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Erschienen in: Cluster Computing 5/2019

31.08.2017

Efficient data retrieval using adaptive clustered indexing for continuous queries over streaming data

verfasst von: M. R. Sumalatha, M. Ananthi

Erschienen in: Cluster Computing | Sonderheft 5/2019

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Abstract

The Modern era has highly dynamic, heterogeneous and massive data volumes, generated from sensor networks, social media and telecommunications, stock market analyses and the Internet, etc. makes constant query processing quite challenging in processing real-time data, which exist as streams and undergo dynamic changes. Large volumes of data can be efficiently handled by partitioning them into clusters followed by Indexing. An efficient clustering and indexing method is required to process continuous queries for retrieving data streams. A new index structure called adaptive clustering and block-based indexing (ACBBI) is proposed, which is a fusion of cluster-based and block-based techniques to process continuous queries. The incoming data are clustered and stored as blocks using the adaptive clustering method and further indexed by the adaptive indexing approach. Livestock market values that are time variant are used for experimentation. The experimental analysis demonstrates that the ACBBI tree structure significantly decreases half of the space cost, scales better with increasing data size and improves the retrieval rate 30% more than an existing CKDB approach.

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Metadaten
Titel
Efficient data retrieval using adaptive clustered indexing for continuous queries over streaming data
verfasst von
M. R. Sumalatha
M. Ananthi
Publikationsdatum
31.08.2017
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe Sonderheft 5/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-1093-z

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