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Detection of Dataflow Anomalies in Business Process An Overview of Modeling Approaches

EasyChair Preprint no. 226

5 pagesDate: June 2, 2018


Most research focus on control flow modeling when modeling and analyzing business process models, but less attention is paid to data flow. However, both of data flow and control flow are essential in process modeling. Thus, the verification and data flow modeling have an important in detecting anomalies. However, when they began to effect data flow modeling in process model there are several errors discovered. In this study, some recent approaches for anomalies detection are reviewed. Eventually, the approaches are: first an analytical approach for detecting and eliminating the three types of data-flow errors, that formally establishes the correctness criteria for data-flow modeling. Second, formulate the data-flow modeling and verification using a Petri Net based approach. Third, an ad hoc approach detecting data modelling errors in business process models apply for an active help. indeed, we explain for each approach its proper method and tools. We then compare and analyze them in order to discover the added-value of each approach.

Keyphrases: data anomalies, data validation, data-flow modeling, verification

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Najat Chadli and Mohammed Issam Kabbaj and Zohra Bakkouri},
  title = {Detection of Dataflow Anomalies in Business Process  An Overview of Modeling Approaches },
  howpublished = {EasyChair Preprint no. 226},

  year = {EasyChair, 2018}}
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