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Workload-Driven Transactional Partitioning for Distributed Databases

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Data Intelligence and Cognitive Informatics

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Nowadays, data warehouses store massive amounts of data, and various techniques are using to access data. It also requires durable data consistency for the smooth execution of many Web applications. To support scalability, a model is used to investigate a distributed transaction management system that guarantees atomicity, consistency, isolation and durability (ACID) properties. The purpose of this paper to increase the performance and scalability of the database using the workload-driven data partitioning model. The result shows a better performance of NoSQL database using average response time on different partitioning techniques on a cloud-based database. The presented model can be helpful for application developers for building Web applications using distributed and cloud-based databases.

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Author information

Authors and Affiliations

  1. College of Engineering, Pune, India

    R. D. Bharati & V. Z. Attar

Authors
  1. R. D. Bharati
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  2. V. Z. Attar
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Corresponding author

Correspondence to R. D. Bharati .

Editor information

Editors and Affiliations

  1. GITAM University, Bangalore, India

    I. Jeena Jacob

  2. Department of Mathematics and Computer Science, Concordia University Chicago, River Forest, IL, USA

    Selvanayaki Kolandapalayam Shanmugam

  3. University of Florida, Gainesville, FL, USA

    Selwyn Piramuthu

  4. Gdańsk University of Technology, Gdańsk, Poland

    Przemyslaw Falkowski-Gilski

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© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Bharati, R.D., Attar, V.Z. (2021). Workload-Driven Transactional Partitioning for Distributed Databases. In: Jeena Jacob, I., Kolandapalayam Shanmugam, S., Piramuthu, S., Falkowski-Gilski, P. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-8530-2_31

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