Tutorial:
Tutorial
1:
"Storage
and Retrieval of XML Data using Relational Databases"
Tutorial
Abstract
|
The Extensible Markup Language
(XML) is becoming the dominant standard for exchanging data over World Wide
Web. Due to its flexibility, XML is rapidly emerging as the de facto standard
for exchanging information for the next generation web applications.
XML documents can be stored
and queried by using specialized semistructure repositories. Such an approach
does not allow us to be able to use the features of the state-of-the-art
relational database technology. Not only that, large volumes of enterprise
data available today exist only in relational database systems. Therefore,
efficient storage and retrieval of native XML data using existing Outline |
About
the Speakers
|
Dr. Kyuseok Shim is an Assistant Professor at Seoul National University in Korea. Previously, he was an Assistant Professor at KAIST in Korea and a Member of Technical Staff at Bell Laboratories. He was one of the key contributors to the Serendip data mining project in Bell Laboratories. Before that, he worked for Quest Data Mining project at IBM Almaden Research Center and contributed to IBM Intelligent Miner for Data. He received B.S. degree in Electrical Engineering from Seoul National University in 1986, and the MS and Ph.D. degrees in Computer Science from University of Maryland, College Park in 1988 and 1993, respectively. Kyuseok has been working in the area of data mining and databases. He has published several research papers in prestigious conferences and journals. He has served as a program committee member on ACM SIGKDD, ACM SIGMOD, ICDE, PAKDD, and VLDB conferences. He is also currently on the Editorial Board of VLDB and KAIS journals. Dr. Surajit Chaudhuri
is a senior researcher and manager of the Data Management, Exploration
and Mining Group at Microsoft Research. He has worked extensively in the
area of self-tuning database technology, query processing, data warehousing
and data mining on SQL systems. He has published many papers in leading
database conferences and journals. His work on self-tuning database technology
and data mining has been incorporated in the Microsoft SQL Server product.
Surajit has been a |
Tutorial
2:
"Data Analytics for Customer Relationship Management"
Tutorial
Abstract
|
Corporations across the world
are recognizing that intimate, one-to-one relationships with their customers
are critical for survival in the increasingly global and competitive marketplace.
The ones which are proactive and quick footed, have taken the initiative
to implement a Customer Relationship Management (CRM) system that integrates
every area of business that touches the customer - namely marketing, sales,
and customer service - by coordinating people, internal processes and
technology. The tremendous leaps in storage and computational power have made Data Analytics emerge as a powerful business tool that unleashes the power in your data across the organization for better decision making. Data Analytics combines data warehousing, data mining and mathematical modeling concepts to decipher previously unknown, actionable information from business data. Because the basis of data analytics is data - the facts about what has already happened in the organization - data analytics enables the organization to leverage the experience to make better decisions today. This tutorial provides an up-to-date introduction to the increasingly important field of "Analytical CRM", whose goal is provide a quantitative basis for making CRM decisions - thus leading the transition from customer relationship as an art to a science. Participant Profile This tutorial will help participants understand the key architectural and design issues related to building a data warehouse, data mining, and building and deploying analytical models. Two categories of people will benefit from this tutorial: (i) Industry practitioners,
including Chief Information Officers, Project Managers, Business Analysts,
and Technical Architects who would be involved in integrating data analytic
tools with customer relationship management. Key Benefits Tutorial Syllabus |
About
the Speaker
|
Jaideep Srivastava received his B.Tech. from the Indian Institute of Technology, Kanpur, India, in 1983, and M.S. and Ph.D. from the University of California - Berkeley in 1985 and 1988, respectively. Since 1988 he has been on the faculty of the University of Minnesota, where is a Professor. For over 15 years he has been active as a researcher, educator, and consultant in the areas of databases, data mining, and multimedia. He has established and led a database and multimedia research laboratory, where 16 people have received their doctorate and 37 people have received their masters. Over half of the Ph.D.s have gone on to become faculty members, both nationally and internationally. Throughout his career Dr. Srivastava has had an active collaboration with the industry, both for collaborative research and technology transfer. Specifically, he has collaborated with Honeywell, IBM, Fujitsu, and Apertus/Carleton for research purposes. In addition, he has been active in transferring technology to the Army, Air Force, and Minnesota Department of Transportation. Between 1999 and 2001 Dr. Srivastava was on leave from the University of Minnesota, during which period he has spent time at Amazon.com (www.amazon.com) as the Chief Data Mining Architect, and at Yodlee Inc. (www.yodlee.com) as Director of Data Analytics. This wide-ranging industry experience has provided Dr. Srivastava a unique perspective on the application of various computer science ideas in the industry. Dr. Srivastava is an often-invited
participant in technical as well as technology strategy forums. He has
given more than a hundred talks in various industry, academic, and government
forums. He has organized and served on the program committee of a number
of conferences. He is currently an associate editor for the IEEE Transactions
on Knowledge & Data Engineering and a guest editor for the Data Mining
& Knowledge Discovery Journal. The federal government has solicited
his opinion on computer science research as an expert witness. He has
served in an advisory role to the governments of India and Chile on various
software technologies. A sample of Dr. Srivastava's research work is available
at |
Tutorial
3:
"Data
Clustering Analysis, from Simple Groupings to Scalable Clustering with Constraints"
Tutorial
Abstract
|
Cluster analysis is the automatic
identification of groups of similar objects. There have been many works
on cluster analysis but we are now witnessing a significant resurgence of
interest in new clustering techniques. Indeed, discovering distributions
of patterns in either numerical or categorical data is relevant for many
application domains. Scalability and high dimensionality are not the only
focus of the recent research in clustering analysis. Indeed, it is getting
difficult to keep track of all the new clustering strategies, their advantages
and shortcomings.
This tutorial surveys clustering
- what it is, its utility, the various approaches to cluster and outlier
discovery - with a particular focus on the important recent advances in
the field. Clustering is an unsupervised classification process that is
fundamental to data mining. Many data mining queries are concerned either
with how the data objects are grouped or which objects could be considered
remote from natural groupings. We discover this through clustering which
attempts to group data objects by maximizing inter-group similarity and
minimizing intra-group similarity. The various approaches to clustering,
their basic concepts, principles and assumptions as well as their efficiency
and effectiveness in practice |
About
the Speakers
|
Osmar R. Zaiane, now Assistant Professor at the University of Alberta, Canada, received a Masters degree in Electronics (DEA) from the University of Paris XI, France, and another Masters in Computer Science from Laval University, Canada. He received his Ph.D. from Simon Fraser University under the supervision of Dr. Jiawei Han. His Ph.D. work concentrated on data mining from the Web and multimedia repositories. He has published more than 40 papers in international conferences and journals. Osmar Zaiane was the co-chair of the First and Second International Workshop on Multimedia Data Mining (MDM/KDD) held in conjunction with ACM SIGKDD 2000 and 2001, and guest editor for the Journal of Intelligent Information Systems. He has been an ACM Member since 1986. Andrew Foss, has a BA and MA in Physics from Oxford University and is currently a graduate student in computing science at the University of Alberta specializing in Clustering. He has his own successful software development company and has published in astronomy and computer science. He has developed several new clustering algorithms in association with Dr. Zaiane. |