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Load Shedding using Window Aggregation Queries on Data Streams
by
S. Senthamilarasu,
M. Hemalatha
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 54 - Number 9 |
| Year of Publication: 2012 |
| Authors: S. Senthamilarasu, M. Hemalatha |
10.5120/8598-2362
|
S. Senthamilarasu, M. Hemalatha . Load Shedding using Window Aggregation Queries on Data Streams. International Journal of Computer Applications. 54, 9 ( September 2012), 42-49. DOI=10.5120/8598-2362
@article{
10.5120/8598-2362,
author = {
S. Senthamilarasu,
M. Hemalatha
},
title = { Load Shedding using Window Aggregation Queries on Data Streams },
journal = {
International Journal of Computer Applications
},
issue_date = { September 2012 },
volume = { 54 },
number = { 9 },
month = { September },
year = { 2012 },
issn = { 0975-8887 },
pages = {
42-49
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume54/number9/8598-2362/
},
doi = { 10.5120/8598-2362 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:55:37.256235+05:30
%A S. Senthamilarasu
%A M. Hemalatha
%T Load Shedding using Window Aggregation Queries on Data Streams
%J International Journal of Computer Applications
%@ 0975-8887
%V 54
%N 9
%P 42-49
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract
The processes of extracting knowledge structures for continuous, rapid records are known as the Data Stream Mining. The main issue in stream mining is handling streams of elements delivered rapidly which makes it infeasible to store everything in active storage. To overcome this problem of handling voluminous data we exposed a novel load shedding system using window based aggregate function of the data stream in which we accept those tuples in the stream that meet a criterion. Accepted tuples are conceded to another process as a stream, while further tuples are dropped. This proposed model conceivably segregates the data input stream into windows and probabilistically decides which tuple to drop based on the window function. The best window aggregate function used for dropping tuples is identified with the three prediction models used in data mining they are Decision Tree, Naïve Bayes and Logistic Regression. The result shows that the cumulative distance and density rank functions outperforms the remaining methods. Distinct to prior methods, our method preserves uniformity of windows all over a query plan, and constantly distributes subsets of the original query responds with insignificant denial in the excellence of the consequence.
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Index Terms
Computer Science
Information Sciences
Keywords