Data mining: concepts and techniques
Data mining: concepts and techniques
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Object-Based Selective Materialization for Efficient Implementation of Spatial Data Cubes
IEEE Transactions on Knowledge and Data Engineering
Selective Materialization: An Efficient Method for Spatial Data Cube Construction
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Multidimensional data modeling for location-based services
The VLDB Journal — The International Journal on Very Large Data Bases
Capturing complex multidimensional data in location-based data warehouses
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Selection of Views to Materialize in a Data Warehouse
IEEE Transactions on Knowledge and Data Engineering
Extended cascaded star schema and ECOLAP operations for spatial data warehouse
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Extended cascaded star schema for distributed spatial data warehouse
PPAM'09 Proceedings of the 8th international conference on Parallel processing and applied mathematics: Part I
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A data warehouse is defined as subject-oriented, integrated, time-variant and nonvolatile collection of data. Often, the data representing different subjects is multi-dimensional in nature, where each dimension of each subject could again be multi-dimensional. We refer to this as hyper-dimensional nature of data. Traditional multi-dimensional data models (e.g., the star schema) cannot adequately model these data. This is because, a star schema models one single multi-dimensional subject, hence a complex query crossing different subjects at different dimensional levels has to be specified as multiple queries and the results of each query must be composed together manually. In this paper, we present a novel data model, called the cascaded star model, to model hyper-dimensional data, and propose the cascaded OLAP (COLAP) operations that enable ad-hoc specification of queries that encompass multiple stars. Specifically, our COALP operations include cascaded-roll-up, cascaded-drill-down, cascaded-slice, cascaded-dice and MCUBE. We show that COLAP can be represented by the relational algebra to demonstrate that the cascaded star can be built on top of the traditional star schema framework.