Prioritized query shedding technique for continuous queries over data streams

Helmy, Yehia; Zanfaly, Doaa S. El; Othman, Nermin Abdelhakim

doi:10.1109/icces.2009.5383229

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…The most important variation of load shedding in the data stream includes giving priority of the query for the continuous data stream [3], sketching data stream [4], semantic approximation of windows spilt and joins [6], Windows aggregation queries [9] and Sliding window-join aggregation queries [10]. We continuously evaluate the data stream on the query network, the limit exceeds above the capacity of memory at the same time the insertion, deletion and updating of data stream information could affect the query results.…”

Section: Related Workmentioning

confidence: 99%

Load Shedding using Window Aggregation Queries on Data Streams

Senthamilarasu¹,

Hemalatha²

2012

IJCA

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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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