann auf ihre Übereinstimmung hin geprüft werden. Weiterhin können existierende Modelle durch wertvolle Informationen erweitert und verbessert werden, beispielsweise für eine Performanceanalyse. Während der Reifegrad der Algorithmen immer weiter ansteigt, stehen Process–Mining–Projekte immer noch vor dem Problem unterschiedlicher Abstraktionsebenen von Ereignisdaten und Prozessmodellaktivitäten. Das Mapping der aufgezeichneten Ereignisse zu den Aktivitäten eines gegebenen Prozessmodells ist ein essentieller Schritt für die Übereinstimmungsanalyse, Prozessmodellerweiterungen sowie auch für das Verständnis der Modelle aus einer automatisierten Prozesserkennung. Bereits existierende Ansätze abstrahieren Ereignisse auf automatisierte Art und Weise, welche die notwendigen Domänenkenntnisse für ein Mapping zu bestehenden Geschäftsprozessaktivitäten nicht berücksichtigt. Diese Techniken können hilfreich sein, um die Komplexität eines automatisiert erstellten Prozessmodells schnell zu verringern, sie eignen sich jedoch nicht für Übereinstimmungsprüfungen oder Modellerweiterungen. Zudem können solch automatisierte Verfahren zu irreführenden Ergebnissen führen, da sie nicht die bekannte Geschäftsterminologie verwenden.

In dieser Dissertation entwickeln wir Ansätze, die ein Ereignislog auf die benötigte Abstraktionsebene bringen, welche typischerweise durch ein Prozessmodell gegeben ist. Daher ist das Ziel dieser Dissertation, die Ereignisse eines Ereignislogs den Aktivitäten eines Prozessmodells zuzuordnen. Um dieses Ziel zu erreichen, werden Verhaltens- und Sprachaspekte von Ereignislogs und Prozessmodellen sowie weitergehendes Domänenwissen einbezogen, um teilautomatisierte Zuordnungsansätze zu entwickeln. Die entwickelten Ansätze ermöglichen eine Vorverarbeitung von Ereignislogs, wodurch der notwendige manuelle Aufwand für den Einsatz von Process–Mining–Techniken verringert wird.

Die vorgestellten Ansätze wurden mit Hilfe von Industrie-Case-Studies und simulierten Ereignislogs aus einer großen Prozessmodellkollektion evaluiert. Die Ergebnisse demonstrieren die Effektivität der Ansätze und ihre Robustheit gegenüber nicht-konformem Prozessverhalten.
KW  - process mining
KW  - conformance analysis
KW  - event abstraction
KW  - Process Mining
KW  - Übereinstimmungsanalyse
KW  - Ereignisabstraktion
Y1  - 2015
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-84548
ER  - 
TY  - JOUR
A1  - Baier, Thomas
A1  - Di Ciccio, Claudio
A1  - Mendling, Jan
A1  - Weske, Mathias
T1  - Matching events and activities by integrating behavioral aspects and label analysis
JF  - Software and systems modeling
N2  - Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during the execution of a process. These event data can be used to analyze the process using process mining techniques to discover the real process, measure conformance to a given process model, or to enhance existing models with performance information. Mapping the produced events to activities of a given process model is essential for conformance checking, annotation and understanding of process mining results. In order to accomplish this mapping with low manual effort, we developed a semi-automatic approach that maps events to activities using insights from behavioral analysis and label analysis. The approach extracts Declare constraints from both the log and the model to build matching constraints to efficiently reduce the number of possible mappings. These mappings are further reduced using techniques from natural language processing, which allow for a matching based on labels and external knowledge sources. The evaluation with synthetic and real-life data demonstrates the effectiveness of the approach and its robustness toward non-conforming execution logs.
KW  - Process mining
KW  - Event mapping
KW  - Business process intelligence
KW  - Constraint satisfaction
KW  - Declare
KW  - Natural language processing
Y1  - 2018
U6  - https://doi.org/10.1007/s10270-017-0603-z
SN  - 1619-1366
SN  - 1619-1374
VL  - 17
IS  - 2
SP  - 573
EP  - 598
PB  - Springer
CY  - Heidelberg
ER  - 
TY  - JOUR
A1  - Baier, Thomas
A1  - Mendling, Jan
A1  - Weske, Mathias
T1  - Bridging abstraction layers in process mining
JF  - Information systems
N2  - While the maturity of process mining algorithms increases and more process mining tools enter the market, process mining projects still face the problem of different levels of abstraction when comparing events with modeled business activities. Current approaches for event log abstraction try to abstract from the events in an automated way that does not capture the required domain knowledge to fit business activities. This can lead to misinterpretation of discovered process models. We developed an approach that aims to abstract an event log to the same abstraction level that is needed by the business. We use domain knowledge extracted from existing process documentation to semi-automatically match events and activities. Our abstraction approach is able to deal with n:m relations between events and activities and also supports concurrency. We evaluated our approach in two case studies with a German IT outsourcing company. (C) 2014 Elsevier Ltd. All rights reserved.
KW  - Process mining
KW  - Abstraction
KW  - Event mapping
Y1  - 2014
U6  - https://doi.org/10.1016/j.is.2014.04.004
SN  - 0306-4379
SN  - 1873-6076
VL  - 46
SP  - 123
EP  - 139
PB  - Elsevier
CY  - Oxford
ER  -