Efficient Algorithms for Range Mode Queries in the Big Data Era

被引：0

作者：

Karras, Christos ^{[1
]}

Theodorakopoulos, Leonidas ^{[2
]}

Karras, Aristeidis ^{[1
]}

Krimpas, George A. ^{[1
]}

机构：

[1] Univ Patras, Comp Engn & Informat Dept, Rion 26504, Greece

[2] Univ Patras, Dept Management Sci & Technol, Patras 26334, Greece

来源：

INFORMATION | 2024年 / 15卷 / 08期

关键词：

data structures; algorithms; RAM; range mode queries; big data; internal audit; DATA ANALYTICS;

D O I：

10.3390/info15080450

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The mode is a fundamental descriptive statistic in data analysis, signifying the most frequent element within a dataset. The range mode query (RMQ) problem expands upon this concept by preprocessing an array A containing n natural numbers. This allows for the swift determination of the mode within any subarray A[a..b], thus optimizing the computation of the mode for a multitude of range queries. The efficacy of this process bears considerable importance in data analytics and retrieval across diverse platforms, including but not limited to online shopping experiences and financial auditing systems. This study is dedicated to exploring and benchmarking different algorithms and data structures designed to tackle the RMQ problem. The goal is to not only address the theoretical aspects of RMQ but also to provide practical solutions that can be applied in real-world scenarios, such as the optimization of an online shopping platform's understanding of customer preferences, enhancing the efficiency and effectiveness of data retrieval in large datasets.

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页数：37

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