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Updated: Dec 29, 2025

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SOOM: Sort-Based Optimizer for Big Data Multi-Query.

Radhya Sahal1,2, Mohammed H Khafagy3, Fatma A Omara1

  • 1Faculty of Computers and Information, Cairo University, Cairo, Egypt.

Big Data
|January 31, 2020
PubMed
Summary
This summary is machine-generated.

Optimizing Big Data multi-queries by reusing shared sort operations significantly reduces execution time and data movement. The SOOM system enhances previous methods by exploiting both explicit and implicit sorts, improving efficiency.

Keywords:
Big Dataaggregationmulti-query optimizationsharing opportunitysort

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Area of Science:

  • Computer Science
  • Data Management
  • Database Systems

Background:

  • Sorting data is resource-intensive, especially in Big Data environments with multiple queries.
  • Shared sort operations in Big Data multi-queries incur high I/O costs due to repeated data shuffling.
  • Existing systems like MOTH optimize data sharing but overlook redundant network movement for sorting.

Purpose of the Study:

  • To address the overheads of redundant data movement in Big Data multi-queries.
  • To develop an optimized system for handling both explicit and implicit sort operations in multi-query scenarios.
  • To extend the MOTH system to exploit sharing sort opportunities for improved efficiency.

Main Methods:

  • Extended the Multi-Query Optimization Using Tuple Size and Histogram (MOTH) system to create the Sort-Based Optimizer over MOTH (SOOM) system.
  • Introduced two new modules: query explorer and sort exploiter, to identify and leverage sharing sort opportunities.
  • Integrated SOOM with the existing MOTH system to optimize multiple aggregation and sort queries.

Main Results:

  • The SOOM system reduced query execution time by 45% compared to naive methods and 30% compared to state-of-the-art techniques.
  • Achieved significant intermediate data size reduction, averaging 67% over naive methods and 61% over state-of-the-art techniques.
  • Demonstrated improved performance on Hadoop-like infrastructures.

Conclusions:

  • The SOOM system effectively optimizes Big Data multi-queries by exploiting sharing sort opportunities.
  • Reusing intermediate sort results significantly cuts down execution time and network data transfer.
  • SOOM offers a substantial improvement over existing methods for Big Data query optimization.