Database Integration Task

Context:
- Input: Two or more Database Systems.
- output: a Consistent Database.
- It can be supported by a Database Schema Matching Task, and a Database Transformation Task.
- It can range from being a Database Merging Task to being a Multi-database Logical View Creation Task.
- It can require the use of a Data Integration Design Pattern, such as CDC.
- …
Example(s):
- an Ontology Integration Task.
- an Taxonomy Integration Task.
- a Product Database Integration Task.
- …
Counter-Example(s):
- a Data Cleaning Task.
- an Information Extraction Task.
- a Data Transformation Task.
See: Information Fussion Task, Integration Task, Datawarehousing Task.

References

http://en.wikipedia.org/wiki/Data_integration
- Data integration involves combining data residing in different sources and providing users with a unified view of these data. This process becomes significant in a variety of situations both commercial (when two similar companies need to merge their databases) and scientific (combining research results from different bioinformatics repositories, for example). Data integration appears with increasing frequency as the volume and the need to share existing datan explodes. It has become the focus of extensive theoretical work, and numerous open problems remain unsolved. In management circles, people frequently refer to data integration as “Enterprise Information Integration” (EII).

Maurizio Lenzerini. (2002). “Data Integration: a theoretical perspective.” Invited Tutorial at Twenty-First ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. doi:10.1145/543613.543644
- ABSTRACT: Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. The problem of designing data integration systems is important in current real world applications, and is characterized by a number of issues that are interesting from a theoretical point of view. This document presents on overview of the material to be presented in a tutorial on data integration. The tutorial is focused on some of the theoretical issues that are relevant for data integration. Special attention will be devoted to the following aspects: modeling a data integration application, processing queries in data integration, dealing with inconsistent data sources, and reasoning on queries.

(Madhavan et al., 2001) ⇒ Jayant Madhavan, Philip A. Bernstein, and Erhard Rahm. (2001). “Generic Schema Matching with Cupid.” In: Proceedings of the 27th International Conference on Very Large Data Bases