In this demo, we present an ontology-based visual query system, namely OptiqueVQS, for querying static and dynamic data sources. The demo will be based on industrial scenarios provided by Statoil ASA and Siemens AG.
The operational e ciency and e ectiveness of business processes rely on domain
experts' agility in interpreting data into actionable business information. In a
typical value creation scenario, domain experts depend on IT experts to extract
and deliver relevant data by translating their information needs into
extracttransform-load (ETL) processes. Such a work ow is too time intensive,
heavyweight and in exible; therefore, domain experts need to extract and analyse the
data of interest directly. Although querying is an essential instrument for meeting
ad hoc information needs, domain experts do not necessarily have technical
skills and knowledge on databases and formal query languages, such as SQL and
SPARQL, to extract data. In this context, visual methods for query formulation
undertake the challenge of making querying independent of users' technical skills
and knowledge on the underlying textual query language and data structure [
To this end, we have developed an ontology-based visual query system, namely
OptiqueVQS [
In this demo, we present OptiqueVQS over two industrial scenarios, involving
static and dynamic data, provided by Statoil ASA [
OptiqueVQS is a visual query system (VQS), which is a system of interactions rather than a visual query language (VQL) with a formal syntax and notation [16]. It combines multiple representation and interaction paradigms in a widget-based architecture to address a broad range of user and task types. One can formulate tree-shaped conjunctive queries with aggregation. Expressivity is intentionally compromised for the sake of usability; more precisely, frequently used query types presenting less complexity to the users are of priority.
Figure 1 and Figure 2 are examples for querying static [18] and dynamic data [19] sources respectively. OptiqueVQS generates SPARQL for static data and STARQL [13] for dynamic data. Users formulate queries by selecting conceptrelationship pairs from a menu-based widget, and constraining and selecting attributes from a form-based widget (see Figure 1). Formulated queries are presented as trees, where typed variables appear as nodes and object properties appear as arcs. Dynamic properties are coloured in blue and when one is selected users could provide parameters such as slide and window and can select prede ned templates (see Figure 2).
The main component of OptiqueVQS's backend is a graph projector [19].
This component uses a technique to extract a suitable graph-like structure from
a set of OWL 2 axioms [
Finally, a set of user experiments with casual users [17] and domain experts [18,19] has been conducted. The results have revealed high e ciency and e ectiveness. 3
This demo will be based on anonymised Siemens relational stream sensor data gathered from di erent appliances, such as steam turbines and generators, and on a public fragment of Statoil data regarding the petroleum activities. Acknowledgments. This work was funded by the EU FP7 Grant \Optique" (agreement no. 318338). 11. Kharlamov, E., Jimenez-Ruiz, E., Zheleznyakov, D., Bilidas, D., Giese, M., Haase, P., Horrocks, I., Kllapi, H., Koubarakis, M., O zcep, O ., Rodr guez-Muro, M., Rosati, R., Schmidt, M., Schlatte, R., Soylu, A., Waaler, A.: Optique: Towards OBDA Systems for Industry. In: Proceedings of the ESWC 2013 Satellite Events. LNCS, vol. 7955, pp. 125{140. Springer (2013) 12. Kharlamov, E., Solomakhina, N., Ozcep, O.L., Zheleznyakov, D., Hubauer, T., Lamparter, S., Roshchin, M., Soylu, A., Watson, S.: How Semantic Technologies Can Enhance Data Access at Siemens Energy. In: Proceedings of the 13th International Semantic Web Conference (ISWC 2014). LNCS, vol. 8796, pp. 601{619. Springer (2014) 13. Neuenstadt, C., Moller, R., Ozcep, O.: OBDA for Temporal Querying and Streams with STARQL. In: Proceedings of the First Workshop on High-Level Declarative Stream Processing (HiDeSt 2015). CEUR Workshop Proceedings, vol. 1447, pp. 70{75. CEUR-WS.org (2015) 14. Poggi, A., Lembo, D., Calvanese, D., De Giacomo, G., Lenzerini, M., Rosati, R.:
Linking Data to Ontologies. Journal on Data Semantics X 10, 133{173 (2008) 15. Soylu, A., Giese, M., Jimenez-Ruiz, E., Kharlamov, E., Zheleznyakov, D., Horrocks, I.: Towards Exploiting Query History for Adaptive Ontology-Based Visual Query Formulation. In: Proceedings of the 8th Research Conference on Metadata and Semantics Research (MTSR 2014). CCIS, vol. 478, pp. 107{119. Springer (2014) 16. Soylu, A., Giese, M., Jimenez-Ruiz, E., Kharlamov, E., Zheleznyakov, D., Horrocks, I.: Ontology-based End-user Visual Query Formulation: Why, what, who, how, and which? Universal Access in the Information Society (in press) 17. Soylu, A., Giese, M., Jimenez-Ruiz, E., Vega-Gorgojo, G., Horrocks, I.: Experiencing OptiqueVQS { a multi-paradigm and ontology-based visual query system for endusers. Universal Access in the Information Society 15(1), 129{152 (2016) 18. Soylu, A., Giese, M., Schlatte, R., Jimenez-Ruiz, E., Ozcep, O., Brandt, S.: Domain Experts Sur ng on Stream Sensor Data over Ontologies. In: Proceedings of the 1st International Workshop on Semantic Web Technologies for Mobile and Pervasive Environments (SEMPER 2016). CEUR Workshop Proceedings, vol. 1588. CEURWS.org (2016) 19. Soylu, A., Kharlamov, E., Zheleznyakov, D., Jimenez-Ruiz, E., Giese, M., Horrocks, I.: Ontology-based Visual Query Formulation: An Industry Experience. In: Proceedings of the 11th International Symposium on Visual Computing (ISVC 2015). LNCS, vol. 9474, pp. 842{854. Springer (2015)