Automated Integration of Heterogeneous Data Warehouse Schemas

Abstract

A federated data warehouse is a logical integration of data warehouses applicable when physical integration is impossible due to privacy policy or legal restrictions. In healthcare systems federated data warehouses are a most feasible source of data for deducing guidelines for evidence-based medicine based on data material from different participating institutions. In order to enable the translation of queries in a federated approach, schemas of the federated warehouse and the local warehouses must be matched. In this paper we present a procedure that enables the matching process for schema structures specific to the multidimensional model of data warehouses: facts, measures, dimensions, aggregation levels and dimensional attributes. Similarities between warehouse-specific structures are computed by using linguistic and structural comparison. The calculated values are used to create necessary mappings.

New Frontiers in Business Intelligence: Distribution and Personalization

“…Although the integration of heterogeneous databases has been widely discussed in the literature, only a few works are specifically focused on strategies for data warehouse integration [2,16] and federation [1]. Indeed, in this context, problems related to data heterogeneity are usually solved by ETL (Extraction, Transformation, and Loading) processes that read data from several data sources and load them in a single multidimensional repository to be accessed by users.…”

Towards OLAP query reformulation in peer-to-peer data warehousing

“…The problem of how to define mappings between concepts is not considered. The work proposed in [2] present a complete algorithm for matching multidimensional structures, with a strong emphasis on the process of calculating similarity between complex concepts. However, the data-related aspects -that could enrich the matching definitions-are not considered, and no model is provided to formalize the mapping predicates.…”

“…At step (2), an OLAP query expressed on a peer needs be properly reformulated on the other peers, which is a challenging task due to the presence of aggregation and to the possibility of having information represented at different granularities and under different perspectives in each peer. In this paper we focus on this task.…”

Collaborative Business Intelligence

“…In the context of a federated architecture, with specific reference to the healthcare domain, the work in [24,3] presents an algorithm for matching heterogeneous multidimensional structures, possibly characterized by different granularities for data. Mappings between the local schemata of the data warehouses to be integrated and a given global schema are discovered in a semi-automated manner, based on a measure of similarity between complex concepts.…”

“…It provides a broader base for decision-support and knowledge discovery than each single data warehouse could offer. Large corporations integrate their separately-developed departmental data warehouses; newly merged companies integrate their data warehouses into a central data warehouse; 188 S. Rizzi autonomous but related organizations join together their data warehouses to enforce the decision making process [3].Although the integration of heterogeneous databases has been widely discussed in the literature, only a few works are specifically focused on strategies for data warehouse integration. Two categories of approaches were mainly devised: warehousing approaches, where the integrated data are physically materialized, and federative approaches, where integration is virtual.…”