Data warehousing and Online Analytical Processing (OLAP) technology has been
used to access, visualize and analyze multidimensional, aggregated, and summarized
data. Large part of data contains spatial components. Thus, these spatial components
convey valuable information and must be included in exploration and analysis phases
of a spatial decision support system (SDSS). On the other hand, Geographic
Information Systems (GISs) provide a wide range of tools to analyze spatial
phenomena and therefore must be included in the analysis phases of a decision
support system (DSS). In this regard, this study aims to search for answers to the
problem how to design a spatially enabled data warehouse architecture in order to
support spatio-temporal data analysis and exploration of multidimensional data.
Consequently, in this study, the concepts of OLAP and GISs are synthesized in an
integrated fashion to maximize the benefits generated from the strengths of both
systems by building a spatial data warehouse model. In this context, a
multidimensional spatio-temporal data model is proposed as a result of this synthesis.
This model addresses the integration problem of spatial, non-spatial and temporal
data and facilitates spatial data exploration and analysis. The model is evaluated by
implementing a case study in weather pattern searching.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/2/12609573/index.pdf |
| Date | 01 June 2008 |
| Creators | Koylu, Caglar |
| Contributors | Akyurek, Zuhal |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for METU campus |
Page generated in 0.0022 seconds