OLAP Formulations for Supporting Complex Spatial Objects in Data Warehouses

Abstract

Abstract. 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 C 3 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.

SOLAM: A Novel Approach of Spatial Aggregation in SOLAP Systems

“…On the other hand, and in the last decade, several efforts have been made to enrich OLAP systems to support complex space objects. We quote here the work of [7] who proposed C 3 model presented as a new modeling strategy to support complex spatial objects in OLAP cubes. This model is the extension of the "BigCube" model proposed in [8].…”