TY - JOUR
TI - Towards analytics aware ontology based access to static and streaming data
AU - Kharlamov, E.
AU - Kotidis, Y.
AU - Mailis, T.
AU - Neuenstadt, C.
AU - Nikolaou, C.
AU - Özçep, Ö.
AU - Svingos, C.
AU - Zheleznyakov, D.
AU - Brandt, S.
AU - Horrocks, I.
AU - Ioannidis, Y.
AU - Lamparter, S.
AU - Möller, R.
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PY - 2016
VL - 9982 LNCS
TODO - null
SP - 344-362
PB - Springer-Verlag
SN - null
TODO - 10.1007/978-3-319-46547-0_31
TODO - Query languages;  Turbines, Analytical functions;  Distributed streaming;  Industrial scenarios;  Ontology-based;  Process analytical;  Query optimisation;  Real-time analytics;  Streaming data, Semantic Web
TODO - Real-time analytics that requires integration and aggregation of heterogeneous and distributed streaming and static data is a typical task in many industrial scenarios such as diagnostics of turbines in Siemens. OBDA approach has a great potential to facilitate such tasks; however, it has a number of limitations in dealing with analytics that restrict its use in important industrial applications. Based on our experience with Siemens, we argue that in order to overcome those limitations OBDA should be extended and become analytics, source, and cost aware. In this work we propose such an extension. In particular, we propose an ontology, mapping, and query language for OBDA, where aggregate and other analytical functions are first class citizens. Moreover, we develop query optimisation techniques that allow to efficiently process analytical tasks over static and streaming data. We implement our approach in a system and evaluate our system with Siemens turbine data. © Springer International Publishing AG 2016.
ER -