Graphical Query Builder in Opportunistic Sensor Networks to discover Sensor Information
This work addresses the challenge of complex query formulation for users in sensor network applications, though it appears incremental as it builds on existing query translation methods.
The paper tackled the problem of users lacking prior knowledge of sensor data or services in opportunistic sensor networks by developing an interactive visual query interface that translates user-friendly graphical queries into machine-understandable SPARQL queries, enabling easier discovery of sensor information.
A lot of sensor network applications are data-driven. We believe that query is the most preferred way to discover sensor services. Normally users are unaware of available sensors. Thus users need to pose different types of query over the sensor network to get the desired information. Even users may need to input more complicated queries with higher levels of aggregations, and requires more complex interactions with the system. As the users have no prior knowledge of the sensor data or services our aim is to develop a visual query interface where users can feed more user friendly queries and machine can understand those. In this paper work, we have developed an Interactive visual query interface for the users. To accomplish this we have considered several use cases and we have derived graphical representation of query from their text based format for those use case scenario. We have facilitated the user by extracting class, subclass and properties from Ontology. To do so we have parsed OWL file in the user interface and based upon the parsed information users build visual query. Later on we have translated the visual query languages into SPARQL query, a machine understandable format which helps the machine to communicate with the underlying technology.