Some Mathematical Methods for Modeling the Performance of a Distributed Data Base System.

Abstract

This paper presents some mathematical methods for evaluating the performance of a distributed data base system (DDBS). Performance is measured by the speed and accuracy with which data are transmitted from one location to another. Five techniques are described: the Data Flow Model, a semi-Markov model for determining the spatial and temporal distribution of data that are to be transferred from one location to another; Optimal Sample Size Estimation, a method for determining the amount of data to be collected for input to the Data Flow Model; Confidence Interval Estimation, a method for estimating confidence intervals for the outputs of the Data Flow Model; Sensitivity Estimation, a method for estimating the sensitivity of DDBS performance to changes in the parameters of the Data Flow Model; and the Aggregation of Stratified Semi-Markov Processes, a method for combining semi-Markov Data Flow Models developed for subsystems (e.g., geographic regions) into a single model that is representative of the entire DDBS. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1982
Accession Number
ADA116604

Entities

People

  • C. Bernard Barfoot

Organizations

  • Center for Naval Analyses

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Business Administration
  • Computer Programs
  • Databases
  • Diffusion Theory
  • Equations
  • Errors
  • Geographic Regions
  • Information Processing
  • Information Science
  • Management Personnel
  • Marine Corps
  • Markov Models
  • Markov Processes
  • Operations Research
  • Physics
  • Probability
  • Statistics

Fields of Study

  • Mathematics

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  • Regression Analysis.