Contact Associate Professor Ting Ren of the University of Wollongong for more information email@example.com.
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.
SEMINAR: Mining a System - The Use of Data Mining and System Dynamics to Explore Technology Integration
PRESENTER: Dr Jie Yang, Dr Jun Ma & Dr Sarah Howard
WHERE: 6.102 WHEN: 10:30am, Wednesday, 12th August 2015
RSVP: 11th August to firstname.lastname@example.org
TOPIC: Technological innovation in schools has, as yet, resulted in relatively limited teacher and student engagement with new ways of learning supported through information and communication technologies (ICTs). One of the possible reasons for this is that educational research has struggled to grasp the complexity or dynamic nature of technology integration. In this talk, we will present our approach to this problem, and our use of data mining to understand some of the complexities of technology integration within a theoretical system model of technology integration. Specifically, the analysis explores key factors of students' perceptions of technology integration, to suggest how they may inform teachers' technology integration in the classroom. The analysis draws on 2012 student questionnaire data from the evaluation of a large-scale Australian one-to-one laptop program. Early results demonstrate two patterns of interest in the data, related to students' ICT Engagement. Implications for teaching, technology use in schools and our continuing research in this area will be discussed.
BIO: Dr. Jie Yang, PhD, has extensive experience in machine learning and cloud computing. In 2013, Jie joined the SMART Infrastructure Facility at University of Wollongong, where he is responsible for translating conceptual models into implementation programs and code prototyping. These projects involve the application of big-data processing on infrastructure planning and social media analytics.
BIO: Dr. Jun Ma is a research fellow at SMART. His research interests focus on uncertain information processing and data analysis. Before joining SMART Infrastructure Facility, he worked as a senior research associate at the University of Technology, Sydney (UTS). He has over 10 years of experience working in research and teaching in China and Australia. At SMART, he works as a data miner to provide data and analysis support for SMART research and projects.
BIO: Dr. Sarah Howard a Senior Lecturer in Information and Communication Technologies (ICTs) in Education, at the University of Wollongong. Before coming to Australia, she worked as a graphic designer and Art teacher in San Francisco. Her research interests focus on technology-related educational change and teachers' practice. Specifically, she considers how teachers understand and negotiate technology integration within their school and subject area cultures. From 2010-2014 she was the evaluator of the largest one-to-one laptop program internationally, looking at teacher and student technology adoption and integration. Her research has attracted over $2 million in funding, and has included education and industry partners, such as One Laptop Per Child and Adobe Systems and schools across Australia, in the United States and South Africa.