Efficient Algorithms for Range Mode Queries in the Big Data Era

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 a...

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Published inInformation (Basel) Vol. 15; no. 8; p. 450
Main Authors Karras, Christos, Theodorakopoulos, Leonidas, Karras, Aristeidis, Krimpas, George A.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2024
Subjects
Algorithms
Big Data
Boolean
Compliance
Data analysis
Data retrieval
Data structures
Datasets
Effectiveness
Electronic commerce
Information management
internal audit
Number theory
Queries
Query processing
RAM
range mode queries
Social networks
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Abstract 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.
AbstractList 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.
Audience Academic
Author Krimpas, George A
Theodorakopoulos, Leonidas
Karras, Christos
Karras, Aristeidis
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Cites_doi 10.1016/j.jmsy.2021.03.005
10.1016/j.eswa.2022.116821
10.3390/info14040223
10.1109/TIP.2022.3192706
10.1109/ICBATS57792.2023.10111374
10.1155/2022/4892046
10.1109/TDSC.2021.3061611
10.3390/eng5030068
10.3390/s21072376
10.1016/j.jsis.2017.07.003
10.1016/j.ins.2019.09.055
10.1016/j.iheduc.2022.100881
10.1016/j.ipl.2008.10.007
10.1109/AICT50176.2020.9368781
10.1108/MEDAR-03-2020-0838
10.1145/3575879.3575977
10.1016/j.isprsjprs.2015.11.006
10.1007/978-3-030-00479-8_17
10.1145/3397271.3401327
10.1109/TR.2020.2973403
10.1063/1674-0068/cjcp2005077
10.1080/14697688.2017.1402125
10.2139/ssrn.3637784
10.3389/fpsyg.2023.1140972
10.1016/j.intaccaudtax.2020.100357
10.1007/s10115-018-1183-0
10.2307/j.ctv14jx6sm
10.3390/sym12081288
10.1007/978-3-031-33437-5_2
10.26599/BDMA.2019.9020015
10.1016/j.knosys.2020.106462
10.1108/MAJ-08-2018-1980
10.1016/j.pce.2023.103421
10.1145/1290672.1290680
10.1109/JIOT.2018.2823498
10.1007/s00224-013-9455-2
10.1186/s41044-016-0014-0
10.1007/s11042-022-13954-1
10.21511/bbs.17(3).2022.05
10.1016/j.ssci.2019.09.015
10.1016/j.dib.2021.107061
10.14778/3436905.3436910
10.1007/978-3-030-39105-8
10.1007/978-3-031-14054-9_20
10.1109/ICCAKM54721.2022.9990045
10.1080/0144929X.2017.1355014
10.1080/23322039.2022.2148363
10.1016/B978-0-12-809474-7.00019-7
10.1108/JFC-11-2019-0147
10.1002/nme.4857
10.1007/978-3-031-21047-1_33
10.1587/transinf.E92.D.2025
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References Petersen (ref_5) 2009; 109
Liu (ref_22) 2020; 70
Raman (ref_64) 2007; 3
Hou (ref_25) 2020; 208
Tang (ref_9) 2017; 36
ref_14
ref_13
Akingboye (ref_42) 2023; 131
ref_53
ref_52
Liu (ref_40) 2016; 115
ref_51
ref_19
Alazzabi (ref_24) 2023; 30
ref_16
ref_15
ref_59
Alrashidi (ref_12) 2022; 9
Bu (ref_18) 2020; 512
Ma (ref_58) 2020; 33
Mehrizi (ref_61) 2017; 26
Zheng (ref_27) 2021; 19
Rakipi (ref_7) 2021; 42
Ahmed (ref_50) 2019; 58
Hatala (ref_45) 2023; 56
Rahman (ref_37) 2019; 17
Si (ref_17) 2022; 2022
ref_66
McWalter (ref_47) 2018; 18
ref_63
ref_62
Zhao (ref_56) 2018; 5
Wang (ref_54) 2015; 102
(ref_11) 2021; 29
Theodorakopoulos (ref_34) 2024; 5
Putra (ref_21) 2022; 10
ref_36
ref_35
Mahmud (ref_29) 2020; 3
ref_33
Pena (ref_43) 2022; 198
ref_32
ref_31
ref_30
Fourny (ref_28) 2020; 14
ref_38
Krizanc (ref_1) 2005; 12
Na (ref_65) 2009; 92
Raja (ref_39) 2023; 82
Oussous (ref_55) 2018; 30
Luengo (ref_44) 2016; 1
ref_46
Wang (ref_57) 2022; 31
Theodorakopoulos (ref_6) 2022; 17
Nietert (ref_60) 2022; 35
Shabani (ref_10) 2022; Volume 2
ref_41
Wang (ref_26) 2022; 62
ref_3
Turetken (ref_23) 2020; 35
Baatwah (ref_67) 2021; 36
Chan (ref_2) 2014; 55
ref_49
ref_48
ref_8
Hegde (ref_20) 2020; 122
ref_4
References_xml – volume: 62
  start-page: 738
  year: 2022
  ident: ref_26
  article-title: Big data analytics for intelligent manufacturing systems: A review
  publication-title: J. Manuf. Syst.
  doi: 10.1016/j.jmsy.2021.03.005
  contributor:
    fullname: Wang
– volume: 198
  start-page: 116821
  year: 2022
  ident: ref_43
  article-title: Normalization method for quantitative and qualitative attributes in multiple attribute decision-making problems
  publication-title: Expert Syst. Appl.
  doi: 10.1016/j.eswa.2022.116821
  contributor:
    fullname: Pena
– ident: ref_49
– ident: ref_33
  doi: 10.3390/info14040223
– volume: 31
  start-page: 5257
  year: 2022
  ident: ref_57
  article-title: Extendable multiple nodes recurrent tracking framework with RTU++
  publication-title: IEEE Trans. Image Process.
  doi: 10.1109/TIP.2022.3192706
  contributor:
    fullname: Wang
– ident: ref_13
  doi: 10.1109/ICBATS57792.2023.10111374
– volume: 17
  start-page: 44
  year: 2019
  ident: ref_37
  article-title: Statistics-based data preprocessing methods and machine learning algorithms for big data analysis
  publication-title: Int. J. Artif. Intell.
  contributor:
    fullname: Rahman
– volume: 2022
  start-page: 4892046
  year: 2022
  ident: ref_17
  article-title: Construction and application of enterprise internal audit data analysis model based on decision tree algorithm
  publication-title: Discret. Dyn. Nat. Soc.
  doi: 10.1155/2022/4892046
  contributor:
    fullname: Si
– volume: 19
  start-page: 2501
  year: 2021
  ident: ref_27
  article-title: Efficient and privacy-preserving similarity range query over encrypted time series data
  publication-title: IEEE Trans. Dependable Secur. Comput.
  doi: 10.1109/TDSC.2021.3061611
  contributor:
    fullname: Zheng
– volume: 5
  start-page: 1266
  year: 2024
  ident: ref_34
  article-title: A State-of-the-Art Review in Big Data Management Engineering: Real-Life Case Studies, Challenges, and Future Research Directions
  publication-title: Eng
  doi: 10.3390/eng5030068
  contributor:
    fullname: Theodorakopoulos
– ident: ref_15
  doi: 10.3390/s21072376
– volume: 26
  start-page: 191
  year: 2017
  ident: ref_61
  article-title: Debating big data: A literature review on realizing value from big data
  publication-title: J. Strateg. Inf. Syst.
  doi: 10.1016/j.jsis.2017.07.003
  contributor:
    fullname: Mehrizi
– volume: 512
  start-page: 123
  year: 2020
  ident: ref_18
  article-title: A convolutional neural-based learning classifier system for detecting database intrusion via insider attack
  publication-title: Inf. Sci.
  doi: 10.1016/j.ins.2019.09.055
  contributor:
    fullname: Bu
– volume: 56
  start-page: 100881
  year: 2023
  ident: ref_45
  article-title: Progression of students’ SRL processes in subsequent programming problem-solving tasks and its association with tasks outcomes
  publication-title: Internet High. Educ.
  doi: 10.1016/j.iheduc.2022.100881
  contributor:
    fullname: Hatala
– volume: 109
  start-page: 225
  year: 2009
  ident: ref_5
  article-title: Range mode and range median queries in constant time and sub-quadratic space
  publication-title: Inf. Process. Lett.
  doi: 10.1016/j.ipl.2008.10.007
  contributor:
    fullname: Petersen
– ident: ref_19
  doi: 10.1109/AICT50176.2020.9368781
– volume: 29
  start-page: 1088
  year: 2021
  ident: ref_11
  article-title: Big data and data analytics in auditing: In search of legitimacy
  publication-title: Meditari Account. Res.
  doi: 10.1108/MEDAR-03-2020-0838
– ident: ref_35
  doi: 10.1145/3575879.3575977
– volume: 115
  start-page: 134
  year: 2016
  ident: ref_40
  article-title: Rethinking big data: A review on the data quality and usage issues
  publication-title: ISPRS J. Photogramm. Remote Sens.
  doi: 10.1016/j.isprsjprs.2015.11.006
  contributor:
    fullname: Liu
– ident: ref_53
  doi: 10.1007/978-3-030-00479-8_17
– ident: ref_51
  doi: 10.1145/3397271.3401327
– volume: 70
  start-page: 163
  year: 2020
  ident: ref_22
  article-title: A new integrated approach for risk evaluation and classification with dynamic expert weights
  publication-title: IEEE Trans. Reliab.
  doi: 10.1109/TR.2020.2973403
  contributor:
    fullname: Liu
– volume: 33
  start-page: 727
  year: 2020
  ident: ref_58
  article-title: NeuDATool: An open source neutron data analysis tools, supporting GPU hardware acceleration, and across-computer cluster nodes parallel
  publication-title: Chin. J. Chem. Phys.
  doi: 10.1063/1674-0068/cjcp2005077
  contributor:
    fullname: Ma
– volume: 18
  start-page: 693
  year: 2018
  ident: ref_47
  article-title: Recursive marginal quantization of higher-order schemes
  publication-title: Quant. Financ.
  doi: 10.1080/14697688.2017.1402125
  contributor:
    fullname: McWalter
– ident: ref_48
  doi: 10.2139/ssrn.3637784
– volume: 9
  start-page: 93
  year: 2022
  ident: ref_12
  article-title: The impact of big data analytics on audit procedures: Evidence from the Middle East
  publication-title: J. Asian Financ. Econ. Bus.
  contributor:
    fullname: Alrashidi
– ident: ref_4
– ident: ref_8
  doi: 10.3389/fpsyg.2023.1140972
– volume: 35
  start-page: 28179
  year: 2022
  ident: ref_60
  article-title: Statistical, robustness, and computational guarantees for sliced wasserstein distances
  publication-title: Adv. Neural Inf. Process. Syst.
  contributor:
    fullname: Nietert
– ident: ref_41
– volume: 42
  start-page: 100357
  year: 2021
  ident: ref_7
  article-title: Correlates of the internal audit function’s use of data analytics in the big data era: Global evidence
  publication-title: J. Int. Account. Audit. Tax.
  doi: 10.1016/j.intaccaudtax.2020.100357
  contributor:
    fullname: Rakipi
– volume: 58
  start-page: 249
  year: 2019
  ident: ref_50
  article-title: Data summarization: A survey
  publication-title: Knowl. Inf. Syst.
  doi: 10.1007/s10115-018-1183-0
  contributor:
    fullname: Ahmed
– ident: ref_66
– ident: ref_62
– ident: ref_38
– ident: ref_46
  doi: 10.2307/j.ctv14jx6sm
– volume: Volume 2
  start-page: 362
  year: 2022
  ident: ref_10
  article-title: A Study of Big Data Analytics in Internal Auditing
  publication-title: Intelligent Systems and Applications, Proceedings of the 2021 Intelligent Systems Conference (IntelliSys), Virtual, 2–3 September 2021
  contributor:
    fullname: Shabani
– volume: 12
  start-page: 1
  year: 2005
  ident: ref_1
  article-title: Range mode and range median queries on lists and trees
  publication-title: Nord. J. Comput.
  contributor:
    fullname: Krizanc
– ident: ref_59
  doi: 10.3390/sym12081288
– ident: ref_31
  doi: 10.1007/978-3-031-33437-5_2
– volume: 3
  start-page: 85
  year: 2020
  ident: ref_29
  article-title: A survey of data partitioning and sampling methods to support big data analysis
  publication-title: Big Data Min. Anal.
  doi: 10.26599/BDMA.2019.9020015
  contributor:
    fullname: Mahmud
– volume: 208
  start-page: 106462
  year: 2020
  ident: ref_25
  article-title: A novel dynamic ensemble selection classifier for an imbalanced data set: An application for credit risk assessment
  publication-title: Knowl.-Based Syst.
  doi: 10.1016/j.knosys.2020.106462
  contributor:
    fullname: Hou
– ident: ref_3
– volume: 35
  start-page: 238
  year: 2020
  ident: ref_23
  article-title: Internal audit effectiveness: Operationalization and influencing factors
  publication-title: Manag. Audit. J.
  doi: 10.1108/MAJ-08-2018-1980
  contributor:
    fullname: Turetken
– volume: 131
  start-page: 103421
  year: 2023
  ident: ref_42
  article-title: RQD modeling using statistical-assisted SRT with compensated ERT methods: Correlations between borehole-based and SRT-based RMQ models
  publication-title: Phys. Chem. Earth Parts A/B/C
  doi: 10.1016/j.pce.2023.103421
  contributor:
    fullname: Akingboye
– volume: 3
  start-page: 43-es
  year: 2007
  ident: ref_64
  article-title: Succinct indexable dictionaries with applications to encoding k-ary trees, prefix sums and multisets
  publication-title: ACM Trans. Algorithms (TALG)
  doi: 10.1145/1290672.1290680
  contributor:
    fullname: Raman
– volume: 5
  start-page: 3606
  year: 2018
  ident: ref_56
  article-title: Deploying edge computing nodes for large-scale IoT: A diversity aware approach
  publication-title: IEEE Internet Things J.
  doi: 10.1109/JIOT.2018.2823498
  contributor:
    fullname: Zhao
– volume: 55
  start-page: 719
  year: 2014
  ident: ref_2
  article-title: Linear-space data structures for range mode query in arrays
  publication-title: Theory Comput. Syst.
  doi: 10.1007/s00224-013-9455-2
  contributor:
    fullname: Chan
– volume: 1
  start-page: 9
  year: 2016
  ident: ref_44
  article-title: Big data preprocessing: Methods and prospects
  publication-title: Big Data Anal.
  doi: 10.1186/s41044-016-0014-0
  contributor:
    fullname: Luengo
– ident: ref_14
– ident: ref_63
– volume: 82
  start-page: 12635
  year: 2023
  ident: ref_39
  article-title: Analysis of anomaly detection in surveillance video: Recent trends and future vision
  publication-title: Multimed. Tools Appl.
  doi: 10.1007/s11042-022-13954-1
  contributor:
    fullname: Raja
– volume: 17
  start-page: 49
  year: 2022
  ident: ref_6
  article-title: The drivers of volume volatility: A big data analysis based on economic uncertainty measures for the Greek banking system
  publication-title: Banks Bank Syst.
  doi: 10.21511/bbs.17(3).2022.05
  contributor:
    fullname: Theodorakopoulos
– volume: 122
  start-page: 104492
  year: 2020
  ident: ref_20
  article-title: Applications of machine learning methods for engineering risk assessment—A review
  publication-title: Saf. Sci.
  doi: 10.1016/j.ssci.2019.09.015
  contributor:
    fullname: Hegde
– volume: 36
  start-page: 107061
  year: 2021
  ident: ref_67
  article-title: Dataset for audit dimensions in an emerging market: Developing a panel database of audit effectiveness and efficiency
  publication-title: Data Brief
  doi: 10.1016/j.dib.2021.107061
  contributor:
    fullname: Baatwah
– volume: 14
  start-page: 498
  year: 2020
  ident: ref_28
  article-title: Rumble: Data independence for large messy data sets
  publication-title: Proc. VLDB Endow.
  doi: 10.14778/3436905.3436910
  contributor:
    fullname: Fourny
– ident: ref_36
  doi: 10.1007/978-3-030-39105-8
– ident: ref_32
  doi: 10.1007/978-3-031-14054-9_20
– ident: ref_16
  doi: 10.1109/ICCAKM54721.2022.9990045
– volume: 36
  start-page: 1125
  year: 2017
  ident: ref_9
  article-title: Exploring perceptions of data analytics in the internal audit function
  publication-title: Behav. Inf. Technol.
  doi: 10.1080/0144929X.2017.1355014
  contributor:
    fullname: Tang
– volume: 10
  start-page: 2148363
  year: 2022
  ident: ref_21
  article-title: The influence of internal audit, risk management, whistleblowing system and big data analytics on the financial crime behavior prevention
  publication-title: Cogent Econ. Financ.
  doi: 10.1080/23322039.2022.2148363
  contributor:
    fullname: Putra
– ident: ref_52
  doi: 10.1016/B978-0-12-809474-7.00019-7
– volume: 30
  start-page: 569
  year: 2023
  ident: ref_24
  article-title: Risk management, top management support, internal audit activities and fraud mitigation
  publication-title: J. Financ. Crime
  doi: 10.1108/JFC-11-2019-0147
  contributor:
    fullname: Alazzabi
– volume: 102
  start-page: 1638
  year: 2015
  ident: ref_54
  article-title: Numerical techniques on improving computational efficiency of spectral boundary integral method
  publication-title: Int. J. Numer. Methods Eng.
  doi: 10.1002/nme.4857
  contributor:
    fullname: Wang
– ident: ref_30
  doi: 10.1007/978-3-031-21047-1_33
– volume: 92
  start-page: 2025
  year: 2009
  ident: ref_65
  article-title: Fast computation of rank and select functions for succinct representation
  publication-title: IEICE Trans. Inf. Syst.
  doi: 10.1587/transinf.E92.D.2025
  contributor:
    fullname: Na
– volume: 30
  start-page: 431
  year: 2018
  ident: ref_55
  article-title: Big Data technologies: A survey
  publication-title: J. King Saud Univ.-Comput. Inf. Sci.
  contributor:
    fullname: Oussous
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Snippet The mode is a fundamental descriptive statistic in data analysis, signifying the most frequent element within a dataset. The range mode query (RMQ) problem...
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StartPage 450
SubjectTerms Algorithms
Big Data
Boolean
Compliance
Data analysis
Data retrieval
Data structures
Datasets
Effectiveness
Electronic commerce
Information management
internal audit
Number theory
Queries
Query processing
RAM
range mode queries
Social networks
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Title Efficient Algorithms for Range Mode Queries in the Big Data Era
URI https://www.proquest.com/docview/3097921862
https://doaj.org/article/e764481c857d49c79e1d153bb027cab4
Volume 15
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