Efficient management of semantic streaming data using TOWL

Cloud guises an emerging technology to integrate real-time objects into cloud environment. Sensor cloud serves as a middleware for storing and computing sensed data into cloud to efficiently utilize real world data. Linking of sensors and social network services is possible to monitor and access real-time objects. Some of the recent researches, to integrate sensor data from other sources with semantic descriptions lack various QoS parameters. Data Manageability and interoperability for heterogeneous data resources relies heavily on degradation of performance. A middleware framework is created to improve the QoS parameters for retrieving heterogeneous stream of sensor data. The complexity in retrieving historical data comes from classes and properties used to represent sensor readings. It is necessary to design an efficient ontology to represent, indexing and querying time-series data. It is proposed to design an efficient Stream Processing Engine so as to improve the scalability and Performance. One way to improve query processing for time-related data, Time-affixed OWL and TSPARQL are designed. In this system, time is attached in the OWL by enhancing the RDF vocabulary to speed up the query processing and to optimize triple storage management.