Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-28323-9_7
Title: Progressive and Approximate Join Algorithms on Data Streams
Authors: Tok, W.H.
Bressan, S. 
Issue Date: 2013
Source: Tok, W.H.,Bressan, S. (2013). Progressive and Approximate Join Algorithms on Data Streams. Intelligent Systems Reference Library 36 : 157-185. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-28323-9_7
Abstract: In this chapter, we discuss the design and implementation of join algorithms for data streaming systems, wherememory is often limited relative to the data that needs to be processed.We first focus on progressive join algorithms for various data models. We introduce a framework for progressive join processing, called the Result Rate based Progressive Join (RRPJ) framework which can be used for join processing for various data models, and discuss its various instantiations for processing relational, high-dimensional, spatial and XML data. We then consider progressive and approximate join algorithms. The need for approximate join algorithms is motivated by the observation that users often do not require complete set of answers. Some answers, which we refer to as an approximate result, are often sufficient. Users expect the approximate result to be either the largest possible or the most representative (or both) given the resources available. We discuss the tradeoffs between maximizing quantity and quality of the approximate result. To address the different tradeoffs, we discuss a family of algorithms for progressive and approximate join processing. © Springer-Verlag Berlin Heidelberg 2013.
Source Title: Intelligent Systems Reference Library
URI: http://scholarbank.nus.edu.sg/handle/10635/77905
ISBN: 9783642283222
ISSN: 18684394
DOI: 10.1007/978-3-642-28323-9_7
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

2
checked on Feb 21, 2018

Page view(s)

32
checked on Feb 17, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.