Business Intelligence and Analytics: Research, Education and Trends
Dr William Yeoh, director, IBM Centre of Excellence in Business Analytics, Deakin University
In a recent worldwide survey of ICT spending conducted by Gartner, Business Intelligence and Analytics (BIA) technologies (still) ranked among top technology priorities for many chief information officers (CIOs). Based on my past 10 years research and teaching (and course development) experience in this BIA space, this talk aims to share some personal insights into the evolution of BIA research (as well as the development of BIA education/training programs in Australia) and shed light on possible new avenues for future research.
Dr William Yeoh is the Director of Australia’s first IBM Centre of Excellence in Business Analytics at Deakin University. He received his PhD from the University of South Australia. He is actively undertaking research in Business Intelligence and Analytics, Information Quality, Cloud Computing, Crowdsourcing and Information Systems. His research are supported by various funding bodies and have appeared in high-tier journals (including A* & A journals) and most competitive top five Information Systems conferences. Moreover, his coached team was crowned the World Champion at the 2016 IBM Watson Analytics Global Competition held in Las Vegas. He was the recipient of Deakin’s Vice Chancellor Award and the internationally-competitive IBM Faculty Award. He is also the current Editor-in-Chief of the International Journal of Business Intelligence Research.
Big Data Streaming Analytics in R&D: Challenges and Opportunities
Simon Fong, University of Macau
Big Data Streaming Analytics (BDSA), an emerging research area, is known as, “techniques that process, analyse and data mine over massive amounts of information while it is dynamically feeding in, as opposed to waiting for data to come to rest in a data warehouse or Hadoop. The technology is being applied increasingly as new sources of data become prevalent, such as streaming sensor data from the Internet of Things, streaming social media data, and streaming video feeds from CCTV.” In big data era, when data are generated and ever changing in real-time, it is no longer enough to simply perform historical analysis and batch reports. In situations where you need to make well-informed decisions in real-time, the data and insights must also be timely and immediately actionable. In this talk, the platforms, techniques and the pros and cons of various data stream mining algorithms are reviewed. In particular, a research methodology called “Stream-based Holistic Analytics and Reasoning in Parallel (SHARP)” is presented. The potentials and efficacy of GPU programming and meta-heuristic optimisation over data stream mining are discussed, pertaining to computational research in BDSA.
Simon Fong graduated from La Trobe University, Australia, with a 1st Class Honours BEng. Computer Systems degree and a PhD. Computer Science degree in 1993 and 1998 respectively. Simon is now working as an Associate Professor at the Computer and Information Science Department of the University of Macau. He is a co-founder of the Data Analytics and Collaborative Computing Research Group in the Faculty of Science and Technology. Prior to his academic career, Simon took up various managerial and technical posts, such as systems engineer, IT consultant and e-commerce director in Australia and Asia. Dr Fong has published over 365 international conference and peer-reviewed journal papers, mostly in the areas of data mining, big data analytics, meta-heuristics optimisation algorithms, and their applications. He serves on the editorial boards of the Journal of Network and Computer Applications of Elsevier, IEEE IT Professional magazine, and various special issues of SCIE-indexed journals.
Semantic Mapping of Big Data
Hai Zhuge, Aston University, UK
Big data research is shifting the science paradigm and driving the fourth industrial revolution but what is the fundamental challenge of big data computing? How can we map big data facing the challenge? This lecture will introduce two techniques to semantically map big data, including semantic link Network and multi-dimensional category space.
Professor Hai Zhuge is a Chair in Computer Science at Aston University and a joint professor in Chinese Academy of Sciences. He has made systematic contribution to semantics modelling, knowledge modelling and practice of advanced cyber-infrastructure for knowledge sharing and management through lasting fundamental innovation on knowledge, semantics, dimension and self-organisation. He is a Distinguished Scientist of the ACM (Association of Computing Machinery) and a Fellow of British Computer Society.
Design Informatics: Embracing Data Analytics in Design Engineering
Ying Liu, Cardiff University, UK
With the arrival of cyber-physical systems or internet of things era, the massive human- and machine-generated data will create unprecedented challenges and at the same time unmatched opportunities in advancing the theory, methods, tools and practice of data analytics for the design of product, system and service. In this talk, I will first introduce the concept and scope of Design Informatics and its rising interests in different design communities in the past ten years. I will then introduce a number of recent research efforts under design informatics where the primary perspective is to showcase how an application domain can embrace data analytics so that exciting researches take place. I shall close my talk by highlighting some interesting future research directions.
Dr Ying Liu is currently an Associate Professor (SL) with the Institute of Mechanical and Manufacturing Engineering at the School of Engineering in Cardiff University, UK. He obtained his bachelor and master both in Mechanical Engineering from Chongqing University, China in 1998 and 2001, and then MSc and PhD from the Innovation in Manufacturing Systems and Technology (IMST) program under the Singapore MIT Alliance (SMA) at Nanyang Technological University and National University of Singapore in 2002 and 2006 respectively. His research interests focus primarily on design informatics, manufacturing informatics, intelligent manufacturing, design methodology and process, product design, and ICT in design and manufacturing. He is an Associate Editor of the ASME Journal of Computing and Information Science in Engineering (JCISE) and the Journal of Industrial and Production Engineering (Taylor & Francis) and is on the Editorial Board of Advanced Engineering Informatics (ADVEI). Currently, he is editing a special issue on Data-Driven Design (D^3) with the ASME Journal of Mechanical Design (JMD).
Machine Learning and Signal Processing for Big Data
M L D Wong, Heriot-Watt University, Malaysia
In this talk, we would review some of the recent developments in machine learning and signal processing (MLSP) for big data analytics. We will also look at some of the challenges and opportunities in this area.
M L D Wong is currently a Professor and Deputy Provost at Heriot-Watt University Malaysia. He received his BEng (Hons) in Electronics and Communication Engineering and PhD in Signal Processing from the Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, UK. In 2004, Dr Wong joined the School of Engineering, Swinburne University of Technology Sarawak Campus as a Lecturer. Subsequently, he was appointed a Senior Lecturer in 2007 at the same institution. From 2011 to mid 2012, he was an Associate Professor at Xi’an Jiaotong-Liverpool University, Suzhou, China. In June 2012, Dr Wong was appointed as an Associate Professor and Acting Dean for Faculty of Engineering, Computing and Science at Swinburne Sarawak. Subsequently, he was appointed as the full Dean in August 2013. In October 2016, Professor Wong moved to Heriot-Watt University to take up his current appointment.
Professor Wong is also a Chartered Engineer registered with the Engineering Council UK; a Fellow of the IET; a Fellow and Chartered Professional Engineer with IEAust and a Senior Member of the IEEE. His research interests include statistical signal processing and pattern classification, machine condition monitoring, and VLSI for digital signal processing.