Invited Research Talk 1
 
 Small Device Data Management
  Rajkumar Sen
 (IIT
Bombay and Sybase India)

ABSTRACT:
Lightweight computing devices are becoming ubiquitous and increasing number of applications are being developed for these devices. Many applications e.g E­governance, healthcare

etc. need to deal with a significant amount of data. The queries are quite complicated involving selctions, joins and aggregates. Data processing on a handheld device involves many complex issues. Applications like Microbanking involve transactional details. Hence, atomicity and durablity properties need to be satisfied. All such issues necessitate the presence of a local database management system on the device. Also, a DBMS will make application code development easy and fast. However, scaling down the database has not been easy as these devices are constrained by limited stable storage and main memory. Resources in handhelds are increasing with every new device; optimum utilization of these limited resources is a must for such a database system. Evaluation of complex joins and aggregates requires efficient usage of the limited main memory. Memory should be optimally allocated among the database operators. We present in this talk a new data storage model, ID Based Storage which reduce storage cost considerably. We present an exact and a heuristic method for optimally allocating memory among the database operators and compare both the methods. Our query processing strategy guarantees the best query execution plan for any handheld device depending on the available resources. Our lightweight DBMS for handheld devices has been tested on the Simputer.  

 

 

SPEAKER BIO:
Rajkumar Sen received his MTech degree from Dept. of Computer Science & Engineering, IIT Bombay, in 2003. The work presented here is part of his master’s thesis and will appear in the forthcoming IEEE ICDE 2005 conference.

  

Invited Research Talk 2
 
 Privacy-Preserving Data Mining
  Shipra Agrawal
 (IISc Bangalore and
Bell Labs India)

ABSTRACT:
To preserve client privacy in the data mining process, a variety of techniques based on random perturbation of individual data records have been proposed recently.  In this talk, we present FRAPP, a generalized matrix-theoretic framework of random perturbation, which facilitates a systematic approach to the design of perturbation mechanisms for privacy-preserving mining.  Specifically, FRAPP is used to demonstrate that (a) the prior techniques differ only in their choices for the perturbation matrix elements, and (b) a symmetric perturbation matrix with minimal condition number can be identified, maximizing the accuracy even under strict privacy guarantees. We also propose a novel perturbation mechanism wherein the matrix elements are themselves characterized as random variables, and demonstrate that this feature provides significant improvements in privacy at only a marginal cost in accuracy.

 

 

SPEAKER BIO:
Shipra Agrawal graduated with an ME degree from Dept. of Computer Science & Automation, Indian Institute of Science, Bangalore, in July 2004. She is currently employed with Bell Labs India. The work presented here is part of her master’s thesis and will appear in the forthcoming IEEE ICDE 2005 conference.