Presentation
Resource Utilization Prediction in Decision-Intensive Business Processes
Author/s | Sperl, Simon
Havur, Giray Steyskal, Simon Cabanillas Macías, Cristina ![]() ![]() ![]() ![]() ![]() ![]() ![]() Polleres, Axel Haselböck, Alois |
Department | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Publication Date | 2017 |
Deposit Date | 2020-11-19 |
Published in |
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ISBN/ISSN | 1613-0073 |
Abstract | An appropriate resource utilization is crucial for organizations
in order to avoid, among other things, unnecessary costs (e.g. when
resources are under-utilized) and too long execution times (e.g. due to
excessive ... An appropriate resource utilization is crucial for organizations in order to avoid, among other things, unnecessary costs (e.g. when resources are under-utilized) and too long execution times (e.g. due to excessive workloads, i.e. resource over-utilization). However, traditional process control and risk measurement approaches do not address resource utilization in processes. We studied an often-encountered industry case for providing large-scale technical infrastructure which requires rigorous testing for the systems deployed and identi ed the need of projecting resource utilization as a means for measuring the risk of resource underand over-utilization. Consequently, this paper presents a novel predictive model for resource utilization in decision-intensive processes, present in many domains. In particular, we predict the utilization of resources for a desired period of time given a decision-intensive business process that may include nested loops, and historical data (i.e. order and duration of past activity executions, resource pro les and their experience etc.). We have applied our method using a real business process with multiple instances and presented the outcome. |
Funding agencies | Austrian Research Promotion Agency (FFG) Austrian Science Found (FWF) |
Project ID. | 845638 (SHAPE)
![]() V 569-N31 (PRAIS) ![]() |
Citation | Sperl, S., Havur, G., Steyskal, S., Cabanillas Macías, C., Polleres, A. y Haselböck, A. (2017). Resource Utilization Prediction in Decision-Intensive Business Processes. En SIMPDA 2017: 7th International Symposium on Data-driven Process Discovery and Analysis (128-141), Neuchâtel, Switzerland: CEUR-WS.Org. |
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