From VLDB to VMLDB (Very MANY Large Data Bases): Dealing with Large-Scale Semantic Heterogenity
From VLDB to VMLDB (Very MANY Large Data Bases): Dealing with Large-Scale Semantic Heterogenity
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发表时间：
1995-09
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通讯作者：
S. Madnick
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作者：
S. Madnick
分布式计算环境的流行和“信息高速公路”的发展极大地增加了可供使用的数据库的数量。不幸的是，有重大的挑战需要克服。一个特别的问题是上下文交换，即每个信息源和该信息的潜在接收者可能在不同的上下文中操作，从而导致大规模的语义异构。上下文是关于信息的上下文定义（即意义）和上下文特征（即质量）的隐式假设的集合。本文描述了各种形式的上下文挑战和潜在上下文中介服务的示例，例如数据语义获取、数据质量属性以及不断发展的语义和质量，这些都可以缓解问题。
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.