The problem of representing RDF data using charts, dashboards, maps and so on has become pressing, in particular to prove the value of the Semantic Web to enhance the analysis of business data. State of the art solutions focus on mapping query results to a specific chart type or view and then manually writing the procedure that creates the final dashboard. Whenever a different visualization model is required, the mapping process needs to be repeated. Furthermore, even if several different mappings are pre-defined and available to the user there may be several situations which cannot be predicted because they depend on the specific analysis the user wants to carry out. In this paper we propose a semi-automatic approach that generates various charts from SPARQL queries over data represented as RDF graphs, we introduce and describe the generic approach and present a use case scenario in the context of service monitoring.

1. Toward Automatic Generation of SPARQL Result Set Visualizations A Use Case in Service Monitoring Leida Marcello EBTIC (Etisalat BT Innovation Centre), Khalifa University - Abu Dhabi, U.A.E. http://www.ebtic.org [email_address]

4. Federated queries Enabling interesting scenarios even without AI support: “ Query WiMAX customer complains, together with related location and weather situation of the day of the complain; this may lead to discovering that some devices do not perform well in some particular conditions.”

16. Proposed Approach: Chart Ontology The Chart Ontology imports the Label Ontology. This ontology defines the concept of ResultSet and VisualizationMethod . Which are the basic concepts used to model the query and the method used to visualize the results. The variables in the results set are instances of the Label Ontology.

17. Proposed Approach: Chart Ontology Equivalent Concepts and Rules definition The Chart Ontology is at the core of the inference process. Here the various rules (SWRL) and equivalent concepts (defined using OWL-DL) are defined. These rules and logic concepts are used in the inference process in order to automatically infer the set of suitable visualization for a given result set. The concept of ResultSet is extended by equivalent concepts: An inference process will re-classify the instances of the ResultsSet with the equivalent concepts. Once this is done the system will apply the rules:

23. Use case: services monitoring PREFIX rdfs:<http://www.w3.org/2000/01/rdf-schema#> PREFIX db:<http://localhost:2020/resource/> PREFIX owl:<http://www.w3.org/2002/07/owl#> PREFIX xsd:<http://www.w3.org/2001/XMLSchema#> PREFIX map:<file:/D:/d2r-server-0.7/ServMonMapping.n3#> PREFIX rdf:<http://www.w3.org/1999/02/22-rdf-syntax-ns#> PREFIX vocab:<http://localhost:2020/vocab/resource/> SELECT ?serviceEndPoint ?avgeResponseTime ?timestamp WHERE { ?monitor vocab:result_timestamp ?timestamp. ?monitor vocab:result_serviceEndpoint ?serviceEndPoint. ?monitor vocab:result_averageResponseTime ?avgeResponseTime. } ORDER BY ?serviceEndPoint ?timestamp ?avgeResponseTime

24. Conclusions & Future Work We presented a promising approach for the automatic generation of charts from SPARQL queries. System exploits inference processes to generate appropriate charts. Major benefit is the automatic on-the-fly generation of charts. Future work will consider extending the proof of concept implementation presented in this paper to a generic framework. Capture user interaction with the chart in order to automatically generate new SPARQL queries that will consequently lead to new views of the data; interesting in the case where the data to analyze can be linked to an external data such as Linked Data Project. Our approach is particularly relevant in with Linked Data, where the queries that can be submitted can not be predicted at design time because of the high dimensionality and the highly connected nature of the data. This, in turn, may lead to the visualization of data that initially was not considered, which is a situation that current BI systems can not handle.

25. Thank you for your attention!