SIGKDD Annual Report

July 2002 - June 2003
Submitted by: Won Kim ,SIGKDD Chair

1. significant awards given out

In 2002, SIGKDD gave out SIGKDD Annual Services Award (Ramasamy Uthurusamy), and SIGKDD Innovations Award (Jerry Friedman).

2. significant papers on new areas that were published in the Proceedings

The KDD-2002 conference accepted 32 full papers out of 270 submissions to the Regular Track, and 12 full papers out of 37 submissions to the IT Track.

Additional 44 papers were selected for poster presentation. Accepted papers dealt with KDD algorithms, mining of the Web, and interactive knowledge exploration, as well as KDD applications such as e-commerce and telephony.

3. significant programs that provded a springboard for further technical efforts

The KDD-2002 conference included 6 tutorials and 6 workshops. The workshops were on data mining in bioinformatics, web mining, multimedia data mining, multi-relational data mining, temporal data mining, and fractals in data mining. The tutorials were on text mining for bioinformatics, querying and mining data streams, link analysis, multivariate density estimation, common reasons data mining projects fail, and visual data mining.

4. Innovative programs which provide service to some part of our technical community

The SIGKDD semi-annual newsletter Explorations have been made available online.

5. A very brief summary for the key issues that the membership of SIGKDD will have to deal with in the next 2-3 years

This year data mining has unfortunately, and wrongly, become associated with "invasion of privacy and civil liberties". This has come about because of the labeling in the news media of the DARPA Total Information Awareness Initiative as a "massive data mining project" on a huge centralized database of all things about all people in the US. The SIGKDD EC has issued a letter that refutes this misunderstanding and mislabeling. Continued efforts may be needed to ensure that data mining does not get wrongly equated with technology being used to violate civil liberties of innocent citizens. At the same time, additional research efforts may need to be focused on the newly emerging area of "privacy preserving" data mining.

Prediction-Serving Systems

ACM Queue’s “Research for Practice” is your number one resource for keeping up with emerging developments in the world of theory and applying them to the challenges you face on a daily basis. In this installment, Dan Crankshaw and Joey Gonzalez provide an overview of machine learning server systems. What happens when we wish to actually deploy a machine learning model to production, and how do we serve predictions with high accuracy and high computational efficiency? Dan and Joey’s curated research selection presents cutting-edge techniques spanning database-level integration, video processing, and prediction middleware. Given the explosion of interest in machine learning and its increasing impact on seemingly every application vertical, it's possible that systems such as these will become as commonplace as relational databases are today. 

ACM Case Studies

Written by leading domain experts for software engineers, ACM Case Studies provide an in-depth look at how software teams overcome specific challenges by implementing new technologies, adopting new practices, or a combination of both. Often through first-hand accounts, these pieces explore what the challenges were, the tools and techniques that were used to combat them, and the solution that was achieved.

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