Efficient data retrieval using adaptive clustered indexing for continuous queries over streaming data

Abstract

The Modern era has highly dynamic, heterogeneous and massive data volumes, generated from sensor networks, social media and telecommunications, stock market analyses and the Internet, etc. makes constant query processing quite challenging in processing real-time data, which exist as streams and undergo dynamic changes. Large volumes of data can be efficiently handled by partitioning them into clusters followed by Indexing. An efficient clustering and indexing method is required to process continuous queries for retrieving data streams. A new index structure called adaptive clustering and block-based indexing (ACBBI) is proposed, which is a fusion of cluster-based and block-based techniques to process continuous queries. The incoming data are clustered and stored as blocks using the adaptive clustering method and further indexed by the adaptive indexing approach. Livestock market values that are time variant are used for experimentation. The experimental analysis demonstrates that the ACBBI tree structure significantly decreases half of the space cost, scales better with increasing data size and improves the retrieval rate 30% more than an existing CKDB approach.