TY  - BOOK
A1  - Herzberg, Nico
A1  - Weske, Mathias
T1  - Enriching raw events to enable process intelligence : research challenges
N2  - Business processes are performed within a company’s daily business. Thereby, valuable data about the process execution is produced. The quantity and quality of this data is very dependent on the process execution environment that reaches from predominantly manual to fullautomated. Process improvement is one essential cornerstone of business process management to ensure companies’ competitiveness and relies on information about the process execution. Especially in manual process environments data directly related to the process execution is rather sparse and incomplete. In this paper, we present an approach that supports the usage and enrichment of process execution data with context data – data that exists orthogonally to business process data – and knowledge from the corresponding process models to provide a high-quality event base for process intelligence subsuming, among others, process monitoring, process analysis, and process mining. Further, we discuss open issues and challenges that are subject to our future work.
N2  - Die wertschöpfenden Tätigkeiten in Unternehmen folgen definierten Geschäftsprozessen und werden entsprechend ausgeführt. Dabei werden wertvolle Daten über die Prozessausführung erzeugt. Die Menge und Qualität dieser Daten ist sehr stark von der Prozessausführungsumgebung abhängig, welche überwiegend manuell als auch vollautomatisiert sein kann. Die stetige Verbesserung von Prozessen ist einer der Hauptpfeiler des Business Process Managements, mit der Aufgabe die Wettbewerbsfähigkeit von Unternehmen zu sichern und zu steigern. Um Prozesse zu verbessern muss man diese analysieren und ist auf Daten der Prozessausführung angewiesen. Speziell bei manueller Prozessausführung sind die Daten nur selten direkt zur konkreten Prozessausführung verknüpft. In dieser Arbeit präsentieren wir einen Ansatz zur Verwendung und Anreicherung von Prozessausführungsdaten mit Kontextdaten – Daten die unabhängig zu den Prozessdaten existieren – und Wissen aus den dazugehörigen Prozessmodellen, um ein hochwertige Event- Datenbasis für Process Intelligence Anwendungen, wie zum Beispiel Prozessmonitoring, Prozessanalyse und Process Mining, sicherstellen zu können. Des Weiteren zeigen wir offene Fragestellungen und Herausforderungen auf, welche in Zukunft Gegenstand unserer Forschung sein werden.
T3  - Technische Berichte des Hasso-Plattner-Instituts für Digital Engineering an der Universität Potsdam - 73 
Y1  - 2013
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-64012
SN  - 978-3-86956-241-4
PB  - Universitätsverlag Potsdam
CY  - Potsdam
ER  - 
TY  - JOUR
A1  - Herzberg, Nico
A1  - Meyer, Andreas
A1  - Weske, Mathias
T1  - Improving business process intelligence by observing object state transitions
JF  - Data & knowledge engineering
N2  - During the execution of business processes several events happen that are recorded in the company's information systems. These events deliver insights into process executions so that process monitoring and analysis can be performed resulting, for instance, in prediction of upcoming process steps or the analysis of the run time of single steps. While event capturing is trivial when a process engine with integrated logging capabilities is used, manual process execution environments do not provide automatic logging of events, so that typically external devices, like bar code scanners, have to be used. As experience shows, these manual steps are error-prone and induce additional work. Therefore, we use object state transitions as additional monitoring information, so-called object state transition events. Based on these object state transition events, we reason about the enablement and termination of activities and provide the basis for process monitoring and analysis in terms of a large event log. In this paper, we present the concept to utilize information from these object state transition events for capturing process progress. Furthermore, we discuss a methodology to create the required design time artifacts that then are used for monitoring at run time. In a proof-of-concept implementation, we show how the design time and run time side work and prove applicability of the introduced concept of object state transition events. (C) 2015 Elsevier B.V. All rights reserved.
KW  - Business process management
KW  - Events
KW  - Data
KW  - Process Monitoring
KW  - BPMN
Y1  - 2015
U6  - https://doi.org/10.1016/j.datak.2015.07.008
SN  - 0169-023X
SN  - 1872-6933
VL  - 98
SP  - 144
EP  - 164
PB  - Elsevier
CY  - Amsterdam
ER  -