Checking Compliance in Data-Driven Case Management
Lecture Notes in Business Information Processing, ISSN: 1865-1356, Vol: 362 LNBIP, Page: 400-411
2019
- 8Citations
- 11Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Case management approaches address the special requirements of knowledge workers. In fragment-based case management (fCM), small structured parts are modelled and loosely coupled through data dependencies, which can be freely combined at run-time. When executing business processes, organizations must adhere to regulations, to laws, to company guidelines etc. Business process compliance comprises methods to verify designed and executed business processes against certain rules. While design-time compliance checking works well for structured process models, flexible knowledge-intensive processes have been rarely considered despite increasing interest in academia and industry. In this paper, we present (i) formal execution semantics of fCM models using Petri nets. We also cover concurrently running fragment instances and case termination. We (ii) apply model checking to investigate the compliance with temporal logic rules; finally, we (iii) provide an implementation based on the open-source case modeler Gryphon and the free model checker LoLA.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85078536824&origin=inward; http://dx.doi.org/10.1007/978-3-030-37453-2_33; https://link.springer.com/10.1007/978-3-030-37453-2_33; https://doi.org/10.1007%2F978-3-030-37453-2_33; https://dx.doi.org/10.1007/978-3-030-37453-2_33; https://link.springer.com/chapter/10.1007/978-3-030-37453-2_33
Springer Science and Business Media LLC
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