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Multi-objective database queries in combined knapsack and set covering problem domains.

Sean A Mochocki1, Gary B Lamont1, Robert C Leishman1

  • 1Department of Electrical and Computer Engineering, Air Force Institute of Technology, 2950 Hobson Way, Wright-Patterson Air Force Base, 45433 USA.

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|March 16, 2021
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Summary

This study introduces novel algorithms for complex database queries, enhancing data retrieval for the Air Force Institute of Technology (AFIT). The research focuses on optimizing multi-objective knapsack/set covering problems for efficient data analysis.

Keywords:
Algorithm DomainGenetic AlgorithmHill Climber AlgorithmKnapsack ProblemMulti-ObjectivePosition Navigation and TimingProblem DomainSet Covering ProblemThe Knapsack Set Covering Problem

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Area of Science:

  • Computer Science
  • Database Management Systems
  • Algorithm Analysis

Background:

  • Relational databases are crucial for data representation, with varying user needs.
  • The Air Force Institute of Technology (AFIT) manages approximately 100 data logs requiring a standardized format (Scorpion Data Model).
  • Evaluating database schema performance is essential for selecting an efficient data housing solution.

Purpose of the Study:

  • To develop advanced database query capabilities for filter research.
  • To explore the combined Multi-Objective Knapsack Problem (KP) and Set Covering Problem (SCP) for enhanced data retrieval.
  • To propose and evaluate novel algorithms for solving complex, NP-Hard database query problems.

Main Methods:

  • Deterministic polynomial-time queries were used to benchmark schema performance.
  • Genetic and Hill Climber algorithms were proposed for the Multi-Objective KP/SCP.
  • Algorithms were implemented in Java and data structures populated using SQL queries from test databases.
  • Performance comparison of the developed algorithms against existing approaches.

Main Results:

  • A specific database schema was identified as AFIT's optimal solution based on initial performance tests.
  • The study explores the potential of combined KP/SCP algorithms for greater user power.
  • Implementation and comparison of Genetic and Hill Climber algorithms for the Multi-Objective KP/SCP.

Conclusions:

  • The developed algorithms offer a powerful approach to tackling complex database query challenges.
  • This research contributes to optimizing data analysis and retrieval for scientific and military applications.
  • Further development of these algorithms can lead to significant advancements in database query performance and utility.