Querying industrial stream-temporal data: An ontology-based visual approach¹

Issue title: Semantic Web technologies for mobile and pervasive environments

Guest editors: Antonis Bikakis, Thanos G. Stavropoulos and Georgios Meditskos

Article type: Research Article

Authors: Soylu, Ahmet^{a; *} | Giese, Martin^b | Schlatte, Rudolf^b | Jimenez-Ruiz, Ernesto^b | Kharlamov, Evgeny^c | Özçep, Özgür^d | Neuenstadt, Christian^d | Brandt, Sebastian^e

Affiliations: [a] Department of Computer Science, Norwegian University of Science and Technology, Gjøvik, Norway. E-mail: [email protected] | [b] Department of Informatics, University of Oslo, Oslo, Norway. E-mails: [email protected], [email protected], [email protected] | [c] Department of Computer Science, University of Oxford, Oxford, UK. E-mail: [email protected] | [d] Institute of Information Systems, University of Lübeck, Lübeck, Germany. E-mails: [email protected], [email protected] | [e] Corporate Technology, Research and Technology Center, Siemens AG, Munich, Germany. E-mail: [email protected]

Correspondence: [*] Corresponding author. E-mail: [email protected].

Note: [1] This work was funded by the EU FP7 Grant “Optique” (agreement 318338), and by the EPSRC projects MaSI3, DBOnto, and ED3.

Abstract: An increasing number of sensors are being deployed in business-critical environments, systems, and equipment; and stream a vast amount of data. The operational efficiency and effectiveness of business processes rely on domain experts’ agility in interpreting data into actionable business information. A domain expert has extensive domain knowledge but not necessarily skills and knowledge on databases and formal query languages. Therefore, centralised approaches are often preferred. These require IT experts to translate the information needs of domain experts into extract-transform-load (ETL) processes in order to extract and integrate data and then let domain experts apply predefined analytics. Since such a workflow is too time intensive, heavy-weight and inflexible given the high volume and velocity of data, domain experts need to extract and analyse the data of interest directly. Ontologies, i.e., semantically rich conceptual domain models, present an intelligible solution by describing the domain of interest on a higher level of abstraction closer to the reality. Moreover, recent ontology-based data access (OBDA) technologies enable end users to formulate their information needs into queries using a set of terms defined in an ontology. Ontological queries could then be translated into SQL or some other database query languages, and executed over the data in its original place and format automatically. To this end, this article reports an ontology-based visual query system (VQS), namely OptiqueVQS, how it is extended for a stream-temporal query language called STARQL, a user experiment with the domain experts at Siemens AG, and STARQL’s query answering performance over a proof of concept implementation for PostgreSQL.

Keywords: Visual query formulation, ontology-based data access, temporal data, stream sensor data, data retrieval, usability

DOI: 10.3233/AIS-160415

Journal: Journal of Ambient Intelligence and Smart Environments, vol. 9, no. 1, pp. 77-95, 2017