Semantic enrichment of big data sources for effective query processing

Abstract

Owing to the recent technological advancements and growth of voluminous online information the big data analytics has become increasingly crucial in recent years Accordingly there is a huge amount of structured semi structured and unstructured data that has been generated from a variety of information sources In big data environment recognizing the context of the information hidden in the unstructured data is a challenging task regardless of semantic annotation Also the volume of streaming data tremendously increases with the increase of emerging modern applications about sensor networks Hence performing the big data analytics and query processing for the rapidly and dynamically arriving data stream is an arduous task The existing semantic annotation research works lack to annotate the multiple domains from heterogeneous sources of real time applications Hence developing an intelligent annotation framework is necessary to ensure better performance scalability flexibility and robustness in big data infrastructure Moreover there is an essential need of providing the precise result with quick response to the users for the natural language queries posed by the users Thus this dissertation targets in resolving these constraints in a big data environment by providing significant contributions namely MOUNT and SEASOR methodologies for static databases and dynamic streaming data correspondingly. newline