IVI SEMINAR: 1) Knowledge Representation & Acquisition for Big Data Analytics 2) From the IT Era to the Knowledge Era

Date: 27th September 2016 (Tuesday)
Time: 09:30 am – 12:30 pm
Venue: Bilik Mesyuarat Teratai, Pusat Teknologi Maklumat, UKM.

SPEAKER: PROF. DR. ZAHARIN YUSOFF, UPNM

Prof. Dr. Zaharin Yusoff is a professor in computational linguistics at Universiti Pertahanan Nasional Malaysia (UPNM), and a fellow of the Academy of Sciences Malaysia. He began his career in 1980 at Universiti Sains Malaysia (USM), where he served for 25 years, including being the Coordinator of the Computer-Aided Translation Unit, the founding Dean of the School of Computer Sciences, and the Dean of Research (ICT Platform). In August 2005, he went on his secondment to MIMOS, where he served as Senior Director of the Productisation Unit, and then of the Artificial Intelligence Centre as well as of the Knowledge Technology Centre until May 2007. In the rest of 2007, he was the Dean of the College of Graduate Studies at UNITEN, before moving on to be the President of Multimedia University (MMU) from January 2008 to December 2010. He then moved on to Universiti Malaysia Sarawak (UNIMAS) as well as retained a position as Senior Consultant at Gagasan Wibawa Sdn. Bhd. under Andaman Berhad.

He joined UPNM in October 2013, first as the Head of the Artificial Intelligence Section of the Cyber Security Centre, then as the Deputy Dean (Academic) from July 2014, and currently he is the Director of the Publications Centre as of January 2016. He has published numerous papers, won many research and commercialisation grants, graduated many postgraduate students, and led various initiatives, all nationally and internationally.

 SEMINAR ABSTRACTS

1) Knowledge Representation & Acquisition for Big Data Analytics

There is currently a lot of interest in Big Data. Big data analytics is certainly a very much needed domain. It is not new, dating back to the days of information systems, and data warehousing, but made complicated with the size of the data accumulated (especially due to the internet), the non-standardised representation, the disparate locations, and the lack of (even virtual) integration.

Big Data is often seen as extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions. Big Data Analytics is then the process of collecting, organising and analysing large sets of data to discover patterns and other useful information – ranging from business applications, such as customer profiling, purchasing patterns, etc., to high-level security initiatives, such as identification of potential terrorists, cyber espionage, etc.

In practice, the current scenario is that more often than not, whenever there is a large amount of data, one tends to use any available software that brings out some pattern, and the rest is left for interpretation, preferably one that the client wants to hear. Furthermore, results from a big data exercise are usually not properly stored, and thus a fresh exercise would be needed when the same need arises.

A look at the above situation points towards the need for some level of knowledge representation (hence a knowledge base), which should complement current big data efforts in providing additional information that may be required as well as to store previous results to support incremental analyses. The results from the big data efforts should also help in improving the existing knowledge base. A knowledge base requires various forms of knowledge acquisition, thus the theme of this talk: Big Data → Knowledge Representation → Knowledge Acquisition

2) From the IT Era to the Knowledge Era

ICT is now very ubiquitous. There is virtually nothing that has not been impacted by ICT – Education, Business, Healthcare, Travel, Awareness of the World Around Us, Social Interaction, Entertainment, etc. What it does for us is to provide convenience, supporting access, storage and dissemination for information, communication, entertainment, business, but indeed very much more significantly, efficiency in terms of accuracy, speed and automation.

It can be said that the ICT era is very much already here, and it is now time to work towards the next stage – the knowledge era. This is very much needed, not only for the nation to be there amongst the advanced countries, but more so to make computers less of what they are now – monotonous, intolerant, and fundamentally stupid. Data is the collection of symbols, and information is the understanding of relations – responding to the questions of who, what, where, and when. Intelligent systems are poised to fill a growing number of roles in today’s society, and such systems require knowledge – the understanding of patterns, responding to the how and why questions. This will be working on a step towards the ultimate level of wisdom – the understanding of principles, responding to evaluated understanding.

The nation should make a concerted effort towards immersing into the knowledge era, but earlier problems in the ICT era have to be avoided in order to take full advantage of this fast emerging opportunity.