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Mar, 1st '10:: VLDB Abstract

Mar, 9th, '10 :: VLDB Paper

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Communication

Venkatesh Raghavan,
c/o Computer Science Dept. Worcester Polytechnic Institute,
100 Institute Road, Worcester, MA 01609.

Email: venky AT wpi DOT edu

Education

Research Interests

Curriculum Vitae

Research Projects

I am a member of the Database Systems Research Group (DSRG) @ WPI. In addition, I am an active contributor to SkyTeam (Pareto-Optimal Query Processing), CAPE (Continuous Stream Processing Engine) and MASS (Native XML Database) projects.

Multi-Criteria Decision Support Systems

SkyDB: Skyline Aware Query Evaluation Framework

Recent years much attention has been focused in evaluating skylines. However, existing techniques focus on skyline algorithms over single sets. These techniques face two serious limitations, namely one, they define skylines to work on a single set only, and two, they treat skylines as an ``add-on", loosely integrated on top of the query plan. In this work, we investigate the evaluation of skylines over disparate sources via joins. We then propose SkyDB - a skyline aware query evaluation framework that addresses four key issues that enable the treatment of skylines as a first-class citizen in query processing. First, we extend the relational model to include skyline-aware operators. Second, we design query processing strategies that are tuned to exploit the skyline knowledge. Third, we propose our skyline aware query optimizer to effectively choose between the implementation strategies. Evaluating of skylines over joins is considered to be {\it blocking}. Therefore, and existing algorithms focus only on reducing the skyline computation time making them are not applicable for response-time sensitive applications such as fire-monitoring systems. Lastly, we aim to transform the execution of skylines over joins to be non-blocking by making SkyDB support the progressive output of results. Our preliminary experimental study demonstrates the superiority of our proposed methodologies over existing methods by outperforming them in many cases by several orders of magnitude.

  1. V. Raghavan and Elke Rundensteiner, Progressive Result Generation for Multi-Criteria Decision Support Queries, 26th International Conference on Data Engineering, (ICDE) 2010, (Acceptance: 12.5%), To appear.
  2. V. Raghavan and Elke Rundensteiner, SkyDB: Skyline Aware Query Evaluation Framework, In Proceedings of the SIGMOD PhD Workshop on Innovative Database Research, (IDAR) 2009.

Data Stream Management System

FireStream: Sensor Stream Processing Framework to Monitor Fire Spread

Designed a sensor stream-processing framework, which provides services for run-time monitoring and visualization of fire-spread for fire-engineers. Handled heterogeneous sensor types for analysis; determines spatial orientation between pertinent sensors; and manipulated sensor parameters, such as sensitivity, sampling frequency etc., for sensors adjacent to the estimated fire events. Provided ambient condition assessment, initial sensor event detection and moving fire spread tracking require querying across hundreds of sensors.

  1. V. Raghavan, Y. Zhu, E. A. Rundensteiner, and D. Dougherty, Multi-Join Continuous Query Optimization: Covering the Spectrum of Linear, Acyclic and Cyclic Queries, British National Conference on Databases (BNCOD) 2009, (pdf).
  2. A. Mukherji, E. A. Rundensteiner, D. Brown and V. Raghavan, SNIF TOOL: Sniffing for Patterns in Continuous Streams, pp. 369-378, Conference on Information and Knowledge Management (CIKM) 2008 (pdf).
  3. V. Raghavan, E. A. Rundensteiner, J.P. Woycheese and A. Mukherji, FireStream: Sensor Stream Processing for Monitoring Fire Spread, International Conference on Data Engineer- ing, pp. 1507-1508 (ICDE) 2007 (pdf).

VAMANA - A Scalable Cost Driven XPath Engine

VAMANA is an XPath Query Engine designed specifically for evaluating complete XPath query expressions using MASS. MASS can efficiently retrieve nodes for one location-step. An XPath is made-up of several location-step(s). VAMANA's XPath Processor translates the input XPath expression into a default execution tree. VAMANA's XPath Engine has two parts namely the Optimizer, which tries to find the optimal execution tree (ET) and a Execution Engine that executes ET over MASS to return node-sets to the XQuery Engine.

  1. V. Raghavan, K.W. Deschler, E. A. Rundensteiner, VAMANA - A Scalable Cost-Driven XPath Engine, International Workshop on XML Schema and Data Management (XSDM) held in conjunction with ICDE 2005 (pdf).

Engineering Data for Fire Science (EDaFS)

A NIST grant to the Fire Protection Engineering, EDaFS. Experimental Data for Fire Science (EDaFS) is a portal for the fire science community. Designed and developed the architecture for accessing and managing fire test data. Developed dynamic loading for the VRML world based on the experiments the user wants to render. Built supports to manage data of multiple types (XML, CSV, Oracle). Developed user interface and graphing tools required to compare different test data. EDaFS website

Publications

Conference, Workshop and Demonstration Publications:

  1. V. Raghavan and Elke Rundensteiner, Progressive Result Generation for Multi-Criteria Decision Support Queries, 26th International Conference on Data Engineering, (ICDE) 2010, (Acceptance: 12.5%), To appear.
  2. V. Raghavan and Elke Rundensteiner, SkyDB: Skyline Aware Query Evaluation Framework, In Proceedings of the SIGMOD PhD Workshop on Innovative Database Research, (IDAR) 2009.
  3. V. Raghavan, Y. Zhu, E. A. Rundensteiner, and D. Dougherty, Multi-Join Continuous Query Optimization: Covering the Spectrum of Linear, Acyclic and Cyclic Queries, British National Conference on Databases (BNCOD) 2009, (pdf).
  4. A. Mukherji, E. A. Rundensteiner, D. Brown and V. Raghavan, SNIF TOOL: Sniffing for Patterns in Continuous Streams, pp. 369-378, Conference on Information and Knowledge Management (CIKM) 2008 (pdf).
  5. V. Raghavan, E. A. Rundensteiner, J.P. Woycheese and A. Mukherji, FireStream: Sensor Stream Processing for Monitoring Fire Spread, International Conference on Data Engineer- ing, pp. 1507-1508 (ICDE) 2007 (pdf).
  6. V. Raghavan, K.W. Deschler, E. A. Rundensteiner, VAMANA - A Scalable Cost-Driven XPath Engine, International Workshop on XML Schema and Data Management (XSDM) held in conjunction with ICDE 2005 (pdf).

Posters:

  • V. Raghavan, S. Srivastava, and E. Rundensteiner, SkyDB: Skyline-Aware Query Evaluation Framework, NEDBDay'09
  • V. Raghavan and E. A. Rundensteiner, Multi-Join Continuous Query Plan Generation: A Qualified Plan Generation Problem, NEDBDay'08
  • V. Raghavan, E. A. Rundensteiner, J. P. Woycheese and A. Mukherji,FireStream: Sensor Stream Processing for Monitoring Fire Spread. Graduate Research Achievement Day (GRAD @ WPI) 2007

    • Technical Documents:

    1. T. Ranadive and V. Raghavan, Beginner's Document For CAPE, June 2007.
    2. V.Raghavan, How To Make PDF Images With Ease, March 2007.
    3. V. Raghavan, M. Kim and J. P. Woycheese, Experiment Data for Fire Science, Database Architecture 0.9.1, Submitted to National Institute of Standards and Technology (NIST), August 2004.