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SRank: Guiding schema selection in NoSQL document stores.

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Title: SRank: Guiding schema selection in NoSQL document stores.
Authors: Sachdeva, Shelly1 (AUTHOR) shellysachdeva@nitdelhi.ac.in, Bansal, Neha1,2 (AUTHOR) neha.bansal@bennett.edu.in, Bansal, Hardik1 (AUTHOR) 222211009@nitdelhi.ac.in
Source: Data & Knowledge Engineering. Nov2024, Vol. 154, pN.PAG-N.PAG. 1p.
Subjects: Hotel reservation systems, Nonrelational databases, Database design, Big data, Data modeling
Abstract: The rise of big data has led to a greater need for applications to change their schema frequently. NoSQL databases provide flexibility in organizing data and offer multiple choices for structuring and storing similar information. While schema flexibility speeds up initial development, choosing schemas wisely is crucial, as they significantly impact performance, affecting data redundancy, navigation cost, data access cost, and maintainability. This paper emphasizes the importance of schema design in NoSQL document stores. It proposes a model to analyze and evaluate different schema alternatives and suggest the best schema out of various schema alternatives. The model is divided into four phases. The model inputs the Entity-Relationship (ER) model and workload queries. In the Transformation Phase, the schema alternatives are initially developed for each ER model, and subsequently, a schema graph is generated for each alternative. Concurrently, workload queries undergo conversion into query graphs. In the Schema Evaluation phase, the Schema Rank (SRank) is calculated for each schema alternative using query metrics derived from the query graphs and path coverage generated from the schema graphs. Finally, in the Output phase, the schema with the highest SRank is recommended as the most suitable choice for the application. The paper includes a case study of a Hotel Reservation System (HRS) to demonstrate the application of the proposed model. It comprehensively evaluates various schema alternatives based on query response time, storage efficiency, scalability, throughput, and latency. The paper validates the SRank computation for schema selection in NoSQL databases through an extensive experimental study. The alignment of SRank values with each schema's performance metrics underscores this ranking system's effectiveness. The SRank simplifies the schema selection process, assisting users in making informed decisions by reducing the time, cost, and effort of identifying the optimal schema for NoSQL document stores. [ABSTRACT FROM AUTHOR]
Copyright of Data & Knowledge Engineering is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: SRank: Guiding schema selection in NoSQL document stores.
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  Data: <searchLink fieldCode="AR" term="%22Sachdeva%2C+Shelly%22">Sachdeva, Shelly</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> shellysachdeva@nitdelhi.ac.in</i><br /><searchLink fieldCode="AR" term="%22Bansal%2C+Neha%22">Bansal, Neha</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> neha.bansal@bennett.edu.in</i><br /><searchLink fieldCode="AR" term="%22Bansal%2C+Hardik%22">Bansal, Hardik</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 222211009@nitdelhi.ac.in</i>
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  Data: <searchLink fieldCode="DE" term="%22Hotel+reservation+systems%22">Hotel reservation systems</searchLink><br /><searchLink fieldCode="DE" term="%22Nonrelational+databases%22">Nonrelational databases</searchLink><br /><searchLink fieldCode="DE" term="%22Database+design%22">Database design</searchLink><br /><searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br /><searchLink fieldCode="DE" term="%22Data+modeling%22">Data modeling</searchLink>
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  Data: The rise of big data has led to a greater need for applications to change their schema frequently. NoSQL databases provide flexibility in organizing data and offer multiple choices for structuring and storing similar information. While schema flexibility speeds up initial development, choosing schemas wisely is crucial, as they significantly impact performance, affecting data redundancy, navigation cost, data access cost, and maintainability. This paper emphasizes the importance of schema design in NoSQL document stores. It proposes a model to analyze and evaluate different schema alternatives and suggest the best schema out of various schema alternatives. The model is divided into four phases. The model inputs the Entity-Relationship (ER) model and workload queries. In the Transformation Phase, the schema alternatives are initially developed for each ER model, and subsequently, a schema graph is generated for each alternative. Concurrently, workload queries undergo conversion into query graphs. In the Schema Evaluation phase, the Schema Rank (SRank) is calculated for each schema alternative using query metrics derived from the query graphs and path coverage generated from the schema graphs. Finally, in the Output phase, the schema with the highest SRank is recommended as the most suitable choice for the application. The paper includes a case study of a Hotel Reservation System (HRS) to demonstrate the application of the proposed model. It comprehensively evaluates various schema alternatives based on query response time, storage efficiency, scalability, throughput, and latency. The paper validates the SRank computation for schema selection in NoSQL databases through an extensive experimental study. The alignment of SRank values with each schema's performance metrics underscores this ranking system's effectiveness. The SRank simplifies the schema selection process, assisting users in making informed decisions by reducing the time, cost, and effort of identifying the optimal schema for NoSQL document stores. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Data & Knowledge Engineering is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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              Text: Nov2024
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