Meta-data based mediator generation

Mediators are a critical component of any data warehouse; they transform data from source formats to the warehouse representation while resolving semantic and syntactic conflicts. The close relationship between mediators and databases requires a mediator to be updated whenever an associated schema is modified. Failure to quickly perform these updates significantly reduces the reliability of the warehouse because queries do not have access to the most current data. This may result in incorrect or misleading responses, and reduce user confidence in the warehouse. Unfortunately, this maintenance may be a significant undertaking if a warehouse integrates several dynamic data sources. This paper describes a meta-data framework, and associated software, designed to automate a significant portion of the mediator generation task and thereby reduce the effort involved in adapting to schema changes. By allowing the DBA to concentrate on identifying the modifications at a high level, instead of reprogramming the mediator, turnaround time is reduced and warehouse reliability is improved.

[1]  Gio Wiederhold,et al.  Mediators in the architecture of future information systems , 1992, Computer.

[2]  Nick Roussopoulos,et al.  Efficient refreshment of data warehouse views , 1996 .

[3]  Vipul Kashyap,et al.  InfoSleuth: agent-based semantic integration of information in open and dynamic environments , 1997, SIGMOD '97.

[4]  Surajit Chaudhuri,et al.  Maintenance of Materialized Views: Problems, Techniques, and Applications. , 1995 .

[5]  Terence Critchlow,et al.  Automatic Generation of Warehouse Mediators Using an Ontology Engine , 1998, KRDB.

[6]  Vipul Kashyap,et al.  Observer: an approach for query processing in global information systems based on interoperation across pre-existing ontologies , 1996, Proceedings First IFCIS International Conference on Cooperative Information Systems.

[7]  Calton Pu,et al.  An adaptive approach to query mediation across heterogeneous information sources , 1996, Proceedings First IFCIS International Conference on Cooperative Information Systems.

[8]  Vipul Kashyap,et al.  Domain Specific Ontologies for Semantic Information Brokering on the Global Information Infrastructure , 1998 .

[9]  W. H. Inmon,et al.  Building the data warehouse , 1992 .

[10]  Patrick Valduriez,et al.  Scaling heterogeneous databases and the design of Disco , 1996, Proceedings of 16th International Conference on Distributed Computing Systems.

[11]  Ramanathan V. Guha,et al.  Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project , 1990 .

[12]  Thomas R. Gruber,et al.  Ontolingua: a mechanism to support portable ontologies , 1991 .

[13]  R GruberThomas.  Toward principles for the design of ontologies used for knowledge sharing , 1995 .

[14]  Jennifer Widom,et al.  Integrating and Accessing Heterogeneous Information Sources in TSIMMIS , 1994 .

[15]  Carole D. Hafner,et al.  Ontological Foundations for Biology Knowledge Models , 1996, ISMB.

[16]  Natalya F. Noy,et al.  The state of art in ontology design , 1997 .

[17]  Michael Stonebraker,et al.  On rules, procedure, caching and views in data base systems , 1990, SIGMOD '90.

[18]  Jennifer Widom,et al.  The TSIMMIS Project: Integration of Heterogeneous Information Sources , 1994, IPSJ.

[19]  Terence J. Critchlow,et al.  SCHEMA COERCION: USING DATABASE META-INFORMATION TO FACILITATE DATA TRANSFER , 1997 .