期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2018
卷号：6
期号：6
页码：6278-6284
DOI：10.15680/IJIRCCE.2018.0606016
出版社：S&S Publications
摘要：Big data is a broad term for huge data sets that traditional data processing applications are inadequate.
Big data analysis can discover trends of various social aspects and their preferences of individual everyday behaviors
.The main challenging factor is processing large amount of data within a time period .The query processing time is
increased then the network communication cost and local files scanning cost can be increased simultaneously. In our
existing system they use hive in range aggregate query but this could provide inaccurate results in big data
environments .To overcome this limitations we propose a new technique called FASTRAQ- Range Aggregate Queries.
FastRAQ first divides big data into different independent partitions with a balanced partitioning algorithm, and then
generates a local estimation for each partition. If a range-aggregate query request arrives, FastRAQ obtains the result
directly by summarizing local estimates from all partitions. Fast Range Aggregate Queries has time complexity of 0(1)
for data updates. FastRAQ provides range-aggregate query results within a time period that are lower than that of Hive,
while relative error is less than 3 percent within the given confidence time interval.
