Main Reference PaperFastRAQ: A Fast Approach to Range-Aggregate Queries in Big Data Environments, IEEE Transactions on Cloud Computing, April-June 2015.
  • Range-aggregate queries execute the aggregate function on number of columns with simultaneously in a given query ranges. The processing of range-aggregate queries on large amount of data takes the long time to provide the accurate result. Therefore the efficiently handle the query on big data, this work proposes a range-aggregate queries called FastRAQ. The proposed FastRAQ algorithm first divides the bigdata into independent partitions by using balanced partitioning algorithm. These partitions hold data for increasing processing speed. According to large data record field, the bigdata is partitioned and then generate the local estimation sketch for each partition. When a user enters range-aggregate query according to requirements, the proposed FastRAQ algorithm is quickly fetch the respective data from respective partitions.

+ Description
  • Range-aggregate queries execute the aggregate function on number of columns with simultaneously in a given query ranges. The processing of range-aggregate queries on large amount of data takes the long time to provide the accurate result. Therefore the efficiently handle the query on big data, this work proposes a range-aggregate queries called FastRAQ. The proposed FastRAQ algorithm first divides the bigdata into independent partitions by using balanced partitioning algorithm. These partitions hold data for increasing processing speed. According to large data record field, the bigdata is partitioned and then generate the local estimation sketch for each partition. When a user enters range-aggregate query according to requirements, the proposed FastRAQ algorithm is quickly fetch the respective data from respective partitions.

  • To increase the processing speed of range-aggregate query.

  • To achieve scalability.

  • The main aim of this project is handling data efficiently for the aggregate functions which are fired on one or more column on the big data.

+ Aim & Objectives
  • To increase the processing speed of range-aggregate query.

  • To achieve scalability.

  • The main aim of this project is handling data efficiently for the aggregate functions which are fired on one or more column on the big data.

  • An effective matching algorithm is contributed for searching and matching the user query with other data (partition data) that is present at the hadoop distributed file system.

+ Contribution
  • An effective matching algorithm is contributed for searching and matching the user query with other data (partition data) that is present at the hadoop distributed file system.

  • Java JDK 1.8, MySQL 5.5.40, Hadoop 1.2.1, Hive 0.14.0.

  • Netbeans 8.0.1, J2EE, Hadoop, Hive.

+ Software Tools & Technologies
  • Java JDK 1.8, MySQL 5.5.40, Hadoop 1.2.1, Hive 0.14.0.

  • Netbeans 8.0.1, J2EE, Hadoop, Hive.

  • B.E / B.Tech / M.E / M.Tech

+ Project Recommended For
  • B.E / B.Tech / M.E / M.Tech

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