基于本体的流与静态关系数据与Optique集成

E. Kharlamov, S. Brandt, Ernesto Jiménez-Ruiz, Y. Kotidis, S. Lamparter, T. Mailis, C. Neuenstadt, Ö. Özçep, C. Pinkel, C. Svingos, D. Zheleznyakov, Ian Horrocks, Y. Ioannidis, R. Möller
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引用次数: 56

摘要

实时处理来自多个异构数据流和静态数据库的数据是许多工业场景中的典型任务,例如大型机器的诊断。复杂的诊断任务可能需要对此类数据进行多达数百次查询的集合。尽管这些查询中有许多检索相同类型的数据,例如温度测量值,但它们访问的数据源在结构上是不同的。在这项工作中,我们展示了在我们的系统光学中实现的语义技术如何通过提供集成异构数据的抽象层(本体)来简化这种复杂的诊断。简而言之,optique允许用几个高级语义查询来表达复杂的诊断任务。然后,系统可以自动丰富这些查询,将它们转换为具有大量低级数据查询的集合,并最终在高度分布式的环境中优化并有效地执行集合。我们将在西门子的真实场景中演示光学的优点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ontology-Based Integration of Streaming and Static Relational Data with Optique
Real-time processing of data coming from multiple heterogeneous data streams and static databases is a typical task in many industrial scenarios such as diagnostics of large machines. A complex diagnostic task may require a collection of up to hundreds of queries over such data. Although many of these queries retrieve data of the same kind, such as temperature measurements, they access structurally different data sources. In this work we show how Semantic Technologies implemented in our system optique can simplify such complex diagnostics by providing an abstraction layer---ontology---that integrates heterogeneous data. In a nutshell, optique allows complex diagnostic tasks to be expressed with just a few high-level semantic queries. The system can then automatically enrich these queries, translate them into a collection with a large number of low-level data queries, and finally optimise and efficiently execute the collection in a heavily distributed environment. We will demo the benefits of optique on a real world scenario from Siemens.
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