Attribute Partitioning in a Self-Adaptive Relational Database System.

Abstract

One technique that is sometimes employed to enhance the performance of a database management system is known as attribute partitioning. This is the process of dividing the attributes of a file into subfiles that are stored separately. By storing together those attributes that are frequently requested together by transactions, and by separating those that are not, attribute partitioning can reduce the number of pages that must be transferred from secondary storage to primary memory in order to process a transaction. The goal of this work is to design mechanisms that can automatically select a near-optimal attribute partition of a file's attributes, based on the usage pattern of the file and on the characteristics of the data in the file. The approach taken to this problem is based on the use of a file design cost estimator and of heuristics to guide a search through the large space of possible partitions. The heuristics propose a small set of promising partitions to submit for detailed analysis. The estimator assigns a figure of merit to any proposed partition that reflects the cost that would be incurred in processing the transactions in the usage pattern if the file were partitioned in the proposed way. We have also conducted an extensive series of experiments with a variety of design heuristics; as a result, we have identified a heuristic that nearly always finds the optimal partition of a file.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1978
Accession Number
ADA053292

Entities

People

  • Bahram Niamir

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Application Software
  • Command And Control
  • Computer Programming
  • Computer Science
  • Computers
  • Cost Analysis
  • Cost Estimates
  • Database Management Systems
  • Databases
  • Estimators
  • Integer Programming
  • Mathematical Programming
  • Optimization
  • Relational Database Management Systems
  • Relational Databases
  • Time Intervals

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Vision.
  • Parallel and Distributed Computing.

Technology Areas

  • Space