Fuzzy Integration of Web Data Sources for Data Warehousing

Araque, Francisco; Salguero, Alberto; Carrasco, R.A.; Delgado, Cecilia

doi:10.1007/978-3-540-75867-9_151

“…Each Data Integration Processor accesses to its corresponding data source according to the temporal and spatial requirements obtained in the integration stage. A global Data Integrator Processor uses a parallel, fuzzy data integration algorithm to integrate the data (Araque et al, 2007b).…”

Section: Dw Architecturementioning

confidence: 99%

Using Ontology Meta Data for Data Warehousing

Salguero¹,

Araque²,

Delgado³

2008

Proceedings of the Tenth International Conference on Enterprise Information Systems

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One of the most complex issues of the integration and transformation interface is the case where there are multiple sources for a single data element in the enterprise Data Warehouse (DW). There are many facets due to the number of variables that are needed in the integration phase. However we are interested in the integration of temporal and spatial problem due to the nature of DWs. This paper presents our ontology based DW architecture for temporal integration on the basis of the temporal and spatial properties of the data and temporal characteristics of the data sources. The proposal shows the steps for the transformation of the native schemes of the data sources into the DW scheme and end user scheme and the use of an ontology model as the common data model.

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“…Actually we are working about using a parallel fuzzy algorithm for integration process in order to obtain more precise data in the DW. The result is more precise because several refreshments of data sources are semantically integrated in a unique DW fact (Carrasco, Araque, Salguero, & Vila, 2008), (Salguero A. , Araque, Carrasco, Vila, & Martínez, 2007), (Araque, Carrasco, Salguero, Delgado, & Vila, 2007).…”

Section: Discussionmentioning

confidence: 99%

Methodology for Improving Data Warehouse Design using Data Sources Temporal Metadata

Araque

¹

,

Salguero

²

,

Delgado

³

Progressive Methods in Data Warehousing and Business Intelligence

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One of the most complex issues of the integration and transformation interface is the case where there are multiple sources for a single data element in the enterprise Data Warehouse (DW). There are many facets due to the number of variables that are needed in the integration phase. This chapter presents our DW architecture for temporal integration on the basis of the temporal properties of the data and temporal characteristics of the data sources. If we use the data arrival properties of such underlying information sources, the Data Warehouse Administrator (DWA) can derive more appropriate rules and check the consistency of user requirements more accurately. The problem now facing the user is not the fact that the information being sought is unavailable, but rather that it is difficult to extract exactly what is needed from what is available. It would therefore be extremely useful to have an approach which determines whether it would be possible to integrate data from two data sources (with their respective data extraction methods associated). In order to make this decision, we use the temporal properties of the data, the temporal characteristics of the data sources, and their extraction methods. In this chapter, a solution to this problem is proposed.

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