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Trace retrieval for business process operational support
Affiliation:1. DISIT, Computer Science Institute, Università del Piemonte Orientale, Alessandria, Italy;2. Department of Computer Science, Università di Torino, Italy;1. UFABC - CMCC, av. dos Estados 5001, Bl.B, 09210–580 St. André, SP, Brazil;2. UFU - FACOM, av. João Neves de Ávila 2121, Bl.B, 38400–902 Uberlândia, MG, Brazil;3. UNIVASF - CENEL, av. Antônio C. Magalhães 510, 48902-300, Juazeiro, BA, Brazil;4. UNESP - DCCE, r. Cristóvão Colombo 2265, 15054-000, S. J. Rio Preto, SP, Brazil;5. IFTM, r. Belarmino Vilela Junqueira S/N, 38305-200, Ituiutaba, MG, Brazil;1. Department of Intelligent Systems, Halmstad University, Box 823, Halmstad S 301 18, Sweden;2. Department of Electric Power Systems, Kaunas University of Technology, Studentu 50, Kaunas LT-51368, Lithuania;1. Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, Madrid 28040, Spain;2. School of Computing, Office S129A, University of Kent, Cornwallis South Building, Canterbury CT2 7NF, UK;1. Department of Signal Processing and Communications, Universidad de Alcalá, Madrid, Spain;2. OPTIMA Area, TECNALIA, Zamudio 48170, Bizkaia, Spain;3. Department of Communications Engineering, University of the Basque Country UPV/EHU, Bilbao 48013, Bizkaia, Spain;1. Departamento de Economía, Métodos Cuantitativos e Historia Económica, Universidad Pablo de Olavide, Ctra. de Utrera Km. 1, Sevilla 41013, Spain;2. Departamento de Organización Industrial y Gestión de Empresas I, Universidad de Sevilla, Spain;3. Departamento de Economía Aplicada III, Universidad de Sevilla, Spain
Abstract:Operational support assists users while process instances are being executed, by making predictions about the instance completion, or recommending suitable actions, resources or routing decisions, on the basis of the already completed instances, stored as execution traces in the event log.In this paper, we propose a case-based retrieval approach to business process management operational support, where log traces are exploited as cases. Once past traces have been retrieved, classical statistical techniques can be applied to them, to support prediction and recommendation. The framework enables the user to submit queries able to express complex patterns exhibited by the current process instance. Such queries can be composed by several simple patterns (i.e., single actions, or direct sequences of actions), separated by delays (i.e., actions we do not care about). Delays can also be imprecise (i.e., the number of actions can be given as a range). The tool also relies on a tree structure, adopted as an index for a quick retrieval from the available event log.Our approach is highly innovative with respect to the existing literature panorama, since it is the first work that exploits case-based retrieval techniques in the operational support context; moreover, the possibility of retrieving traces by querying complex patterns and the indexing strategy are major departures also with respect to other existing trace retrieval tools proposed in the case based reasoning area.Thanks to its characteristics and methodological solutions, the tool implements operational support tasks in a flexible and efficient way, as demonstrated by our experimental results.
Keywords:
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