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, which can be easily formulated with our visual query formulation system. OPTIQUE can then automatically enrich these queries, translate them into a large collection of low-level data queries, and finally optimise and efficiently execute the collection in a heavily distributed environment. © 2016 ACM.
|Title of host publication||Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems|
|Number of pages||4|
|Place of Publication||New York, NY, USA|
|Publication status||Published - 13.06.2016|
|Event||10th ACM International Conference on Distributed and Event-Based Systems - Beckman Center of the National Academies of Sciences and Engineering, Irvine, United States|
Duration: 20.06.2016 → 24.06.2016
Conference number: 122247