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The two papers "Extrinsic Dependencies in Business Process Management Systems" and "A MapReduce Approach for Mining Multi-Perspective Declarative Process Models" have been accepted for presentation and publication at the 20th International Conference on Enterprise Information Systems (ICEIS 2018). Below you can find the abstracts of the papers.

A MapReduce Approach for Mining Multi-Perspective Declarative Process Models

Automated process discovery aims at generating a process model from an event log. Such models can be represented as a set of declarative constraints where temporal coherencies can also be intertwined with dependencies upon value ranges of data parameters and resource characteristics. Existing mining tools do not support multi-perspective constraint discovery or are not efficient enough. In this paper, we propose an efficient mining framework for discovering multi-perspective declarative models that builds upon the distributed processing method MapReduce. Mining performance and effectiveness have been tested on several real-life
event logs.

Extrinsic Dependencies in Business Process Management Systems

The demand for supporting the flexibility in business processes has been increasing due to dynamic business environments and technological progress. That led to the challenge of designing business processes so as to take context changes into consideration. A context refers to any circumstance of a process and includes factors which impact process execution steps. To overcome this challenge and better fit business processes to customers expectations, this paper conceptualizes contextual factors relevant to the business process description. It defines a model that explains how the relevant contextual factors could be identified and computed in a structured way. To verify the applicability of the identified approach, a prototype is set up for running the experiments. It examines the approach with real information in different real-life scenarios.