FastRAQ A Fast Approach to Range-Aggregate Queries in Big Data Environments
Rs3,500.00
10000 in stock
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Range-aggregate queries are popular in many applications in data warehouse environments with large business relational databases. To evaluate these efficiently, several studies on data cubes have been carried out. In the well-known aggregate cube tree, each entry in every node stores the aggregate values of its corresponding subtree. Therefore, range-aggregate queries can be processed without visiting the child subtree whose nodes are all fully included in the query range. However, the aggregate cube tree does not consider range queries using partial dimensions and range queries without aggregation operations. Manipulation of Queries on a large amount of data is done with various technologies. Query handling of big data is a challenging task because of unstructured, real-time data. Efficient Query execution technique usage partition algorithm & pattern match algorithm for handling queries of the user. These techniques provide faster execution of such queries on the big data in an efficient manner. Various authors propose query execution using DAG, ranges, unification & other methods. Range-aggregate queries are giving better performance with balance partition algorithm and map-reduce technique for handling large amount of queries with pattern matching technique. FastRAQ—a new approximate answering approach proposed in this project. It acquires accurate estimations quickly for range-aggregate queries in big data environments. FastRAQ obtains the result directly by summarizing local estimates from all partitions.it first divides the big data in to independent partitions with a balanced partition algorithm and then generates a local estimation sketch for each partition.
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