By Topic

A Prediction Framework for Distributed Data Stream Processing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
He ZhiYong ; Changsha Univ. of Sci. & Technol., Changsha, China ; Du Rong-Hua

It is very important in a lot of applications to forecast future trend of data streams. For example, a GPS system in a car could send not only the current location of the car but also its vector of movement or expected trajectory. Recent works on query processing over data streams mainly focused on approximate queries over newly arriving data. To the best of the knowledge, there is nothing to date in the literature on predictive query processing over data streams. Prediction models are introduced in distributed data stream processing and the problem formulation is detailed with. A common framework is raised and key parts of the architecture are described. The framework provides a mechanism to maintain adaptive prediction models that significantly reduce communication cost over the distributed environment while still guaranteeing sufficient precision of query results.

Published in:

Circuits, Communications and Systems, 2009. PACCS '09. Pacific-Asia Conference on

Date of Conference:

16-17 May 2009