A foundation for capturing and querying complex multidimensional data
Information Systems - Data warehousing
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Realm-based spatial data types: the ROSE algebra
The VLDB Journal — The International Journal on Very Large Data Bases
Extending Geographic Databases for a Query Language to Support Queries Involving Statistical Data
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
Multidimensional data modeling for location-based services
The VLDB Journal — The International Journal on Very Large Data Bases
Representing spatiality in a conceptual multidimensional model
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Topological relationships between complex spatial objects
ACM Transactions on Database Systems (TODS)
Advanced Data Warehouse Design: From Conventional to Spatial and Temporal Applications (Data-Centric Systems and Applications)
iBLOB: complex object management in databases through intelligent binary large objects
ICOODB'10 Proceedings of the Third international conference on Objects and databases
On the requirements for user-centric spatial data warehousing and SOLAP
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications
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In recent years, there has been a large increase in the amount of spatial data obtained from remote sensing, GPS receivers, communication terminals and other domains. Data warehouses help in modeling and mining large amounts of data from heterogeneous sources over an extended period of time. However incorporating spatial data into data warehouses leads to several challenges in data modeling, management and the mining of spatial information. New multidimensional data types for spatial application objects require new OLAP formulations to support query and analysis operations on them. In this paper, we introduce a set of constructs called C3 for defining data cubes. These include categorization, containment and cubing operations, which present a fundamentally new, user-centric strategy for the conceptual modeling of data cubes. We also present a novel region-hierarchy concept that builds spatially ordered sets of polygon objects and employs them as first class citizens in the data cube. Further, new OLAP constructs to help define, manipulate, query and analyze spatial data have also been presented. Overall, the aim of this paper is to leverage support for spatial data in OLAP cubes and pave the way for the development of a user-centric SOLAP system.