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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：Neo4jHA中基于元启发式的BFDACO数据分配优化，可有效检索查询

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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从社交网络链接到图形NoSQL数据库的数据检索中起着关键作用，该数据库整合了大量数据。需要将这些称为大数据的海量数据分布在许多相邻计算机上并进行分片，以便可以更快地检索发布时的查询。需要在Neo4j高可用性图NoSQL数据库中建立一种用于用户灵活检索查询的有效存储机制，以减少时间开销的查询过程。本文的主要重点是如何在机器之间共享构成数据库的数据，以便所有相关数据的存储都在同一机器或相邻机器中。该图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NP-Hard。为了解决这一难题，提出并实现了将“最佳拟合递减”与基于蚁群优化的元启发式算法相结合的优化策略，并对结果进行了分析。基于仿真评估Neo4jHA的分布式主从体系结构中的数据分配，并将性能与建议的方法的查询效率与其他最佳数据分配启发式算法（如First Fit，Best Fit，First Fit Decreasing和Best Fit）进行比较到目前为止，文献中的可用量正在减少。结果表明，与没有关联和复制的其他数据分配方法相比，具有复制和关联的算法在查询执行中的性能如何。

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