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Robert Andrews
Researcher at Queensland University of Technology
Publications - 33
Citations - 2184
Robert Andrews is an academic researcher from Queensland University of Technology. The author has contributed to research in topics: Process mining & Data quality. The author has an hindex of 11, co-authored 33 publications receiving 1938 citations. Previous affiliations of Robert Andrews include University of Queensland.
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Survey and critique of techniques for extracting rules from trained artificial neural networks
TL;DR: This survey focuses on mechanisms, procedures, and algorithms designed to insert knowledge into ANNs, extract rules from trained ANNs (rule extraction), and utilise ANNs to refine existing rule bases (rule refinement).
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The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks
TL;DR: This paper shows that not only is the ADT taxonomy applicable to a cross section of current techniques for extracting rules from trained feedforward ANN's but also how the taxonomy can be adapted and extended to embrace a broader range of ANN types and explanation structures.
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Event log imperfection patterns for process mining
TL;DR: This paper describes a set of data quality issues commonly found in process mining event logs or encountered while preparing event logs from raw data sources and proposes a systematic approach to using such a pattern repository to identify and repair event log quality issues.
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Rule extraction from local cluster neural nets
Robert Andrews,Shlomo Geva +1 more
TL;DR: This paper presents results for the LC net on a wide variety of benchmark problems and shows that RULEX produces comprehensible, accurate rules that exhibit a high degree of fidelity with the LC network from which they were extracted.
Journal ArticleDOI
Quality-informed semi-automated event log generation for process mining
Robert Andrews,Christopher G. J. van Dun,Moe Thandar Wynn,Wolfgang Kratsch,Maximilian Röglinger,Arthur H. M. ter Hofstede +5 more
TL;DR: This work validated RDB2Log's design against design objectives extracted from literature and competing artifacts, evaluated its design and performance with process mining experts, implemented a prototype with a defined set of quality metrics, and applied it in laboratory settings and in a real-world case study.