A State-of-the-Art in Spatio-Temporal Data Warehousing, OLAP and Mining

A State-of-the-Art in Spatio-Temporal Data Warehousing, OLAP and Mining

Leticia Gómez (Instituto Tecnológico de Buenos Aires, Argentina), Bart Kuijpers (Hasselt University and Transnational University of Limburg, Belgium), Bart Moelans (Hasselt University and Transnational University of Limburg, Belgium) and Alejandro Vaisman (Universidad de la Republica, Uruguay)
DOI: 10.4018/978-1-60960-537-7.ch009
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Abstract

Geographic Information Systems (GIS) have been extensively used in various application domains, ranging from economical, ecological and demographic analysis, to city and route planning. Nowadays, organizations need sophisticated GIS-based Decision Support System (DSS) to analyze their data with respect to geographic information, represented not only as attribute data, but also in maps. Thus, vendors are increasingly integrating their products, leading to the concept of SOLAP (Spatial OLAP). Also, in the last years, and motivated by the explosive growth in the use of PDA devices, the field of moving object data has been receiving attention from the GIS community, although not much work has been done to provide moving object databases with OLAP capabilities. In the first part of this paper we survey the SOLAP literature. We then address the problem of trajectory analysis, and review recent efforts regarding trajectory data warehousing and mining. We also provide an in-depth comparative study between two proposals: the GeoPKDD project (that makes use of the Hermes system), and Piet, a proposal for SOLAP and moving objects, developed at the University of Buenos Aires, Argentina. Finally, we discuss future directions in the field, including SOLAP analysis over raster data.
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Introduction

Geographic Information Systems (GIS) have been extensively used in various application domains, ranging from economical, ecological and demographic analysis, to city and route planning (Rigaux, Scholl, & Voisard, 2001; Worboys, 1995). Spatial information in a GIS is typically stored in different so-called thematic layers (also called themes). Information in themes can be stored in data structures according to different data models, the most usual ones being the raster model and the vector model. In a thematic layer, spatial data is annotated with classical relational attribute information, of (in general) numeric or string type. While spatial data is stored in data structures suitable for these kinds of data, associated attributes are usually stored in conventional relational databases. Spatial data in the different thematic layers of a GIS system can be mapped univocally to each other using a common frame of reference, like a coordinate system. These layers can be overlapped or overlayed to obtain an integrated spatial view.

On the other hand, OLAP (On Line Analytical Processing) (Kimball, 1996; Kimball & Ross, 2002) comprises a set of tools and algorithms that allow efficiently querying multidimensional databases containing large amounts of data, usually called data warehouses. In OLAP, data is organized as a set of dimensions and fact tables. In the multidimensional model, data can be perceived as a data cube, where each cell contains a measure or set of (probably aggregated) measures of interest. As we discuss later, OLAP dimensions are further organized in hierarchies that favor data aggregation (Cabibbo & Torlone, 1997). Several techniques and algorithms have been developed for multidimensional query processing, most of them involving some kind of aggregate precomputation (Harinarayan, Rajaraman, & Ullman, 1996).

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