{"id":{"repo_id":"potsdam-diss","oai_identifier":"oai:kobv.de-opus4-uni-potsdam:8454"},"canonical_url":"https://search.dev.ndltd.org/etd/potsdam-diss/oai:kobv.de-opus4-uni-potsdam:8454","repository":{"repo_id":"potsdam-diss","name":"Universität Potsdam - Diss","base_url":"https://publishup.uni-potsdam.de/opus4-ubp/oai"},"display":{"title":"Matching events and activities","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Baier, Thomas"],"institution":"Universität Potsdam","degree_name":null,"degree_level":"thesis.doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Weske, Mathias"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-12-01","date_published":"2015-12-01","updated_at":"2026-07-24T03:51:54Z","subjects":["process mining","conformance analysis","event abstraction","Übereinstimmungsanalyse","Ereignisabstraktion"],"languages":[],"rights":["Keine öffentliche Lizenz: Unter Urheberrechtsschutz"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://publishup.uni-potsdam.de/frontdoor/index/index/docId/8454","outbound_label":"Repository record","outbound_source":"source_url"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Weske, Mathias"]},{"key":"dc:creator","label":"Author","values":["Baier, Thomas"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:publisher","label":"Institution","values":["Universität Potsdam"]},{"key":"dc:type","label":"Dc Type","values":["doctoralThesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["thesis.doctoral"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Universität Potsdam"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["process mining","conformance analysis","event abstraction","Übereinstimmungsanalyse","Ereignisabstraktion"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Keine öffentliche Lizenz: Unter Urheberrechtsschutz"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.","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."]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Matching events and activities","Zuordnung von Ereignissen zu Aktivitäten"]}]}],"canonical_facts":{"dc:contributor":["Weske, Mathias"],"dc:creator":["Baier, Thomas"],"dc:description.abstract":["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.","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."],"dc:format.medium":["application/pdf"],"dc:publisher":["Universität Potsdam"],"dc:rights":["Keine öffentliche Lizenz: Unter Urheberrechtsschutz"],"dc:subject":["process mining","conformance analysis","event abstraction","Übereinstimmungsanalyse","Ereignisabstraktion"],"dc:title":["Matching events and activities","Zuordnung von Ereignissen zu Aktivitäten"],"dc:type":["doctoralThesis"],"thesis:degree_level":["thesis.doctoral"],"thesis:institution_name":["Universität Potsdam"]},"updated_at":"2026-07-24T03:51:54Z"}