Institute of Information Science, Academia Sinica

Continuous advancements of information systems and technology, have been the prime motivation for industries to increasingly obtain the capability to accumulate huge amounts of data from all levels of their operation. This has resulted in creating large databases for which much of the useful insights are sometimes hidden and untapped. Many attempts have been made in the last 10-20 years to apply systematic methodologies in order to build knowledge discovery and management applications. However, establishing and managing a real-world data mining project is not a trivial task. This is simply due to the fact that majority of industries (life sciences, manufacturing, insurance, telecommunications, banking, etc.) have gone through an evolving paradigm.
Today’s knowledge discovery from data can be classified in several ways: (i) data mining on engineered systems (e.g. complex equipment) or systems designed by nature (e.g. life sciences), (ii) explanatory or predictive data mining, (iii) data mining from static data (e.g. data warehouse) or dynamic data (e.g. data streams), (iv) user operated or automated data mining. There could still be other ways to classify data mining applications. In this talk, we will first provide an overview of data in the real world and some challenges that knowledge discovery projects have encountered. We will then briefly present a number of case studies that cover various aspects of knowledge discovery applications. Actual scientific and business examples used in this talk will illustrate proven case studies designed, implemented and evaluated by domain experts. We also demonstrate how our case studies can lead to real world applications and even tools that could be deployed for better management of data from today’s data rich environments.

Dr. Fazel Famili is a Principal Research Scientist and a leading data mining expert working at the National Research Council of Canada (NRC), where he has been working for the past 29 years. Prior to joining NRC, he worked in industry for 3 years. Dr. Famili has been actively involved in the field of Artificial Intelligence, Data Mining and Bioinformatics and successful application of these technologies. He has a strong data mining and bioinformatics team within NRC that is currently engaged in unique research and development in data mining for Life Sciences. His research has been on data mining, machine learning and bioinformatics and their applications to real world problems in various data rich environments, such as semiconductor manufacturing, aerospace and life sciences. Dr. Famili has edited two books, has published over 50 articles in the area of data mining and AI and has a US data mining patent. He has organized several workshops and has been involved in a number of data mining and AI conferences (e.g. ECAI, ECML/PKDD, AAAI, ICML, and KDD) and has extensive collaboration with a number of Institutes in Canada, Europe, Far East and South America. He is an adjunct professor at the School of Electrical Engineering and Computer Science, at the University of Ottawa, Canada.