TITLE:

Spatial Database Management Systems: A Tour

PRESENTER:

Shashi Shekhar : Biography , Homepage

AFFILIATION:

Computer Science Department, University of Minnesota.

URL:

http://www.cs.umn.edu/~shekhar

VIDEOS and SLIDES:

SLIDES:

ABSTRACT:

The importance of spatial database management system (SDBMS) is growing with the increasing incidence and importance of large geo-spatial datasets such as maps, repositories of remote-sensing images, and the decennial census. Applications include consumer-oriented location-based services (e.g. Google Map, mapquest and GPS devices), business asset/facility management and logistics; as well as science and policy related to environment, public safety and disaster recovery for organizations such as NASA (climatological effects of El Nino, land-use classification and global change using satellite imagery), National Institute of Health (predicting the spread of disease), National Institute of Justice (finding crime hot spots), and transportation agencies (detecting local instability in traffic).

Classical relational DBMS (RDBMS) often perform poorly when applied to spatial data sets because of the following reasons. First, RDBMS conceptual data models (e.g. entity relationship diagram(ERD) or UML) document all relationships explicitly assume a sparse set of relationship among entities. However, spatial entities have a rich set (almost completely connected) of implicit relationships (e.g. distance, direction) leading to a cluttered conceptual model. Second, RDBMS query languages (e.g. SQL) have a limited set of data-types (e.g. numbers, text-strings) leading to a large semantic gap with the needs of spatial querying (e.g. Google Maps, GPS devices). Finally, RDBMS indexes (e.g. B-tree, hashing), and query optimizers are designed for linear-ordered one-dimensional values such as numbers, however, spatial data is embedded in a multi-dimensional space.

Thus, SDBMS (e.g. ESRI GeoDatabase/SDE, Oracle SDO, DB2 SDC, Postgres postgis) have emerged over last decade to meet the unique needs of modeling, querying, and analysis of very large spatial datasets. We uncover SDBMS at three levels, namely, conceptual (e.g. Pictogram enhanced ERDs), logical (SQL3/OGIS query language) and physical (e.g. R-tree index). Trends (e.g. spatial networks and spatial data mining) are discussed briefly.

KEYWORDS: Spatial Datasets, Spatial Databases, Relational Databases.

ACKNOWLEDGMENTS: This work was supported in part by the National Science Foundation, the U.S. Department of Defense, the National Aeronautics and Space Administration the Federal Highway Authority, and the University of Minnesota (e.g. Center for Transportation Studies).

NOTE: Some of the results discussed in this talk appeared in the following publications:

  1. S. Shekhar and J. Kang, Spatial Databases , Wiley Encyclopedia of Computer Science and Engineering (Ed. Benjamin Wah), John Wiley and Sons Inc, 2009, isbn 978-0471383932.
  2. S. Shekhar and S. Chawla, Spatial Databases: A Tour , Prentice Hall 2003, ISBN 0-13-017480-7.
  3. S. Shekhar, S. Chawla, S. Ravada, A. Fetterer, X. Liu and C.T. Liu, Spatial Databases: Accomplishments and Research Needs , , IEEE Transactions on Knowledge and Data Engineering, 11(1), Jan.-Feb. 1999.
  4. S. Shekhar, R. R. Vatsavai, X. Ma, and J. S. Yoo, Navigation Systems: A Spatial Database Perspective, , in Location Based Services (Ed. A. Voisard and J. Schiller), Morgan Kaufmann, 2004, ISBN 1-55860-929-6.
  5. What is special about Mining spatial datasets? .
  6. Wikipedia entries on Spatial Databases and Geodatabases (Have links to details of software such as OGC, Postgis, Myysql etc.) and
  7. S. Shekhar and H. Xiong (Co-EIC) Encyclopedia of GIS , Springer, 2008, isbn 978-0-387-30858-6.