e-ISSN:0976-5166
p-ISSN:2231-3850


INDIAN JOURNAL OF COMPUTER SCIENCE AND ENGINEERING

Call for Papers 2020

Dec 2020 - Volume 11, Issue 6
Deadline: 15 Nov 2020
Publication: 20 Dec 2020

Feb 2021 - Volume 12, Issue 1
Deadline: 15 Jan 2021
Publication: 20 Feb 2021

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ABSTRACT

Title : SAMPLING BASED JOIN-AGGREGATE QUERY PROCESSING TECHNIQUE FOR BIG DATA
Authors : Praveen Kumar Sadineni
Keywords : Approximate Query Processing; Big Data; Sampling; Aggregate Join Queries.
Issue Date : Sep-Oct 2020
Abstract :
Query Processing on Big Data has received significant attention in the literature. Many Big Data sources generate unstructured data. Big Data is characterized by extreme velocity of data generation and very large data volumes, and due to which, structuring of data becomes impractical. Hence, Big Data queries need to be executed on unstructured data, which results in extreme computational costs. Hence, many Approximate Query Processing Techniques (AQPTs) have been presented in the literature which provide approximate query results using sampled data, and thus achieve noticeable computational efficiency. Recently in the literature, AQPT is presented for the approximate execution of simple non-join aggregate queries. This presented AQPT achieves a predefined estimation error, and exhibits noticeable computational efficiency, however, this AQPT only addresses simple non-join aggregate queries, and does not address join-aggregate queries involving join of multiple relations. To address this open issue, in this paper, AQPT is presented for the approximate execution of join-aggregate queries which involve join of multiple relations. The proposed AQPT achieves pre-defined estimation error. Empirical analysis study of the proposed AQPT along with the contemporary technique is outlined. In this outlined empirical analysis study, the proposed AQPT significantly outperforms the contemporary technique in-terms of estimation accuracy and query execution latency.
Page(s) : 532-546
ISSN : 0976-5166
Source : Vol. 11, No.5
PDF : Download
DOI : 10.21817/indjcse/2020/v11i5/201105116