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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

CV Wordle
Figure: Wordle of My Resume

Publications

Conference, Workshop, Demonstration and In-Submission Publications:

  1. C. Garcia-Alvarado, V. Raghavan, S. Narayanan, F. Waas, ``Automatic Data Placement in MPP Databases," to appear in ICDE Workshop (SMDB 2012) .
  2. S. Srivastava, V. Raghavan, and E. A. Rundensteiner, ``Adaptive Processing of Multi-Criteria Decision Support Queries," to appear in VLDB Workshop (BIRTE 2011) .
  3. V. Raghavan, E. A. Rundensteiner, and S. Srivastava, ``Skyline and Mapping Aware Join Query Evaluation,'' to appear in Information Systems (an Elsevier Journal).
  4. V. Raghavan and E. A. Rundensteiner, "Progressive Result Generation for Multi-Criteria Decision Support Queries," 26th International Conference on Data Engineering, (ICDE) 2010, (Acceptance: 12.5%), (pdf), (slides).
  5. V. Raghavan, and E. A. Rundensteiner, "ProgXe: Progressive Result Generation Framework for Multi-Criteria Decision Support Queries," Dem onstration, SIGMOD 2010, (Acceptance Rate: 36.8%), (pdf).
  6. M. Vartak, V. Raghavan, and E. A. Rundensteiner, ``QRelX: Generating Meaningful Queries that Provide Cardinality Assurance,'' Demonstration, SIGMOD 2010, (Acceptance Rate: 36.8%), (pdf).
  7. Y. Zhu, V. Raghavan and E. A. Rundensteiner, ``A New Look At Generating Multi-Join Continuous Query Plans: A Qualified Plan Generation Problem,'' Data and Knowledge Engineering (DKE Journal), (pdf).
  8. N. Park, V. Raghavan, and E. A. Rundensteiner, ``Supporting Multi-Criteria Decision Support Queries Over Time-Interval Data Streams,'' 21st International Conference of Database and Expert System Applications, (DEXA) 2010.
  9. V. Raghavan and Elke Rundensteiner, "SkyDB: Skyline Aware Query Evaluation Framework," In Proceedings of the SIGMOD PhD Workshop on Innovative Database Research, (IDAR) 2009, (pdf).
  10. 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).
  11. 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).
  12. V. Raghavan, E. A. Rundensteiner, J.P. Woycheese and A. Mukherji, "FireStream: Sensor Stream Processing for Monitoring Fire Spread," International Conference on Data Engineering, pp. 1507-1508 (ICDE) 2007 (pdf).
  13. 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).
  14. V. Raghavan, M. Vartak, and E. A. Rundensteiner, `` Query Oriented Refinement for Cardinality Assurance,'' in submission.

Posters:

  • V. Raghavan and E. A. Rundensteiner, Progressive Result Generation for Multi-Criteria Decision Support Queries, NEDBDay '10
  • V. Raghavan, S. Srivastava, and E. A. 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

    • Other Technical Documents:

    1. T. Ranadive and V. Raghavan, Beginner's Document For CAPE, June 2007.
    2. 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.

    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

    ProgXe: Progressive Evaluation of Multi-Criteria Decision Queries

    Multi-criteria decision support (MCDS) applications need to report results early to formulate competitive decisions in near real-time. Designed and implemented ProgXe -- a progressive execution methodology that transforms ``blocking'' queries into ``non-blocking'' by returning results as they are found. Proposed output-aware join ordering techniques that maximizes responsiveness and reduces total execution time. ProgXe exhibits superior performance in many cases by 2-4 orders of magnitude and is robust for all distributions, cardinalities and join factors.

    1. V. Raghavan and E. A. Rundensteiner, "Progressive Result Generation for Multi-Criteria Decision Support Queries," 26th International Conference on Data Engineering, (ICDE) 2010, (Acceptance: 12.5%), (pdf), (slides).
    2. V. Raghavan, and E. A. Rundensteiner, "ProgXe: Progressive Result Generation Framework for Multi-Criteria Decision Support Queries," Demonstration, SIGMOD 2010, (Acceptance Rate: 36.8%), (pdf).

    TI-Sky: Multi-Criteria Decision Support Queries Over Time-Interval Streams

    Pareto-optimal query evaluation is computationally intensive over continuous time-interval data streams where each object has its own customized expiration time. Designed and implemented TI-Sky -- an efficient framework to support continuous skyline evaluation. TI-Sky strikes a perfect balance between the costs of continuously maintaining the changing result space and the costs of computing the results whenever a pull-based query is received. TI-Sky incrementally maintains the partially precomputed result space -- doing so efficiently by working at a higher level of abstraction. In comparison to current approaches TI-Sky has a performance improvement of up to 2 folds.

    1. N. Park, V. Raghavan, and E. A. Rundensteiner, ``Supporting Multi-Criteria Decision Support Queries Over Time-Interval Data Streams,'' 21st International Conference of Database and Expert System Applications, (DEXA) 2010.

    SkyDB: Skyline Aware Query Evaluation Framework

    Real-world decision support applications are required to access data from disparate sources, while existing techniques define the skyline operation to work on a single set, and therefore, treat skylines as an ``add-on" on top of a traditional query plan. Implemented SkyDB -- a novel approach to support multi-criteria decision (Pareto-optimal) queries over joins. Designed and implemented execution strategies that leverage mature DBMS technology by treating the skyline (Pareto-Optimal) operation as a first-class citizen in query processing. Proposed techniques produce on an average 50% fewer number of join results in comparison to state-of-the-art techniques and achieves 1-2 orders of magnitude fewer skyline comparisons in producing the final result. Implemented adaptive spatial reasoning to support queries over high dimensional scientific data.

    1. S. Srivastava, V. Raghavan, and E. A. Rundensteiner, ``Adaptive Processing of Multi-Criteria Decision Support Queries," to appear in VLDB Workshop (BIRTE 2011) .
    2. V. Raghavan, E. A. Rundensteiner, and S. Srivastava, ``Skyline and Mapping Aware Join Query Evaluation,'' to appear in Information Systems (an Elsevier Journal).
    3. V. Raghavan and E. A. Rundensteiner, "SkyDB: Skyline Aware Query Evaluation Framework,In Proceedings of the SIGMOD PhD Workshop on Innovative Database Research,(IDAR) 2009, (pdf).

    QRelX: A Novel Approach to Query Cardinality Assurance

    Database users often follow a trial-and-error approach to find queries with the desired query cardinality. Designed {\it QRelX} -- a novel framework to auto-generate alternate queries that maintain closeness to the original user query while satisfying cardinality constraints. Developed an incremental cardinality estimation model with spatial reasoning technique to reduce search complexity.

    1. M. Vartak, V. Raghavan, and E. A. Rundensteiner, ``QRelX: Generating Meaningful Queries that Provide Cardinality Assurance,'' Demonstration, SIGMOD 2010 To Appear, (Acceptance Rate: 36.8%), (pdf).
    2. V. Raghavan, M. Vartak, and E. A. Rundensteiner, ``Query Oriented Refinement for Cardinality Assurance ,'' in submission.

    Data Stream Management System

    Constraint-Exploiting Adaptive Stream Processing Engine (CAPE)

    ``Multi-Resource Constraint Query Optimization"-- proposed techniques that generate robust query plans that are adherent to both CPU and memory restrictions. Proposed polynomial time techniques to generate ``{\it near-optimal}'' continuous query plans. Contributor to the stream engine CAPE, developed at WPI. CAPE Website.

    1. Y. Zhu, V. Raghavan and E. A. Rundensteiner, ``A New Look At Generating Multi-Join Continuous Query Plans: A Qualified Plan Generation Problem,'' Data and Knowledge Engineering (DKE Journal), (pdf).
    2. 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).

    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. 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).
    2. V. Raghavan, E. A. Rundensteiner, J.P. Woycheese and A. Mukherji, "FireStream: Sensor Stream Processing for Monitoring Fire Spread," International Conference on Data Engineering, 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. Developed a scalable XPath Engine to support large (> 1 Gb) XML files and all 13 XPath axes. The proposed index-oriented query plans allow queries to be evaluated while reading only a fraction of data rather than expensive structural join operations. Designed cost model to support ad-hoc XPath expressions.

    1. V. Raghavan, K.W. Deschler, and 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.