Dynamic continuous query processing over streaming Data

Data streams refers to uninterrupted flow of long sequence of data, e.g. stock quotes, e-commerce data, system logs, sensor networks, network traffic management, etc. A wide range of query scenarios emerge naturally in these applications. Stream databases raise new challenges for query processing due to both the streaming nature of data which constantly changes over time and the wider range of queries proposed by the user when compared with the traditional database. In traditional databases Index structures have been effectively employed to store the data and to improve the query performance. To adapt different query scenarios and handle them efficiently, index structure is one of the major challenges in stream data flow. A new index structure called CBSD (Clustering Based Stream Data) -tree is proposed. CBSD-tree is the combination of block-based and tree-based technique. It addresses three main challenges in data indexing: insert, search, and deletion. Here the incoming data are clustered based on some criteria and it is stored in a block. Each block is indexed using B+-tree. CBSD-tree index structure significantly reduces the space cost and scales well with the increasing data size.