Generating data integration mediators that use materialization

Abstract

This paper presents a framework for data integration that is based on using "Squirrel integration mediators" that use materialization to support integrated views over multiple databases. These mediators generalize techniques from active databases to provide incremental propagation of updates to the materialized views. A framework based on "View Decomposition Plans" for optimizing the support of materialized integrated views is introduced. The paper describes the Squirrel mediator generator currently under development, which can generate the mediators based on high-level specifications.The integration of information by Squirrel mediators is expressed primarily through an extended version of a standard query language, that can refer to data from multiple information sources. In addition to materializing an integrated view of data, these mediators can monitor conditions that span multiple sources. The Squirrel framework also provides efficient support for the problem of "object matching", that is, determining when object representations (e.g., O1Ds) in different databases correspond to the same object-in-the-world, even if a universal key is not available.To establish a context for the research, the paper presents a taxonomy that surveys a broad variety of approaches to supporting and maintaining integrated views.

Enterprise modeling and Data Warehousing in Telecom Italia

“…We report part of these assertions in Table 2. Through these assertions we can define the relations between the Conceptual Model and their sources, as discussed below: Assertions 8,9,11,12,13,14 Assertion 16, stating the conceptual equivalence between two entities in two sources, was already discussed.…”

“…This extends the traditional LAV approach to integration, where the information content of each data source is defined in terms of a query over (possibly materialized) global relations constituting the corporate view of data [1,[7][8][9][10]. The LAV approach is in contrast to the global-as-view (GAV) approach for data integration [11][12][13][14][15][16][17], typically proposed in Data Warehousing [2,18]. Such an approach requires, for each information need, to specify the corresponding query in terms of the data at the sources.…”

Data-centric intelligent information integration—from concepts to automation

“…In the materialized integration scenario Zhou et al 1996, the data is stored in a central data repository, since the early 1990s called a data warehouse Jarke et al 2003. While research has focused on the fundamental and transformational aspects of this, very similar to the virtual integration scenario, industrial practice is at least equally interested in the resource-constrained scheduling of the huge bulk tasks involved in operating this architecture with the enormous data sizes of today.…”

Information integration: conceptual modeling and reasoning support

“…in the WHIPS system [20,37], in which information is not represented at the conceptual level. The lack of a conceptual level is shared by the SQUIRREL system [40,39,38,23]. However, within SQUIRREL it is also possible to take into account the case of virtual views.…”

“…In this case, the basic issue is to design suitable software modules that access the sources in order to fulfill the predefined information requirements. Several information integration (both virtual and materialized) projects, such as TSIMMIS [12,33], Squirrel [38,23], and WHIPS [20,37] follow this idea. They do not require an explicit notion of integrated data schema, and rely on two kinds of software components: wrappers that encapsulate sources, converting the underlying data objects to a common data model, and mediators [35] that obtain information from one or more wrappers or other mediators, refine this information by integrating and resolving conflicts among the pieces of information from the different sources, and provide the resulting information either to the user or to other mediators.…”