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A MapReduce Approach for Mining Multi-Perspective Declarative Process Models

Title data

Sturm, Christian ; Schönig, Stefan ; Jablonski, Stefan:
A MapReduce Approach for Mining Multi-Perspective Declarative Process Models.
In: International Conference on Enterprise Information Systems (ICEIS). - Funchal, Madeira, Portugal : SCITEPRESS Science and Technology Publications , 2018

Abstract in another language

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.

Further data

Item Type: Article in a book
Refereed: Yes
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IV > Chair Applied Computer Science IV - Univ.-Prof. Dr.-Ing. Stefan Jablonski
Faculties
Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Applied Computer Science IV
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 21 Dec 2017 07:56
Last Modified: 21 Dec 2017 07:56
URI: https://eref.uni-bayreuth.de/id/eprint/41064