DSL NEWS UPDATE:
Prof. Aditya Ghose will present a keynote at a plenary session of Int'l Conf. on Service-Oriented Computing Workshops on Nov. 16th in Goa, India.
Prof. Aditya Ghose has been invited to deliver a keynote at a joint plenary session of the Service-Oriented Enterprise Architecture and Evolutionary Business Process Workshops at the EDOC-2015 conference (Adelaide, September, 2015). His keynote title: "The post-theoretic enterprise: Data-driven evolution of enterprise functionality".
Outcomes of a project on merging software models led by Dr Hoa Dam with researchers at Johannes Kepler University (Austria) and University of Otago (New Zealand) has recently been published in the highly-regarded Journal of Systems and Software. Earlier results of this research have won a Best Paper Award at the 11th Working IEEE/IFIP Conference on Software Architecture.
DSL fast growing research on Big Data analytics for software engineering (by PhD student Morakot Choetkiertikul, Dr Hoa Dam, and Prof. Aditya Ghose) scored another major success with a paper at the 30th IEEE/ACM International Conference on Automated Software Engineering (ASE), one of the top conferences in software engineering with an acceptance rate of 20.8%.
DSL authors Karthikeyan Ponnalagu, Prof. Aditya Ghose and Dr. Hoa Dam celebrate a major paper success at the BPM-2015 conference (the premier - and highly selective with a 17% acceptance rate - venue for business process management) with a paper that offers ways to use semantic annotation and goal-oriented analysis to manage variability in business process instances.
DSL authors Morakot Choetkiertikul, Dr. Hoa Dam and Prof. Aditya Ghose have won the ACM SIGSOFT Distinguished Paper Award for their work on mining risk indicators for software projects.
Review of "Log delta analysis: Interpretable differencing of business process event logs"
DATE: November 19, 2015
TIME: 4pm onwards
This paper addresses the problem of explaining behavioral differences between two business process event logs. The paper presents a method that, given two event logs, returns a set of statements in natural language capturing behavior that is present or frequent in one log, while absent or infrequent in the other. This log delta analysis method allows users to diagnose differences between normal and deviant executions of a process or between two versions or variants of a process. The method relies on a novel approach to losslessly encode an event log as an event structure, combined with a frequency-enhanced technique for differencing pairs of event structures. A validation of the proposed method shows that it accurately diagnoses typical change patterns and can explain differences between normal and deviant cases in a real-life log, more compactly and precisely than previously proposed methods.