Query Optimization: Fund Data Generation Applying NonClustered Indexing and MapReduced Data Cube Numerosity Reduction Method

Decision-based programs include large-scale complex database queries. If the response time is short, query optimization is critical. Users usually observe data as a multi-dimensional data cube. Each data cube cell displays data as an aggregation in which the number of cells depends on the number of other cells in the cube. At any given time, a powerful query optimization method can visualize part of the cells instead of calculating results from raw data. Business systems use different approaches and positioning of data in the data cube. In the present study, the data is trained by a neural network and a genetic-firefly hybrid algorithm is proposed for finding the best position for the data in the cube.

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Closed Cube Algorithm Research for Distributed Storage

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.433-435.1724 ◽

2013 ◽

Vol 433-435 ◽

pp. 1724-1729

Author(s):

Jun Shuai Ding ◽

Jin Guo You ◽

Shui Li Zhou

Keyword(s):

Parallel Processing ◽

Distribution System ◽

Distributed Storage ◽

Data Cube ◽

Massive Data ◽

Data Generation ◽

Practical Applications ◽

Query Response Time ◽

Closed Data ◽

Important Means

Data cube is to improve the efficiency of an important means in data query, while the enclosed data cube used by eliminating non-enclosed data cube tuples to reduce the disk space and improve the query response time. Based on the MapReduce parallel processing technology research, the MapReduce-based closed data cube generation algorithm is proposed,which can effectively improve the closed data cube massive data generation rate. The simulation results show that the proposed MCDC algorithm in practical applications can offer conditional definition by users to build closed data cube that is needed. Parallel processing capability of distribution system has been fully developed.

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FQ-BF: An Efficient Forwarding and Query Optimization Framework based on Bloom Filters in NDN

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d7721.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 1001-1006

Keyword(s):

Query Optimization ◽

Search Algorithm ◽

Bloom Filters ◽

Communication Model ◽

Data Generation ◽

Average Delay ◽

Data Intensive ◽

Optimization Framework ◽

Internet Users ◽

Content Search

With the increase in internet users and data generation resources; Named Data Networking (NDN), a content-driven communication model that has come into the limelight. Most of the applications in today’s world are data-intensive, prefer a data-based network over the address-based network for communication. Continuous efforts by researchers try to optimize the NDN performance in various areas like routing, forwarding, caching, security, etc. NDN basically works on searching for the required data packet, so the content search is one of the promising areas in NDN that still needs improvement. This paper proposes a novel content search algorithm based on Bloom Filters (BF) named FQ-BF; which focuses on efficient and smart forwarding of I_pkt and reduces the number of queries to search a packet in the network. The important metrics where the proposed approach has shown significant improvement as compared to standard NDN approach; include the average delay of packets, improving the packet serving capacity of the network.

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