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research:info [2015/09/28 09:21]
hverbeek [Information on research]
research:info [2016/08/24 17:03] (current)
hverbeek [Information on research]
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 for the design of architectures for complex information systems based on requirements or (a description of) an existing system. An architecture is a collection of models, that are described in such a way that properties of these models (and thus the systems) can be formally analyzed. The research concentrates on formalisms for modeling and methods to analyze models. for the design of architectures for complex information systems based on requirements or (a description of) an existing system. An architecture is a collection of models, that are described in such a way that properties of these models (and thus the systems) can be formally analyzed. The research concentrates on formalisms for modeling and methods to analyze models.
  
-As more and more companies are embracing Big Data technologies,​ it becomes apparent that the ultimate challenge is to relate massive amounts of event data to processes that are highly dynamic. To unleash the value of event data, events need to be tightly connected to the control and management of operational processes. However, the primary focus of Big Data technologies is currently on storage, processing, and rather simple analytical tasks. Big Data initiatives rarely focus on the improvement of end-to-end processes. To address this mismatch, we advocate a better integration of data science ​ and process science. Data science approaches tend to be process ​agonistic ​whereas process science approaches tend to be model-driven without considering the “evidence” hidden in the data. Process mining aims to bridge this gap. +{{ :​images:​dsce652.png?​direct |}} 
 +As more and more companies are embracing Big Data technologies,​ it becomes apparent that the ultimate challenge is to relate massive amounts of event data to processes that are highly dynamic. To unleash the value of event data, events need to be tightly connected to the control and management of operational processes. However, the primary focus of Big Data technologies is currently on storage, processing, and rather simple analytical tasks. Big Data initiatives rarely focus on the improvement of end-to-end processes. To address this mismatch, we advocate a better integration of data science ​ and process science. Data science approaches tend to be process ​agnostic ​whereas process science approaches tend to be model-driven without considering the “evidence” hidden in the data. Process mining aims to bridge this gap. 
  
-The AIS group is headed by [[http://wwwis.win.tue.nl/​~wvdaalst/​|Prof.dr.ir. W.M.P. van der Aalst]].+The AIS group is headed by [[:organization:​staff:​w.m.p.v.d.aalst|Prof.dr.ir. W.M.P. van der Aalst]].
  
 ===== Links ===== ===== Links =====
  
   * [[research:​assessment2010|Assessment of research quality]]   * [[research:​assessment2010|Assessment of research quality]]
-  * [[research:​iofeb2011|Eindelijk correcte procesmodellen]]