From VLDB to VMLDB (Very MANY Large Data Bases): Dealing with Large-Scale Heterogeneity

The popularity of distributed computing environments and the growth of the “Information SuperHighway” have dramatically increased the number of data bases available for use. Unfortunately, there are significant challenges to be overcome. One particular problem is context interchange, whereby each source of information and potential receiver of that information may operate with a different context, leading to largescale semantic heterogeneity. A context is the collection of implicit assumptions about the context dejinition (i.e., meaning) and context characteristics (i.e., quality) of the information. This paper describes various forms of context challenges and examples of potential context mediation services, such as data semantics acquisition, data quality attributes, and evolving semantics and quality, that can mitigate the problem.