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Rewrite Techniques for Performance Optimization of Schema Matching Processes

机译:用于模式匹配过程性能优化的重写技术

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A recurring manual task in data integration, ontology alignment or model management is finding mappings between complex meta data structures. In order to reduce the manual effort, many matching algorithms for semi-automatically computing mappings were introduced.rnUnfortunately, current matching systems severely lack performance when matching large schemas. Recently, some systems tried to tackle the performance problem within individual matching approaches. However, none of them developed solutions on the level of matching processes. In this paper we introduce a novel rewrite-based optimization technique that is generally applicable to different types of matching processes. We introduce filter-based rewrite rules similar to predicate push-down in query optimization. In addition we introduce a modeling tool and recommendation system for rewriting matching processes. Our evaluation on matching large web service message types shows significant performance improvements without losing the quality of automatically computed results.
机译:数据集成,本体对齐或模型管理中的一项重复性手动任务是寻找复杂的元数据结构之间的映射。为了减少手工工作,引入了许多用于半自动计算映射的匹配算法。不幸的是,当前的匹配系统在匹配大型模式时严重缺乏性能。最近,一些系统试图在单个匹配方法中解决性能问题。但是,他们都没有在匹配过程级别上开发解决方案。在本文中,我们介绍了一种新颖的基于重写的优化技术,该技术通常适用于不同类型的匹配过程。我们在查询优化中引入了类似于谓词下推的基于过滤器的重写规则。另外,我们引入了用于重写匹配过程的建模工具和推荐系统。我们对匹配大型Web服务消息类型的评估显示出显着的性能改进,而又不损失自动计算结果的质量。

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