Optimizing the execution of multiple data analysis queries on parallel and distributed environments

  • Andrade H
  • Kurc T
  • Sussman A
 et al. 
  • 10


    Mendeley users who have this article in their library.
  • 13


    Citations of this article.


Abstract--This paper investigates techniques for efficiently executing multiquery workloads from data and computation-intensive applications in parallel and/or distributed computing environments. In this context, we describe a database optimization framework that supports data and computation reuse, query scheduling, and active semantic caching to speed up the evaluation of multiquery workloads. Its most striking feature is the ability of optimizing the execution of queries in the presence of application-specific constructs by employing a customizable data and computation reuse model. Furthermore, we discuss how the proposed optimization model is flexible enough to work efficiently irrespective of the parallel/distributed environment underneath. In order to evaluate the proposed optimization techniques, we present experimental evidence using real data analysis applications. For this purpose, a common implementation for the queries under study was provided according to the database optimization framework and deployed on top of three distinct experimental configurations: a shared memory multiprocessor, a cluster of workstations, and a distributed computational Grid-like environment.

Author-supplied keywords

  • Cluster computing
  • Data analysis applications
  • Grid computing
  • Multiquery optimization
  • Parallel databases
  • Symmetric multiprocessing

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Henrique Andrade

  • Tahsin Kurc

  • Alan Sussman

  • Joel Saltz

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free