Decision point analysis of time series data in process-aware information systems

Reinhold Dunkl, Stefanie Rinderle-Ma, Wilfried Grossmann, Karl Anton Fröschl

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


The majority of process mining techniques focuses on control flow. Decision Point Analysis (DPA) exploits additional data attachments within log files to determine attributes decisive for branching of process paths within discovered process models. DPA considers only single attribute values. However, in many applications, the process environment provides additional data in form of consecutive measurement values such as blood pressure or container temperature. We introduce the DPATimeSeries method as an iterative process for exploiting time series data by combining process mining and data mining techniques. The method also offers different approaches for incorporating time series data into log files in order to enable existing process mining techniques to be applied. Finally, we provide the simulation environment DPATimeSeries Sim to produce log files and time series data. The DPATimeSeries method is evaluated based on an application scenario from the logistics domain.

Original languageEnglish
Pages (from-to)33-40
Number of pages8
JournalCEUR Workshop Proceedings
StatePublished - 2014
Externally publishedYes
EventJoint 26th International Conference on Advanced Information Systems Engineering Forum and Doctoral Consortium, CAiSE-Forum-DC 2014 - Thessaloniki, Greece
Duration: 18 Jun 201420 Jun 2014


  • Data mining
  • Decision mining
  • Process mining
  • Time series data


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