Efficient Selection of Process Mining Algorithms
Raymond Wong (UNSW)
NICTA SML SEMINARDATE: 2012-09-27
TIME: 11:15:00 - 12:00:00
LOCATION: NICTA - 7 London Circuit
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ABSTRACT:
While many process mining algorithms have been proposed recently, there does not exist a widely-accepted benchmark to evaluate and compare these process mining algorithms. As a result, it can be difficult to choose a suitable process mining algorithm for a given enterprise or application domain. Some recent benchmark systems have been developed and proposed to address this issue. However, evaluating available process mining algorithms against a large set of business models is computationally expensive, tedious and time-consuming. This seminar presents a scalable solution that can evaluate, compare and rank these process mining algorithms efficiently, and hence proposes a novel framework that can efficiently select the process mining algorithms that are most suitable for a given model set. In particular, using the proposed solution, only a portion of process models need empirical evaluation and others can be recommended directly via a prediction model. As a further optimization, we also discuss a metric and technique to select high quality reference models to derive an effective regression model. Experiments using artificial and real enterprise datasets show that our approach is practical and outperforms the traditional approach.
BIO:
Raymond Wong is currently an Associate Professor at School of Computer Science and Engineering, University of New South Wales, Visiting Professor of Tsinghua University and CTO of Cohesive Data Inc. During 2005-2010, he led the database research group at NICTA NRL. He has published more than 100 research papers and supervised more than 12 PhD graduates to completion. He received his BSc from The Australian National University, MPhil and PhD from Hong Kong University of Science and Technology.
