dc.creator | Sperl, Simon | es |
dc.creator | Havur, Giray | es |
dc.creator | Steyskal, Simon | es |
dc.creator | Cabanillas Macías, Cristina | es |
dc.creator | Polleres, Axel | es |
dc.creator | Haselböck, Alois | es |
dc.date.accessioned | 2020-11-19T11:47:54Z | |
dc.date.available | 2020-11-19T11:47:54Z | |
dc.date.issued | 2017 | |
dc.identifier.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. | |
dc.identifier.issn | 1613-0073 | es |
dc.identifier.uri | https://hdl.handle.net/11441/102729 | |
dc.description.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 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. | es |
dc.description.sponsorship | Austrian Research Promotion Agency (FFG) 845638 (SHAPE) | es |
dc.description.sponsorship | Austrian Science Fund (FWF) V 569-N31 (PRAIS) | es |
dc.format | application/pdf | es |
dc.format.extent | 14 | es |
dc.language.iso | eng | es |
dc.publisher | CEUR-WS.Org | es |
dc.relation.ispartof | SIMPDA 2017: 7th International Symposium on Data-driven Process Discovery and Analysis (2017), p 128-141 | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Decision-intensive business processes | es |
dc.subject | Prediction model | es |
dc.subject | Resource utilization | es |
dc.subject | Risk management | es |
dc.title | Resource Utilization Prediction in Decision-Intensive Business Processes | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | 845638 (SHAPE) | es |
dc.relation.projectID | V 569-N31 (PRAIS) | es |
dc.relation.publisherversion | http://ceur-ws.org/Vol-2016/ | es |
dc.publication.initialPage | 128 | es |
dc.publication.endPage | 141 | es |
dc.eventtitle | SIMPDA 2017: 7th International Symposium on Data-driven Process Discovery and Analysis | es |
dc.eventinstitution | Neuchâtel, Switzerland | es |
dc.relation.publicationplace | Aachen, Germany | es |
dc.contributor.funder | Austrian Research Promotion Agency (FFG) | es |
dc.contributor.funder | Austrian Science Found (FWF) | es |