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DC Field | Value | Language |
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dc.contributor.author | Radha, R | - |
dc.contributor.author | Selvarani, R | - |
dc.date.accessioned | 2023-12-09T08:56:07Z | - |
dc.date.available | 2023-12-09T08:56:07Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | pp. 1-5 | en_US |
dc.identifier.isbn | 9781665468510 | - |
dc.identifier.uri | https://doi.org/10.1109/ASIANCON55314.2022.9909466 | - |
dc.identifier.uri | http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/2314 | - |
dc.description.abstract | Relational 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.iso | en | en_US |
dc.publisher | 2022 2nd Asian Conference on Innovation in Technology, ASIANCON 2022 | en_US |
dc.subject | Big data analytics | en_US |
dc.subject | Multi storage systems | en_US |
dc.subject | Query optimization and machine learning | en_US |
dc.subject | Query processing | en_US |
dc.title | A Survey on Automatic Query Optimization Approaches In Multi Store Systems For Big Data Analytics | en_US |
dc.type | Article | en_US |
Appears in Collections: | Conference Papers |
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