What do you mean by spatial data warehouse spatial data cube, and Spatial Data Warehouse for Data Mining?

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SPATIAL DATA WAREHOUSE

A Spatial Data Warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of both stable spatial and nonspatial data in support of spatial data mining and spatial-data-related decision-making processes. As a kind of data warehouse, it integrates meta-data, thematic data, data engines, and data tools (extraction, transformation, load, storage, analysis, index, mining, etc.). When characterizing spatial data warehouses from spatial databases, spatial dimension and temporal dimension are basic. A spatial data warehouse is a tool to effectively manage spatial data and distribute them to the public to enhance spatial data-referenced decision-making.

A spatial data cube 

A spatial data cube organizes the multi-dimensional data from different fields (i.e., geospatial data and other thematic data from a variety of spheres) into an accessible data cube or hypercube according to dimensions, using three or more dimensions to describe an object. These dimensions are perpendicular to each other because the analysis results in the user needs is just in the cross-points.. data cube is a kind of multi-dimensional data structure with a hierarchy of multi-dimensions to identify and gather data representing the requirements of making a multi-faceted, multi-angle analysis for processing data in management and decision-making. For example, the time dimension can express a hierarchy of current, daily, weekly, monthly, quarterly, biannually, and annually.

Example: There are about 3,000 weather probes distributed in British Columbia (BC), Canada, each recording daily temperature and precipitation for a designated small area and transmitting signals to a provincial weather station. With a spatial data warehouse that supports spatial OLAP, a user can view weather patterns on a map by month, by region, and by different combinations of temperature and precipitation, and can dynamically drill down or roll up along any dimension to explore desire patterns, such as "wet and hot regions in the Fraser Valley in Summer 1999."

Spatial Data Warehouse for Data Mining

A spatial data warehouse reorganizes heterogeneous data under thematic subjects. Spatial data a refined and stable and are updated over a period of time. Due to the complexity of the structure, relationship, and computation in spatial data, it is more complicated and difficult to study and establish spatial data warehouses. The development of spatial data warehouses lags behind that of business data warehouses, such as IBM DB2 OLAP Server, Oracle Express Server, Microsoft OLAP Services, Informix's MetaCube, Sybase IQ. At present, for spatial data that are not very large in size, a spatial data warehouse can be developed on an existing GIS platform and is considered a GIS tool. As for large-scale diverse spatial data, a spatial data warehouse is developed in a spatial database engine by using a common relational database management system to manage spatial data, with the mature technologies in data security and data consistency and ease of maintenance, such as ESRI'S Spatial Database Engine and MapInfo's Spatialware. At present, there is no uniform standard on spatial data warehouses. are

Spatial data mining may be implemented on spatial data warehouses or embedded as one of the data tools. OLAP is a basis of SDM in spatial data warehouses, and SDM is the deepening of spatial OLAP. SDM's efficiency is improved when it is combined with spatial OLAP. In fact, it is more efficient to uncover knowledge in data warehouses than in original databases because the data in spatial data warehouses - after a process of selection, cleaning, and integration-provide a good foundation for SDM. When extracting patterns from data warehouses, data mining is often driven by validation and discovery. Validation-driven data mining is supervised by a user hypothesis and is used to verify or deny the assumption through the use of the tools of recursive query at a relatively low level; discovery-driven data mining automatically finds out unknown but useful patterns from a large amount of data. 


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