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Professor Emeritus Dr. Tengku Mohd T. Sembok
National Defence University of Malaysia, Malaysia
Keynote Speech Title:
Semantic Knowledge Representation and Knowledge Discovery
The levels-of-processing theory proposes that there are many ways to process, represent and store information. The more complex the level of processing the better will be the results obtained. In information retrieval the levels of processing can be classified as follows: string processing, morphological processing, syntactic processing and semantic processing. These levels-of-processing are imbedded in various models of information retrieval. Conventional information retrieval models, such as Boolean and Vector Space models rely on an extensive use of keywords, as independent strings, and their frequencies in storing and retrieving information. It is believed that such an approach has reached its upper limit of retrieval effectiveness, and therefore, new approaches have been investigated for the development of the future systems that will be more effective. With advances in natural language processing, artificial intelligence and cognitive science, there are attempts made to include semantics into information retrieval systems. We will highlight some of the research done in the area of information retrieval at the various levels of processing complexity, and also expound the suitability of the approaches to knowledge discovery in the era of data analytics.
Simon James Fong
Professor at University of Macau, China
Big Data Streaming Analytics in R&D: Challenges and Opportunities
Big Data Streaming Analytics (BDSA), an emerging research area, is known as, “techniques that process, analyze 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 optimization 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 also one of the founding members 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-bommerce director in Australia and Asia. Dr. Fong has published over 300 international conference and peer-reviewed journal papers, mostly in the areas of data mining and optimization algorithms.
