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FastRAQ: A Fast Approach to Range-Aggregate Queries in Big Data Environments

FastRAQ: A Fast Approach to Range-Aggregate Queries in Big Data Environments

Starting at: Rs.4,500.00

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FastRAQ: A Fast Approach to Range-Aggregate Queries in Big Data Environments

Range-aggregate queries are to apply a certain aggregate function on all tuples within given query ranges. Existing approaches to range-aggregate queries are insufficient to quickly provide accurate results in big data environments. In this paper, we propose FastRAQa fast approach to range-aggregate queries in big data environments. FastRAQ first divides big data into Independent partitions with a balanced partitioning algorithm, and then generates a local estimation sketch for each partition. When a range-aggregate query request arrives, FastRAQ obtains the result directly by summarizing local estimates from all partitions. FastRAQ has O1 time complexity for data updates and O N P_B time complexity for range-aggregate queries, where N is the number of distinct tuples for all dimensions, P is the partition number, and B is the bucket number in the histogram. We implement the FastRAQ approach on the Linux platform, and evaluate its performance with about 10 billion data records.



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  • Model: PROJ5080
  • 999 Units in Stock
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This product was added to our catalog on Thursday 08 September, 2016.

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