By Jerome Darmont, Omar Boussaid
In lots of choice aid fields, the information that's exploited is changing into increasingly more advanced. To take this phenomenon under consideration, classical architectures of knowledge warehouses or information mining algorithms has to be thoroughly re-evaluated. "Processing and coping with advanced info for choice aid" offers readers with an outline of the rising box of complicated facts processing by means of bringing jointly a variety of study experiences and surveys in several subfields, and by way of highlighting the similarities among the several facts, concerns, and techniques. This publication offers with very important issues, reminiscent of: complicated facts warehousing, together with spatial, XML, and textual content warehousing; and complicated info mining, together with distance metrics and similarity measures, development administration, multimedia, and gene series mining.
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The following discussion regarding the existing work on dimensional modelling of data warehouses indicates that the issue is not completely addressed. The elicitation of requirements and their use to data warehouse design is a significant and, as yet, an unaddressed issue, hence we focus on this aspect. Since the introduction of dimensional modelling, which revolves around facts and dimensions, several design techniques have been proposed to capture multidimensional data (MD) at the conceptual level.
The advantages of this algebra are twofold: It is formally defined, and it is a good representative of the class of algebras for cube-oriented models (Vassiliadis, 1998; Vassiliadis & Sellis, 1999), which are close to our model. Besides the basic operators defined in the original algebra (LevelClimbing, Packing, FunctionApplication, Projection and Dicing), we introduce the following operators: MeasureClimbing, SpatialFunctionApplication and CubeDisplay. The MeasureClimbing operator is introduced to enable the scaling up of spatial measures to different granularities; the SpatialFunctionApplication operator performs aggregation of spatial measures; CubeDisplay simply visualizes a cube as a map.
A major research issue is how to obtain summarized data out of a database of trajectories. The problem is complex because it requires the comparison and classification of trajectories. For that purpose, the notion of trajectory similarity is used. It means that trajectories are classified to be the same when they are sufficiently similar. , 2002). A spatial data warehouse of trajectories could provide the unifying representation framework to integrate data mining techniques for data classification.