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We present a description and analysis of the data access challenge in Siemens Energy. We advocate Ontology Based Data Access (OBDA) as a suitable Semantic Web driven technology to address the challenge. We derive requirements for applying OBDA in Siemens, review existing OBDA systems and discuss their limitations with respect to the Siemens requirements. We then introduce the Optique platform as a suitable OBDA solution for Siemens. The platform is based on a number of novel techniques and components including a deployment module, BootOX for ontology and mapping bootstrapping, a query language STARQL that allows for a uniform querying of both streaming and static data, a highly optimised backend, ExaStream, for processing such data, and a query formulation interface, OptiqueVQS, that allows to formulate STARQL queries without prior knowledge of its formal syntax. Finally, we describe our installation and evaluation of the platform in Siemens.

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 fleet of up to hundreds of queries over such data. Although many of these queries retrieve data of the same kind like temperature measurements, they are different since they access structurally different data sources. We have investigated how Semantic Technologies can make such complex diagnostics simpler by providing an abstraction semantic layer that integrates heterogeneous data. We developed the system OPTIQUE to put our ideas in practice. In a nutshell, OPTIQUE allows to express complex diagnostic tasks with just a few high-level semantic queries. Then, the system can automatically enrich these queries, translate them into a fleet with a large number of low-level data queries, and finally optimise and efficiently execute the fleet in a heavily distributed environment. We will demo the benefits of OP-TIQUE on a real world scenario of Siemens Energy. For this purpose we prepared anonymised streaming and static data relevant to 950 Siemens power generating turbines with more than 100, 000 sensors and deployed OPTIQUE on multiple distributed environments with up to 128 nodes. By registering and monitoring continuous semantic high-level queries that combine streaming and static data the demo attendees will be able to see how OPTIQUE makes diagnostics of turbines easy. They will also see how OPTIQUE can handle more than a thousand concurrent complex diagnostic tasks that integrate heterogeneous data in real-time with a 10 TB/day throughput. Finally, they will see that creating a semantic layer, such as the one over the Siemens demo data, can be done in realistic time with the help of our bootstrapping interactive system.

Five varieties of Ocimum basilicum L. namely lettuce, cinnamon, minimum, latifolia, and violetto were separately cultivated in field and greenhouse in the island Kefalonia (Greece). The effect of successive harvesting to the essential oil content was evaluated. In total 23 samples of essential oils (EOs) were analyzed by GC-FID and GC-MS. Ninety-six constituents, which accounted for almost 99% of the oils, were identified. Cluster analysis was performed for all of the varieties in greenhouse and field conditions, in order to investigate the possible differentiation on the chemical composition of the essential oils, obtained between harvests during growing period. Each basil variety showed a unique chemical profile, but also the essential oil composition within each variety seems to be differentiated, affected by the harvests and the cultivation site.

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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.This work was partially funded by the EU project Optique (FP7-ICT-318338) and the EPSRC projects MaSI 3 , DBOnto, and ED 3 arXiv:1607.05351v2 [cs.AI]

Fuzzy Description Logics (DLs) provide a means for representing vague knowledge about an application domain. In this paper, we study fuzzy extensions of conjunctive queries (CQs) over the DL SROIQ based on finite chains of degrees of truth. To answer such queries, we extend a well-known technique that reduces the fuzzy ontology to a classical one, and use classical DL reasoners as a black box. We improve the complexity of previous reduction techniques for finitely valued fuzzy DLs, which allows us to prove tight complexity results for answering certain kinds of fuzzy CQs. We conclude with an experimental evaluation of a prototype implementation, showing the feasibility of our approach.

Fuzzy Description Logics (DLs) generalize crisp ones by providing membership degree semantics for concepts and roles. A popular technique for reasoning in fuzzy DL ontologies is by providing a reduction to crisp DLs and then employ reasoning in the crisp DL. In this paper we adopt this approach to solve conjunctive query (CQ) answering problems for fuzzy DLs. We give reductions for Gödel, and Łukasiewicz variants of fuzzy SROIQ and two kinds of fuzzy CQs. The correctness of the proposed reduction is proved and its complexity is studied for different fuzzy variants of SROIQ.