The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
An Adaptive Probe-Based Technique to Optimize Join Queries in Distributed Internet Databases
|
Author(s): Latifur Khan (University of Texas at Dallas, USA), Dennis McLeod (University of Southern California, USA)and Cyrus Shahabi (University of Southern California, USA)
Copyright: 2002
Pages: 24
Source title:
Human Computer Interaction Development & Management
Source Author(s)/Editor(s): Tonya Barrier (Southwest Missouri State University, USA)
DOI: 10.4018/978-1-931777-13-1.ch006
Purchase
|
Abstract
An adaptive probe-based optimization technique is developed and demonstrated in the context of an Internet-based distributed database environment. More and more common are database systems, which are distributed across servers communicating via the Internet where a query at a given site might require data from remote sites. Optimizing the response time of such queries is a challenging task due to the unpredictability of server performance and network traffic at the time of data shipment; this may result in the selection of an expensive query plan using a static query optimizer. We constructed an experimental setup consisting of two servers running the same DBMS connected via the Internet. Concentrating on join queries, we demonstrate how a static query optimizer might choose an expensive plan by mistake. This is due to the lack of a priori knowledge of the run-time environment, inaccurate statistical assumptions in size estimation, and neglecting the cost of remote method invocation. These shortcomings are addressed collectively by proposing a probing mechanism. Furthermore, we extend our mechanism with an adaptive technique that detects sub-optimality of a plan during query execution and attempts to switch to the cheapest plan while avoiding redundant work and imposing little overhead. An implementation of our run-time optimization technique for join queries was constructed in the Java language and incorporated into an experimental setup. The results demonstrate the superiority of our probe-based optimization over a static optimization.
Related Content
Gülsüm Akarsu.
© 2023.
19 pages.
|
Mitchell Alan Kaplan.
© 2023.
18 pages.
|
Sonia Bhandari, Aarti Bhandari.
© 2023.
21 pages.
|
Mehmet Hilmi Özkaya, Abdullatif Almukaddam, Maeen Alhuwesh.
© 2023.
14 pages.
|
Oğuzhan Yelkesen.
© 2023.
19 pages.
|
Lina Karabetyan.
© 2023.
12 pages.
|
Özlem Yorulmaz.
© 2023.
18 pages.
|
|
|