Industry Practice Expo

Industry Practice Expo track will comprise of technical invited talks and panel discussions / debates by leading experts in the world of applied data mining and knowledge discovery. The expo will feature highly influential speakers who have directly contributed to successful data mining applications in their respective fields. The talks and discussions will focus on innovative and leading-edge, large-scale industry or government applications of data mining in areas such as finance, health-care, bio-informatics, public policy, infrastructure (transportation, utilities, etc.), telecommunications, social media, and computational advertising. (IPE in KDD 2011)

The objective of the Industry Practice Expo track is to bring together leading industry and government practitioners to share their insights and experiences will inspire the KDD community and spread awareness of the variety of seminal, innovative, and proven applications of data mining and knowledge discovery in the industry and government. This track will complement the already established Industry and Government track at KDD that focuses on peer reviewed publications.

Confirmed Speakers (More In Progress)

Rich Holada: Maximizing Return and Minimizing Cost with the Right Decision Management Systems
Yong Shi: China's National Personal Credit Scoring System：A Real-life KDD and Intelligent Knowledge Application
Graham Williams: Ensembles and Model Delivery for Tax Compliance

TITLE: Maximizing Return and Minimizing Cost with the Right Decision Management Systems
SPEAKER: Rich Holada, Vice President, Predictive Analytics, IBM Software Group

ABSTRACT: The ability to achieve operational efficiency, product leadership and customer intimacy still eludes many organizations due, in large part, to the chaos of business. Inconsistent prioritization and decision making; poor visibility between systems; processes that are not well controlled; and individual front-line decisions that seem small but, in totality, have a huge impact make it difficult for organizations to link strategy to execution and back. During this presentation, we will demonstrate how automating and optimizing decisions (operational efficiency) with business rules and predictive models enables better data driven results across the enterprise, and how this is implemented at the point of impact (customer intimacy) to transform an organization and support market leadership.

Rich Holada heads IBM’s predictive analytics group, which includes the SPSS line of software and solutions. Holada is responsible for setting the strategic and technological direction of the group, as well as overseeing its day-to-day operations, from global product development to sales and marketing. With Business Analytics as a key growth area for IBM, Holada is focused on helping customers solve real-world business problems through predictive analytics to create a Smarter Planet.

Previously, Holada was CTO of predictive analytics that included global responsibility for product marketing, product strategy, research and development, and technical support for the IBM SPSS predictive analytics brand. He joined SPSS, prior to its acquisition by IBM, in November 2006 as Senior Vice President of Research & Development, bringing with him nearly 20 years of deep software research and development background and diverse experience in the technology industry.

Previously, Holada has held senior level posts with Oracle Corporation and PeopleSoft, Inc., both leading CRM technology organizations. At PeopleSoft, Inc. he was the senior technical executive responsible for creating the industry-leading PeopleSoft CRM product offering.

Holada has also held senior research and development positions at Trimark Technologies, Inc., where he transformed the firm from a consulting operation to a software product company, as well as at Intelligent Trading Systems, Inc. and Sun Microsystems, Inc.

Holada received his B.S. in Computer Science from University of Illinois, Champaign, and his Juris Doctorate from John Marshall Law School in Chicago, graduating cum laude with honors.

TITLE: Ensembles and Model Delivery for Tax Compliance
SPEAKER: Graham Williams, Director of Data Mining, Australian Taxation Office

ABSTRACT: Revenue authorities characteristically have a large stores of historic audit data, with outcomes, ready for analysis. The Australian Taxation Office established one of the largest data mining teams in Australia in 2004 as a foundation to becoming a knowledge-based organisation. Today, every tax return lodged in Australia is risk assessed by one or more models developed through data mining, generally based on historic data. We observe that any of the traditional modelling approaches, particularly including random forests, generally deliver similar models in terms of accuracy. We take advantage of combining different model types and modelling approaches for risk scoring, and in particular report on recent research that increases the diversity of trees that make up a random forest. We also review, in a practical context, how such models are evaluated and delivered.

Dr Graham Williams is Director of Data Mining at the Australian Taxation Office, and previously Principal Computer Scientist for Data Mining with CSIRO. He is a Senior International Expert and Visiting Professor of the Chinese Academy of Sciences at the Shenzhen Institutes of Advanced Technologies, and Adjunct Professor in Data Mining, Fraud Prevention, Security, at the University of Canberra and Australian National University. Graham is an active machine learning researcher and regularly teaches data mining courses. He is author of the freely available Rattle software for data mining and of the Rattle book published by Springer in 2011: Data Mining with Rattle and R: The Art of Excavating Knowledge from Data. Graham has been involved in data mining projects for clients from government and industry for over 25 years. His research developments include ensemble learning (1988) and hot spots discovery (1997). He is involved in numerous international artificial intelligence and data mining research activities and conferences and has editted a number of books and has authored many academic and industry papers. He is chair of the Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD), and the Australasian Conference on Data Mining (AusDM). His passion is in ensuring data mining technology is readily accessible and available to all who wish, supporting innovation and the sharing of our knowledge widely.

TITLE: China’s National Personal Credit Scoring System：A Real-life KDD and Intelligent Knowledge Application
SPEAKER: Yong Shi, Executive Deputy Director, Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences/Strategic Advisor, China Data Technology (Suzhou) Co., Ltd

ABSTRACT: Credit Reference Centre (CRC) of People’s Bank of China (PBC) has built a big data: the largest personal credit database in the world with 800 million people’s accounts collected from all commercial banks in China since 2003. From June 2006 to Sept 2009, Research Centre on Fictitious Economy and Data Science, Chinese Academy of Sciences (CASFEDS) and CRC jointly developed China’s National Personal Credit Scoring System, known as “China Score”, which is a unique and advanced KDD application under intelligent knowledge management on this big data. The system will be eventually serving all 1.3 billion population of China for their daily financial activities, such as bank accounts, credit card application, mortgage, personal loans, etc. It can become one of the most influential events of KDD techniques to human kind. This talk will introduce the key components of China Score project that includes objectives, modeling process, KDD techniques used in the projects, intelligent knowledge management and experience of the project development. In addition, the talk will also outline a number of policy recommendations based on China Score project which has been potentially impacting Chinese Government on its strategic decision making for China’s economic developments.

Dr Yong Shi a Senior Member of IEEE, serves as the Executive Deputy Director, Chinese Academy of Sciences Research Center on Fictitious Economy & Data Science, China, since 2007. He has been the Charles W. and Margre H. Durham Distinguished Professor of Information Technology, College of Information Science and Technology, Peter Kiewit Institute, University of Nebraska, USA from 1999 to 2004. Dr. Shi's research interests include business intelligence, data mining, and multiple criteria decision making. He has published more than 17 books, over 200 papers in various journals and numerous conferences/proceedings papers. He is the Editor-in-Chief of International Journal of Information Technology and Decision Making (SCI), and a member of Editorial Board for a number of academic journals. Dr. Shi has received many distinguished awards including the Georg Cantor Award of the International Society on Multiple Criteria Decision Making (MCDM), 2009; Fudan Prize of Distinguished Contribution in Management, Fudan Premium Fund of Management, China, 2009; Outstanding Young Scientist Award, National Natural Science Foundation of China, 2001; and Speaker of Distinguished Visitors Program (DVP) for 1997-2000, IEEE Computer Society. He has consulted or worked on business projects for a number of international companies in KDD and intelligent knowledge management.