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Table of contents:

Object Oriented Database vs Relational Database

• Introduction
• The Wall: Where an RDBMS breaks down
• Programming Time and Cost
• Integrity Loss
• Performance
• Information Model Issues
• Architecture Issues
• Benchmark
• Benchmark Parameters
• Benchmark Execution
• Parts Creation
• Updates
• Deletes
• Selects
• Summary
• Examples
• Cimskil (Manufacturing)
• Exxon (Configuration and FEA)
• CERN (Data Gathering and Analysis)
• Decision•ism (Decision Supportware)
• QUALCOMM (Telecommunications)
• COM21 (Cable Modem)
• Nortel, Sprint (Cell Phone Base Station Management)
• Nousoft (Library Document Management, Web)
• Iridium (Telecommunications)
• BBN (Military Logistics and Control)
• Intraspect (Knowledge Management)
• Fisher-Rosemount (Manufacturing, Process Control)
• A Brief Overview of Our Development
• ODB Selection
• Introduction
• Performance
• Scalability
• Productivity
• Fault Tolerance
• Additional Features and Benefits
• Summary
• Kintetsu (Transportation, Logistics)
• Siemens (Process Control)
• SEH (Semiconductor Manufacturing)
• NEC (Semiconductor Manufacturing)
• American Meter (Process Control)
• Sloan Digital Sky Survey (Geographic Information System)
• LMS (Healthcare, Fetal Monitoring)
• Synthesis (Healthcare, Record Management)
• Daikin (Multi-Media CD-I Authoring)
• How to Avoid the Wall

Object Oriented Database vs Relational Database

Sloan Digital Sky Survey
(Geographic Information System)

The Sloan Digital Sky Survey (SDSS) is a $32M project run by a consortium, led by Johns Hopkins Space Telescope Science Institute and Chicago's FermiLab, to produce a new, digital survey of all the objects (stars, galaxies , quasars, etc.) in the sky. The previous survey, now over 50 years old, consists of analog glass photographic plates, which dramatically limit distribution, amount of information, and accuracy of information. SDSS has constructed a new 2.5m telescope, based directly on digital (CCD, 4Kx2K chips) imaging, which will gather information including luminosity, spectra and spectral intensities in various bands, and variability, for all objects in space. It is expected to take 5 years to map the northern celestial sphere. Raw information will go into the database, as well as analysis results that collect that information into identified objects, resulting in a catalogue of 100M objects. The database will be accessible to scholars, and parts of it even to schools and the public, via the Internet. Production software entered test usage in 1996, and the final database is expected to be approximately 40 TB, in Objectivity/DB.

The key to use of this enormous archive is supporting multidimensional indexing for queries not only in 3D space, but also in luminosity in several spectral ranges. To make this efficient, SDSS developed a new indexing algorithm, based on modified quad-trees, for n-dimensions. This index partitions all 100M objects into some 40K containers, a clus tering device in Objectivity/DB. Coarse-grained maps of the multidimensional space then model the contents of these containers. Queries, then, quickly index over this coarse grain map to quickly determine which of the containers are needed. This efficiently reduces the search space, and hence query execution time, dramatically. Measured results show it to be 100x faster than traditional databases.

Fermi and Johns Hopkins evaluated other DBMSs, including RDBMSs and ODBMSs, and chose Objectivity/DB for these reasons:

• Objectivity's performance in large-scale applications
• Objectivity's support for platform heterogeneity (they can spread the very large database across multiple hardware from multiple vendors)
• Objectivity provides features (e.g. caching) that Fermi would otherwise have had to code themselves in their application

For more information, see
http://www-sdss.fnal.gov:8000/intro
http://tarkus.pha.jhu.edu/scienceArchive/