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Multi-Objective Big Data View Materialization Using NSGA-Ⅱ

机译：使用NSGA-Ⅱ的多目标大数据视图实现

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摘要

Big data views, in the context of distributed file system (DFS), are defined over structured, semi-structured and unstructured data that are voluminous in nature with the purpose to reduce the response time of queries over Big data. As the size of semi-structured and unstructured data in Big data is very large compared to structured data, a framework based on query attributes on Big data can be used to identify Big data views. Materializing Big data views can enhance the query response time and facilitate efficient distribution of data over the DFS based application. Given all the Big data views cannot be materialized, therefore, a subset of Big data views should be selected for materialization. The purpose of view selection for materialization is to improve query response time subject to resource constraints. The Big data view materialization problem was defined as a bi-objective problem with the two objectives- minimization of query evaluation cost and minimization of the update processing cost, with a constraint on the total size of the materialized views. This problem is addressed in this paper using multi-objective genetic algorithm NSGA-II. The experimental results show that proposed NSGA-II based Big data view selection algorithm is able to select reasonably good quality views for materialization.
机译：在分布式文件系统（DFS）的上下文中的大数据视图是通过结构化的，半结构化和非结构化数据定义的，其自然界是大量的，目的是减少对大数据的查询的响应时间。随着与结构数据相比，大数据中半结构化和非结构化数据的大小非常大，基于大数据上的查询属性的框架可用于识别大数据视图。实质化的大数据视图可以增强查询响应时间，并促进基于DFS的应用程序的数据分发。鉴于所有大数据视图不能实现，因此应选择大数据视图的子集以进行实现。查看实现的选择的目的是改善对资源约束的查询响应时间。大数据查看物化问题被定义为双目标问题，其中两个目标 - 最小化查询评估成本和最小化更新处理成本的最小化，其限制了整理视图的总大小。本文使用多目标遗传算法NSGA-II在本文中解决了该问题。实验结果表明，所提出的基于NSGA-II的大数据观测选择算法能够为实现的合理质量看。

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