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DTSTART:20161030T030000
TZOFFSETFROM:+0200
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RDATE:20171029T030000
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DTSTART:20170326T020000
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UID:calendar.12381.field_data.0@www.diag.uniroma1.it
DTSTAMP:20260404T130117Z
CREATED:20170422T134424Z
DESCRIPTION:Predictive business process monitoring is concerned with predic
 ting future states or properties of ongoing executions of a business proce
 ss\, based on past executions thereof. Such predictions can range from pre
 dicting which activity will be performed next\, when\, and who will perfor
 m it\, to predicting the remaining execution time or the final outcome of 
 the process. For example\, in an order-to-cash process\, predictive monito
 ring techniques can be used to predict how likely is it that a purchase or
 der will be fulfilled on time\, or how likely is it that the customer will
  be satisfied after fulfillment of the order. In this talk\, we will prese
 nt a framework for conceptualizing and addressing predictive process monit
 oring problems using various machine learning techniques\, ranging from cl
 assical classification techniques (e.g. random forests)\, to Hidden Markov
  Models and Recurrent Neural Networks. We will also present an empirical e
 valuation of the relative performance of these techniques and discuss thei
 r relative applicability and limitations. The seminar will be given in Aul
 a A6 at 14:15 on Friday 28 April 2017BioMarlon Dumas is Professor of Softw
 are Engineering at University of Tartu\, Estonia. Prior to this appointmen
 t he was faculty member at Queensland University of Technology and visitin
 g researcher at SAP Research\, Australia. His research interests span acro
 ss the fields of software engineering\, information systems and business p
 rocess management. His ongoing work focuses on combining data mining and f
 ormal methods for analysis and monitoring of business processes. He has pu
 blished extensively in conferences and journals across the fields of softw
 are engineering and information systems. He is co-inventor of seven grante
 d US/EU patents and co-author of two textbooks in the field of business pr
 ocess management.  
DTSTART;TZID=Europe/Paris:20170428T140000
DTEND;TZID=Europe/Paris:20170428T140000
LAST-MODIFIED:20190805T155749Z
LOCATION:Aula A6
SUMMARY:Predictive Monitoring of Business Processes - Marlon Dumas
URL;TYPE=URI:https://www.diag.uniroma1.it/node/12381
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