Two approaches to the integration of heterogeneous data warehouses

Abstract

In this paper we address the problem of integrating independent and possibly heterogeneous data warehouses, a problem that has received little attention so far, but that arises very often in practice.We start by tackling the basic issue of matching heterogeneous dimensions and provide a number of general properties that a dimension matching should fulfill. We then propose two different approaches to the problem of integration that try to enforce matchings satisfying these properties. The first approach refers to a scenario of loosely coupled integration, in which we just need to identify the common information between data sources and perform join operations over the original sources. The goal of the second approach is the derivation of a materialized view built by merging the sources, and refers to a scenario of tightly coupled integration in which queries are performed against the view.We also illustrate architecture and functionality of a practical system that we have developed to demonstrate the effectiveness of our integration strategies.

Collaborative Business Intelligence

“…An approach in this direction is the one proposed in [18]. Given two dimensions belonging to different data marts where a set of mappings between corresponding levels has been manually declared or automatically inferred, three properties (namely coherence, soundness, and consistency) that enable a compatibility check between the two dimensions are defined.…”

“…In [18], the problem of virtual integration of heterogeneous data marts is faced in a loosely-coupled scenario where there is a need for identifying the common information (intuitively, the intersection) between the components while preserving their autonomy. A set of rules to check for dimension compatibility are declared first, then drill-across queries are used to correlate on-the-fly the component data marts.…”

Enforcing strictness in integration of dimensions

“…Torlone [29] defines the mapping between two dimensions as inconsistent if the strictness in the original dimensions is not maintained in the integrated result. For any pair of matching child members where one of the parents member is a null, Torlone proposes to replace the null parent member with the parent of the other child member.…”

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.…”