Title :
Multidimensional data modeling for complex data
Author :
Pedersen, Torben Bach ; Jensen, Christian S.
Author_Institution :
Center for Health Inf. Services, Arhus N, Denmark
Abstract :
Online Analytical Processing (OLAP) systems considerably ease the process of analyzing business data and have become widely used in industry. Such systems primarily employ multidimensional data models to structure their data. However current multidimensional data models fall short in their abilities to model the complex data found in some real world application domains. The paper presents nine requirements to multidimensional data models, each of which is exemplified by a real world, clinical case study. A survey of the existing models reveals that the requirements not currently met include support for many-to-many relationships between facts and dimensions, built-in support for handling chance and time, and support for uncertainty as well as different levels of granularity in the data. The paper defines an extended multidimensional data model, and an associated algebra, which address all nine requirements
Keywords :
data mining; data models; medical information systems; uncertainty handling; OLAP systems; Online Analytical Processing; associated algebra; built-in support; business data; clinical case study; complex data; extended multidimensional data model; granularity; many-to-many relationships; multidimensional data modeling; multidimensional data models; real world application domains; uncertainty; Algebra; Computer industry; Computer science; Data mining; Data models; Electronic switching systems; Hip; Information analysis; Multidimensional systems; Uncertainty;
Conference_Titel :
Data Engineering, 1999. Proceedings., 15th International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7695-0071-4
DOI :
10.1109/ICDE.1999.754949
