Research On Semantic Of Streaming Data Under The Distributed Environment And Self-adaptive Mechanism Of System

The study on streaming data is one of the hottest topics among the database circle all over the world. Conventional database technologies have been well developed and widely applied. Unfortunately, they could not be adopted to handle a new kind of data, named streaming data with rapid data arriving speed and huge size of data set.The ability of distributed processing and detect the dynamic data source become hot topics of research of the streaming data processing model recently. How to resolve the problems of heterogeneousness and coordination between the distributed data source is the point of the research. Uses the semantic representation of data source to resolve the possible heterogeneousness in streaming data and increase or decrease data source dynamically, moreover, we can enhance the adaptiveness of the system via the strong reasoning mechanism of semantics.Based on current method of building domain ontology, discusses a methodology of building domain ontology, and create two domain ontologies: streaming data ontology and system status ontology. We support the semantic integration of dynamic streaming data source well by creating the streaming data ontology, and the deduction of ontology basing on the system status is the foundation of self-adaptiveness.The load system is an important topic of the data stream management system in certain fileds, also, it is an important part of the self-adaptive mechanism. By using the current reasoning tools, presents a framwork of integration of dynamic streaming data source and self-adaptive mechanism. Practises an adaptive filter algorithm to reduce the burden of communication between source and central processor, analyses the CPU and memory limited during the running system and proves its advantages through experiment.