VLDB 2010 , 36th International Conference on Very Large Data Bases
  Singapore : 13 to 17 Sept 2010, Grand Copthorne Waterfront Hotel
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Tutorial 6

Prof. S. (Muthu) Muthukrishnan

The speaker S. (Muthu) Muthukrishnan is a Professor in Rutgers University and a Research Scientist at Google.
Muthu's research interest is in databases and algorithms, recently on data stream management and in algorithms for Internet ad systems.

New systems produce data that often present new challenges for data management and mining problems. For example, inventory and sales data led to emphasis on data mining problems such as association rule mining; analysis of Internet Packet traffic logs led to data stream management systems; and, growing markup publication systems led to challenges addressed by semi-structured data management.

In this tutorial, we are inspired by systems that have emerged in the past decade that enable advertisements (ads) on the Internet.  Such Internet ad systems handle billions of transactions every day involving millions of users, websites and advertisers, and are the basis for billions of dollars worth industry.  They crucially rely on real-time collection, management and analysis of data for their effectiveness. Further, they represent unusual challenges for data analysis: nearly all parties in Internet ad systems from marketeers to publishers use active, selfish strategies that both help generate new data as well as distort data produced due to their selfish strategies. Mining such data while cognizant of the inherent game theory is a great research challenge. Finally, Internet ad systems use Information Retrieval, Auction and Game Theory, Machine Learning and Optimization Algorithms, and data analysis systems have to be compatible with these methods.

The tutorial will provide an overview of Internet ad systems and discuss in detail both data management as well as data mining tasks that arise: In the first part, we will describe different Internet ad systems and discuss issues in managing the data that arises in them as well as various tools. The second part will be on the data mining problems that arise, many unique to these systems.

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Last modified on 21 Jul 2010