A complete data integration solution can be viewed as an iterative process that consists of three phases, namely analysis, derivation and evolution. The entire process is similar to a software development process with the target application being the derivation rules for the integrated databases. In many cases, data integration requires several iterations of refining the local-to-global database mapping rules before a stable set of rules can be obtained. In particular, the mapping rules, as well as the data model and query model for the integrated databases have to cope with poor data quality in local databases, ongoing local database updates and instance heterogeneities. In this paper, we therefore propose a new object-oriented global data model, known as OORA, that can accommodate attribute and relationship instance heterogeneities in the integrated databases. The OORA model has been designed to allow database integrators and end users to query both the local and resolved instance values using the same query language throughout the derivation and evolution phases of database integration. Coupled with the OORA model, we also define a set of local-to-global database mapping rules that can detect new heterogeneities among databases and resolve instance heterogeneities if situations permit.
Databases and Information Systems
Decision Support Systems
LIM, Ee Peng and CHIANG, Roger Hsiang-Li.
Accommodating Instance Heterogeneities in Database Integration. (2004). Decision Support Systems. 38, (2), 213-231. Research Collection School Of Information Systems.
Available at: https://ink.library.smu.edu.sg/sis_research/58
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