A Survey on Techniques for Indexing and Hashing in Big Data

Mitali Desai, R. Mehta, Dipti P Rana
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引用次数: 4

Abstract

Conventional indexing and hashing solutions become ineffective to provide efficient storage and fast retrieval for Big Data due to challenging characteristics of Big Data. Thus, an effective method for Big Data indexing and hashing is required with optimized construction time, storage space, training time, query response time and query result accuracy. Existing Big Data indexing techniques are categorized as Non-Artificial Intelligent, Artificial Intelligent and Collaborative Artificial Intelligent indexing whereas Big Data hashing techniques are categorized as non-deterministic and deterministic hashing. This paper provides an extensive survey on widely used Big Data indexing and hashing techniques along with their parametric comparisons based upon various identified parameters
大数据中索引与哈希技术综述
由于大数据具有挑战性的特点,传统的索引和哈希解决方案无法为大数据提供高效的存储和快速的检索。因此,需要一种有效的大数据索引和哈希方法,优化构建时间、存储空间、训练时间、查询响应时间和查询结果准确性。现有的大数据索引技术分为非人工智能、人工智能和协作人工智能索引,而大数据哈希技术分为非确定性和确定性哈希。本文对广泛使用的大数据索引和散列技术进行了广泛的调查,并基于各种已确定的参数对其进行了参数比较
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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