Efficient Storage of Large Volume Spatial and Temporal Point-Data in an Object-Oriented Database

Abstract

Data mining applications must deal with large volumes of data. In particular, Spatio-Temporal Information Systems must efficiently store and access potentially very large quantities of spatial and temporal data. Therefore, storing the data in an efficient and useful way is of great importance. Binary Large Objects (BLOBs) are found in many database systems and have been extensively used in typical database applications for the storage of large volume data. In this chapter, we describe the extension of basic BLOBs for specialized use with spatial and temporal data. These ncw repositories, Spatial BLOBs and Temporal BLOBs. add additional functionality for the query and retrieval of the repository's contents in a semantically meaningful, object- oriented form. The repositories are designed as flexible frameworks, decoupled from the particular binary format of their internal contents. Custom plug-ins allow the frameworks to be extended to use a particular binary format that is most appropriate for a given data type.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2002
Accession Number
ADA406876

Entities

People

  • David V. Oliver
  • Frank P. Mccreedy
  • Roy V. Ladner
  • Ruth A. Wilson

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Abstracts
  • Access Time
  • Atmospheres
  • Data Analysis
  • Data Mining
  • Data Sets
  • Data Storage Systems
  • Data Transmission
  • Databases
  • Environment
  • Grids
  • Information Systems
  • Meteorological Data
  • Military Research
  • Storage
  • Time Intervals
  • Weather Stations

Fields of Study

  • Computer science

Readers

  • Database Systems and Applications
  • Distributed Systems and Data Platform Development
  • Space/Atmospheric Physics.

Technology Areas

  • AI & ML