@inproceedings{sperl-etal-2017SIMPDA,
  author    = {Simon Sperl and
               Giray Havur and
               Simon Steyskal and
               Cristina Cabanillas and
               Axel Polleres and
               Alois Haselb{\"{o}}ck},
  editor    = {Paolo Ceravolo and
               Maurice van Keulen and
               Kilian Stoffel},
  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 identified 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 profiles and their experience etc.).
We have applied our method using a real business process with multiple
instances and presented the outcome.},
  title     = {Resource Utilization Prediction in Decision-Intensive Business Processes},
  booktitle = {Proceedings of the 7th International Symposium on Data-driven Process Discovery and Analysis {(SIMPDA} 2017)},
  address   = {Neuch{\^{a}}tel, Switzerland},
  month = Dec,
  day = {6--8}, 
  year = 2017,
  series    = {{CEUR} Workshop Proceedings},
  volume    = {2016},
  pages     = {128--141},
  publisher = {CEUR-WS.org},
  url       = {http://ceur-ws.org/Vol-2016/paper10.pdf},
}

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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 identified 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 profiles and their experience etc.). 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However, traditional\nprocess control and risk measurement approaches do not address resource\nutilization in processes. We studied an often-encountered industry case\nfor providing large-scale technical infrastructure which requires rigorous\ntesting for the systems deployed and identified the need of projecting\nresource utilization as a means for measuring the risk of resource underand\nover-utilization. Consequently, this paper presents a novel predictive\nmodel for resource utilization in decision-intensive processes, present in\nmany domains. In particular, we predict the utilization of resources for\na desired period of time given a decision-intensive business process that\nmay include nested loops, and historical data (i.e. order and duration\nof past activity executions, resource profiles and their experience etc.).\nWe have applied our method using a real business process with multiple\ninstances and presented the outcome.},\n title = {Resource Utilization Prediction in Decision-Intensive Business Processes},\n booktitle = {Proceedings of the 7th International Symposium on Data-driven Process Discovery and Analysis {(SIMPDA} 2017)},\n address = {Neuch{\\^{a}}tel, Switzerland},\n month = Dec,\n day = {6--8}, \n year = 2017,\n series = {{CEUR} Workshop Proceedings},\n volume = {2016},\n pages = {128--141},\n publisher = {CEUR-WS.org},\n url = {http://ceur-ws.org/Vol-2016/paper10.pdf},\n}\n\n\n","author_short":["Sperl, S.","Havur, G.","Steyskal, S.","Cabanillas, C.","Polleres, A.","Haselböck, A."],"editor_short":["Ceravolo, P.","van Keulen, M.","Stoffel, K."],"key":"sperl-etal-2017SIMPDA","id":"sperl-etal-2017SIMPDA","bibbaseid":"sperl-havur-steyskal-cabanillas-polleres-haselbck-resourceutilizationpredictionindecisionintensivebusinessprocesses-2017","role":"author","urls":{"Paper":"http://ceur-ws.org/Vol-2016/paper10.pdf"},"metadata":{"authorlinks":{"polleres, a":"https://bibbase.org/show?bib=www.polleres.net/mypublications.bib"}}},"search_terms":["resource","utilization","prediction","decision","intensive","business","processes","sperl","havur","steyskal","cabanillas","polleres","haselböck"],"keywords":[],"authorIDs":["547db45fa29145d03f0005cd","5PFMiHGwfvbGBZwWF","FyLDFGg993nDS2Spf","NCjPvWahWRjdP3ghB","aiXjXMLP63k5WCt84","fTDcT5K3oSTcdxSBj","haaAs2rQaQA7EaZva","nuWuyLnGu7YzMrn4d","pfENTBFWo85mRy3ik","woa42kCD35yCmdQTj"],"dataSources":["cBfwyqsLFQQMc4Fss","gixxkiKt6rtWGoKSh","kpPtwc4Ra4r5Dybva","QfLT6siHZuHw9MqvK"]}