Context Interchange as a Scalable Solution to Interoperating Amongst Heterogeneous Dynamic Services
Hongwei (Harry) Zhu
University of Massachusetts Lowell; Massachusetts Institute of Technology (MIT); Old Dominion University
Massachusetts Institute of Technology (MIT) - Sloan School of Management
MIT Sloan Working Paper No. 4514-04; CISL Working Paper No. 2004-16
Many online services access a large number of autonomous data sources and at the same time need to meet different user requirements. It is essential for these services to achieve semantic interoperability among these information exchange entities. In the presence of an increasing number of proprietary business processes, heterogeneous data standards, and diverse user requirements, it is critical that the services are implemented using adaptable, extensible, and scalable technology. The Context Interchange (COIN) approach, inspired by similar goals of the Semantic Web, provides a robust solution. In this paper, we describe how COIN can be used to implement dynamic online services where semantic differences are reconciled on the fly. We show that COIN is flexible and scalable by comparing it with several conventional approaches. With a given ontology, the number of conversions in COIN is quadratic to the semantic aspect that has the largest number of distinctions. These semantic aspects are modeled as modifiers in a conceptual ontology; in most cases the number of conversions is linear with the number of modifiers, which is significantly smaller than traditional hard-wiring middleware approach where the number of conversion programs is quadratic to the number of sources and data receivers. In the example scenario in the paper, the COIN approach needs only 5 conversions to be defined while traditional approaches require 20,000 to 100 million. COIN achieves this scalability by automatically composing all the comprehensive conversions from a small number of declaratively defined sub-conversions.
Number of Pages in PDF File: 14
Keywords: ontology, semantics, scalability, data integration, heterogeneous sources
Date posted: October 29, 2004
© 2015 Social Science Electronic Publishing, Inc. All Rights Reserved.
This page was processed by apollo5 in 0.438 seconds