Efficient Algorithms for Range Mode Queries in the Big Data Era

被引：3
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.
引用
收藏
页数：37
相关论文
共 50 条
  • [31] Insurance in Big Data Era
    Xie Dongzhou
    Lin Sha
    PROCEEDINGS OF THE 2015 CHINA INTERNATIONAL CONFERENCE ON INSURANCE AND RISK MANAGEMENT, 2015, : 90 - 103
  • [32] Policing in the Era of Big Data
    Ridgeway, Greg
    ANNUAL REVIEW OF CRIMINOLOGY, VOL 1, 2018, 1 : 401 - 419
  • [33] A survey on parallel clustering algorithms for Big Data
    Dafir, Zineb
    Lamari, Yasmine
    Slaoui, Said Chah
    ARTIFICIAL INTELLIGENCE REVIEW, 2021, 54 (04) : 2411 - 2443
  • [34] GIS in the Era of Big Data
    Goodchild, Michael F.
    CYBERGEO-EUROPEAN JOURNAL OF GEOGRAPHY, 2016,
  • [35] Towards Energy-Efficient Wireless Networking in the Big Data Era: A Survey
    Cao, Xianghui
    Liu, Lu
    Cheng, Yu
    Shen, Xuemin
    IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2018, 20 (01): : 303 - 332
  • [36] AUC Maximization in the Era of Big Data and AI: A Survey
    Yang, Tianbao
    Ying, Yiming
    ACM COMPUTING SURVEYS, 2023, 55 (08)
  • [37] TAX AUDIT IN THE ERA OF BIG DATA: THE CASE OF INDONESIA
    Darono, Agung
    Pratama, Aldi
    JOURNAL OF TAX ADMINISTRATION, 2022, 7 (02): : 27 - 50
  • [38] Big Data and Clustering Algorithms
    Ajin, V. W.
    Kumar, Lekshmy D.
    2016 INTERNATIONAL CONFERENCE ON RESEARCH ADVANCES IN INTEGRATED NAVIGATION SYSTEMS (RAINS), 2016,
  • [39] Efficient Sensory Data Transformation: A Big Data Approach
    Pasha, Akram
    Latha, P. H.
    INTELLIGENT SYSTEMS APPLICATIONS IN SOFTWARE ENGINEERING, VOL 1, 2019, 1046 : 69 - 82
  • [40] Comparative Analysis of Energy-Efficient Scheduling Algorithms for Big Data Applications
    Li, Hongjian
    Wang, Huochen
    Xiong, Anping
    Lai, Jun
    Tian, Wenhong
    IEEE ACCESS, 2018, 6 : 40073 - 40084
12345