High-Speed Retrieval Method for Unstructured Big Data Platform Based on K-Ary Search Tree Algorithm

Zhang Helin, Jiang Meiling, Wang Yiting, Zhang Hang, Liao Huadong, C. Anqing
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Abstract

With the popularity of the Internet, the amount of data created by people every day increases exponentially, and most of these data are unstructured data. How to search for useful information from a large amount of data is the problem to be solved in this paper. In this regard, this paper builds an unstructured big data platform for data retrieval, and conducts two test experiments. One is to test the retrieval efficiency of the platform in stand-alone mode and distributed mode. The retrieval efficiency is better; the second is to test the impact of the k-ary search tree algorithm on the retrieval computing efficiency of the platform. It is found that when the amount of data to be retrieved exceeds 400M, the platform can effectively improve the computing speed by using this algorithm.
基于K-Ary搜索树算法的非结构化大数据平台高速检索方法
随着互联网的普及,人们每天产生的数据量呈指数级增长,其中大部分是非结构化数据。如何从海量的数据中搜索出有用的信息是本文要解决的问题。为此,本文搭建了一个非结构化大数据平台进行数据检索,并进行了两次测试实验。一是测试平台在单机模式和分布式模式下的检索效率。检索效率较好;二是测试k-ary搜索树算法对平台检索计算效率的影响。研究发现,当需要检索的数据量超过400M时,使用该算法平台可以有效提高计算速度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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