Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/2314
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dc.contributor.authorRadha, R-
dc.contributor.authorSelvarani, R-
dc.date.accessioned2023-12-09T08:56:07Z-
dc.date.available2023-12-09T08:56:07Z-
dc.date.issued2022-
dc.identifier.citationpp. 1-5en_US
dc.identifier.isbn9781665468510-
dc.identifier.urihttps://doi.org/10.1109/ASIANCON55314.2022.9909466-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/2314-
dc.description.abstractRelational Database Management System (RDBMS) was the most used storage and query processing engine for data warehousing applications till the arrival of Bigdata stores. Enterprises are recently adopting a multi store system with combined use of Bigdata stores and RDBMS. Bigdata stores are used for exploratory queries and gathering business insights. RDBMS is used for business reporting. Though, Multi storage systems improves the query processing capability by executing it over multiple stores it is challenging to optimize the query execution considering queries on both structured and unstructured data for big data analytics. This work surveys the existing methods for query optimization in multi storage systems. The objective is to identify the challenges and shortcoming in the existing methods on query optimization over multi data stores. © 2022 IEEE.en_US
dc.language.isoenen_US
dc.publisher2022 2nd Asian Conference on Innovation in Technology, ASIANCON 2022en_US
dc.subjectBig data analyticsen_US
dc.subjectMulti storage systemsen_US
dc.subjectQuery optimization and machine learningen_US
dc.subjectQuery processingen_US
dc.titleA Survey on Automatic Query Optimization Approaches In Multi Store Systems For Big Data Analyticsen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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