- authors00000
- Prof. Dr.-Ing. Wolfgang Lehner Technische Universität Dresden, Fakultät Informatik, Institut für Systemarchitektur, Professur für Datenbanken
- Thomas Ruf
- Michael Teschke
- title
- CROSS-DB: a feature-extended multidimensional data model for statistical and scientific databases
- Please use the following URL when quoting:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-806231
- conference
- CIKM96: Conference on Information and Knowledge Management. Rockville, 12.-16. November 1996
- Source
- CIKM '96: Proceedings of the fifth international conference on Information and knowledge management
Herausgeber: Charles Nicholas
Herausgeber: Niki Pissinou
Herausgeber: M. Tamer Özsu
Herausgeber: Ken Barker
Erscheinungsort: New York
Verlag: ACM
Erscheinungsjahr: 1996
Seiten: 253-260
ISBN: 978-0-89791-873-2 - publication_date
- 1996
- Abstract (EN)
- Statistical and scientific computing applications exhibit characteristics that are fundamentally different from classical database system application domains. The CROSS-DB data model presented in this paper is optimized for use in such applications by providing advanced data modelling methods and application-oriented query facilities, thus providing a framework for optimized data management procedures. CROSS-DB (which stands for Classification-oriented, Redundancy-based Optimization of Statistical and Scientific DataBases) is based on a multidimensional data view. The model differs from other approaches by o~ering two complementary rnechanisrnsfor structuring qualifying information, classification and feature description. Using these mechanisms results in a normalized, low-dimensional database schema which ensures both, modelling uniqueness and understandability while providing enhanced modelling flexibility.
- otherVersion0
- Link zum Artikel, der zuerst in der ACM Digital Library erschienen ist.
DOI: 10.1145/238355.238547 - Keywords (DE)
- Verarbeitung und Optimierung von Datenbankabfragen, Statistische Grafiken
- Keywords (EN)
- Database query processing and optimization, Statistical graphics
- Classification (DDC)
- 004
- Publishing house
- ACM, New York
- version
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-806231
- Qucosa date of publication
- 13.09.2022
- Document type
- in_proceeding
- Document language
- English
- licence