Robert Andrews

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Moe Thandar Kyaw WynnProfessor, Queensland University of TechnologyVerified email at qut.edu.au
Joachim DiederichUniversity of QueenslandVerified email at uq.edu.au
Suriadi SuriadiQueensland University of TechnologyVerified email at qut.edu.au
Kanika GoelQueensland University of TechnologyVerified email at qut.edu.au
Fahame EmamjomeQueensland University of TechnologyVerified email at qut.edu.au
Kirsten Vallmuurqueensland university of technologyVerified email at qut.edu.au
Emma BosleyDirector of Research, Queensland Ambulance ServiceVerified email at ambulance.qld.gov.au
Maximilian RöglingerUniversity of Bayreuth; FIM Research Center for Information Management; Fraunhofer FITVerified email at fim-rc.de
Rebekah EdenUniversity of QueenslandVerified email at uq.edu.au
Wil van der AalstProfessor of Process and Data Science, RWTH Aachen University || Celonis || Fraunhofer FIT || TU/eVerified email at pads.rwth-aachen.de
Hajo A. ReijersProfessor of Computer Science, Utrecht University & Eindhoven University of TechnologyVerified email at uu.nl
Erik PoppeResearch Fellow, Queensland University of TechnologyVerified email at qut.edu.au
Anastasiia PikaProcess Mining ResearchVerified email at hdr.qut.edu.au
Guido BolognaUniversity of Applied Sciences and Arts of Western Switzerland, Geneva.Verified email at hesge.ch
Dirk FahlandAssociate Professor in Computer Science, Eindhoven University of TechnologyVerified email at tue.nl
Xixi LuAssistant Professor at Utrecht UniversityVerified email at uu.nl
Prabhakar DixitPhD StudentVerified email at tue.nl
Chun OuyangAssociate Professor, PhD, Queensland University of TechnologyVerified email at qut.edu.au
Frederic MaireAdjunct Senior Lecturer in Robotics, Queensland University of TechnologyVerified email at qut.edu.au
Michael RosemannQueensland University of TechnologyVerified email at qut.edu.au

Robert Andrews

Senior Research Fellow, Queensland University of Technology

Verified email at qut.edu.au

Process mining Data mining Data Quality Pattern recognition Machine learning


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Survey and critique of techniques for extracting rules from trained artificial neural networks R Andrews, J Diederich, AB Tickle Knowledge-based systems 8 (6), 373-389, 1995	1767	1995
The truth will come to light: Directions and challenges in extracting the knowledge embedded within trained artificial neural networks AB Tickle, R Andrews, M Golea, J Diederich IEEE Transactions on Neural Networks 9 (6), 1057-1068, 1998	603	1998
Event log imperfection patterns for process mining: Towards a systematic approach to cleaning event logs S Suriadi, R Andrews, AHM ter Hofstede, MT Wynn Information systems 64, 132-150, 2017	324	2017
Process mining for healthcare: Characteristics and challenges Journal of Biomedical Informatics 127, 103994, 2022	264	2022
Quality-informed semi-automated event log generation for process mining R Andrews, CGJ van Dun, MT Wynn, W Kratsch, MKE Röglinger, ... Decision Support Systems 132, 113265, 2020	96	2020
Rule extraction from a constrained error back propagation MLP R Andrews, S Geva Australian Conference on Neural Networks, Brisbane, Queensland, 9-12, 1994	88	1994
Leveraging data quality to better prepare for process mining: an approach illustrated through analysing road trauma pre-hospital retrieval and transport processes in Queensland R Andrews, MT Wynn, K Vallmuur, AHM Ter Hofstede, E Bosley, M Elcock, ... International journal of environmental research and public health 16 (7), 1138, 2019	65	2019
A case study lens on process mining in practice F Emamjome, R Andrews, AHM ter Hofstede On the Move to Meaningful Internet Systems: OTM 2019 Conferences …, 2019	64	2019
Rule extraction from local cluster neural nets R Andrews, S Geva Neurocomputing 47 (1-4), 1-20, 2002	61	2002
Detection and interactive repair of event ordering imperfection in process logs PM Dixit, S Suriadi, R Andrews, MT Wynn, AHM ter Hofstede, JCAM Buijs, ... Advanced Information Systems Engineering: 30th International Conference …, 2018	57	2018
Inserting and extracting knowledge from constrained error back-propagation networks. R Andrews Proceedings of the 6th Australian Conference on Neural Networks, 1995	56	1995
RULEX & CEBP Networks as the Basis for a Rule Refinement System R Andrews, S Geva Hybrid problems, hybrid solutions 27, 1, 1995	55	1995
Enhancing event log quality: Detecting and quantifying timestamp imperfections DA Fischer, K Goel, R Andrews, CGJ van Dun, MT Wynn, M Röglinger Business Process Management: 18th International Conference, BPM 2020 …, 2020	42	2020
Towards event log querying for data quality: Let’s start with detecting log imperfections R Andrews, S Suriadi, C Ouyang, E Poppe On the Move to Meaningful Internet Systems. OTM 2018 Conferences …, 2018	41	2018
Digital health data quality issues: systematic review R Syed, R Eden, T Makasi, I Chukwudi, A Mamudu, M Kamalpour, ... Journal of Medical Internet Research 25, e42615, 2023	40	2023
Lessons from past, current issues, and future research directions in extracting the knowledge embedded in artificial neural networks AB Tickle, F Maire, G Bologna, R Andrews, J Diederich Hybrid neural systems, 226-239, 2000	34	2000
Improving patient flows at St. Andrew’s War Memorial Hospital’s emergency department through process mining R Andrews, S Suriadi, M Wynn, AHM ter Hofstede, S Rothwell Business Process Management Cases: Digital Innovation and Business …, 2018	33	2018
A comparative process mining analysis of road trauma patient pathways R Andrews, MT Wynn, K Vallmuur, AHM Ter Hofstede, E Bosley International journal of environmental research and public health 17 (10), 3426, 2020	31	2020
Rules and local function networks R Andrews, S Geva Proceedings of the Workshop on Rule Extraction From Trained Artificial …, 1996	29	1996
An evaluation and comparison of techniques for extracting and refining rules from artificial neural networks R Andrews, R Cable, J Diederich, S Geva, M Golea, R Hayward, ... QUT NRC (February 1996), 1996	29	1996

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