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Computer Science > Databases

arXiv:2205.05938 (cs)
[Submitted on 12 May 2022]

Title:Query Complexity Based Optimal Processing of Raw Data

Authors:Mayank Patel, Minal Bhise
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Abstract:The paper aims to find an efficient way for processing large datasets having different types of workload queries with minimal replication. The work first identifies the complexity of queries best suited for the given data processing tool . The paper proposes Query Complexity Aware partitioning technique QCA with a lightweight query identification and partitioning algorithm. Different replication approaches have been studied to cover more use-cases for different application workloads. The technique is demonstrated using a scientific dataset known as Sloan Digital Sky Survey SDSS. The results show workload execution time WET reduced by 94.6% using only 6.7% of the dataset in loaded format compared to the original dataset. The QCA technique also reduced multi-node replication by 5.8x times compared to state-of-the-art workload aware WA techniques. The multi-node and multi-core execution of workload using QCA proposed partitions reduced WET by 42.66% and 25.46% compared to WA.
Comments: Accepted in 10th Region 10 Humanitarian Technology Conference (R10-HTC)
Subjects: Databases (cs.DB); Distributed, Parallel, and Cluster Computing (cs.DC)
Cite as: arXiv:2205.05938 [cs.DB]
  (or arXiv:2205.05938v1 [cs.DB] for this version)
  https://doi.org/10.48550/arXiv.2205.05938
Related DOI: https://doi.org/10.1109/R10-HTC54060.2022.9929945

Submission history

From: Mayank Patel [view email]
[v1] Thu, 12 May 2022 08:02:01 UTC (1,411 KB)
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