DMA4SP 2013 - International Workshop on DATA MANAGEMENT AND ANALYTICS FOR SEMI-STRUCTURED BUSINESS PROCESSES (DMA4SP)

The data mining and business process management communities are currently quite separate. For example, the community in the field of semi-structured and unstructured data works on mining documents of different kinds such as free form xml or text data. Similarly, the image and video processing communities work with data of various kinds. The business process management community is a different community of researchers, which tends to work independently. Researchers in each of these communities think of similar problems related to data and process management. Furthermore both communities experience equal impact from recent rapid advances in the way data evolves and is exchanged brought on by the proliferation of social network and communication platforms and different social media. This workshop intends to bring researchers together from both communities to engage in an exchange of ideas to further collaborative research in the two fields on problems of common interest. Such a fusion is likely to lead to a learning experience for both communities.
Recent interest in topics that intersect data mining and management issues and business process management has peaked as evidenced by the emergence of the IEEE Task force on process mining and the resulting process mining manifesto [1]
Papers on, but not limited to, the following topics under the scope of this workshop are encouraged:
Predictive Modeling
Process graph or workflow graph mining and optimization
Analysis of social media and collaborative tools integrated into business management platforms
Learning algorithms
Data integration, event correlation and event processing
Semantic interpretation and analysis of heterogeneous and semi-structured data
Meta data management and interoperability
Community or social network detection and management
Monitoring data from business management platforms and social media platforms
Visual interfaces for mined process data and visual interactions with mined process data