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首页> 外文期刊>Arabian Journal for Science and Engineering >Metaheuristic-Based BFDACO Data Allocation Optimization in Neo4jHA for Efficient Query Retrieval
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Metaheuristic-Based BFDACO Data Allocation Optimization in Neo4jHA for Efficient Query Retrieval

机译：基于Metaheuristic的BFDACO数据分配优化Neo4JHA以高效查询检索

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摘要

Philosophical methods of query process play a pivotal role in data retrieval from social networks linked to a graph NoSQL database that consolidate massive types of data. This massive data called as big data need to be distributed and sharded across many adjacent machines so that the queries when posted can be retrieved faster. An efficient storage mechanism for flexible retrieval of a query by the user needs to be established in Neo4j High Availability graph NoSQL database for less time overhead query process. The main focus of this paper is how to share the database constituting data across machines such that the storage of all related data comes in same or adjacent machines. This graph NoSQL database allocation problem referred as Neo4j High Availability big data allocation has been proved to be NP-Hard in this paper. To solve this hard problem, an optimization strategy by integrating Best Fit Decreasing with Ant Colony Optimization-based metaheuristic algorithm is suggested and implemented and results are analyzed. This data allocation in a distributed master-slave architecture of Neo4jHA is evaluated based on simulation, and performance is compared on the query efficiency of the proposed method to other best data allocation heuristic algorithms like First Fit, Best Fit, First Fit Decreasing and Best Fit Decreasing available in literature so far. The results exhibit how the proposed algorithm with replication and relation outperforms in query execution compared to other data allocation methods without relation and replication.
机译：查询过程的哲学方法在与图表NoSQL数据库链接的社交网络中的数据检索中发挥了关键作用，该数据网络整合了大规模类型的数据。这种大规模数据称为大数据需要在许多相邻机器上分发和分配，以便可以更快地检索发布时查询。需要在Neo4J高可用性图NOSQL数据库中建立一个有效的存储机制，用于灵活地通过用户在Neo4J高可用性图NOSQL数据库中建立越来越少的架空查询进程。本文的主要焦点是如何共享构成计算机数据的数据库，使得所有相关数据的存储都有相同或相邻的机器。此图NoSQL数据库分配问题称为Neo4J高可用性大数据分配已被证明是本文的NP - 硬。为了解决这个难题，建议并实现了通过集成基于蚁群优化的成分识别算法的最佳拟合减少的优化策略，并分析结果。基于模拟评估了Neo4JHA的分布式主从体系结构中的数据分配，并将性能与其他最佳数据分配启发式算法的查询效率进行了比较，如第一次适合，最适合，首先拟合减少和最合适的算法到目前为止，文学中可用减少。结果表现出所提出的算法如何在没有关系和复制的其他数据分配方法中与查询执行中的复制和关系的概率。

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