Mining of High-Utility Patterns in Big IoT Databases

Jimmy Ming Tai Wu; Gautam Srivastava; Jerry Chun Wei Lin; Youcef Djenouri; Min Wei; Dawid Polap

doi:10.1007/978-3-030-87897-9_19

Mining of High-Utility Patterns in Big IoT Databases

Jimmy Ming Tai Wu
, Gautam Srivastava
, Jerry Chun Wei Lin
, Youcef Djenouri
, Min Wei
, Dawid Polap

Department of Artificial Intelligence Modelling (RMS/RMS2)

Shandong University of Science and Technology
Brandon University
Western Norway University of Applied Sciences
SINTEF

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In general data mining, HUIM also known as high-utility itemset mining is an offshoot of frequent item set mining (FIM). HUIM is known to give more emphasis to many factors which can give HUIM a distinct edge over FIM. PHIUM, or Potential high-utility item set mining has been created to give intrinsic patterns in databases that tend to be uncertain. Despite most previous methods being highly effective and powerful miners, PHUIM needs to work fast. Most current mining techniques do not handle databases with extremely large number of records when performing HUIM. In this paper, we make the assumption that the dataset is bigger than a direct load into RAM could handle. Furthermore, the dataset is not of the size where modification or duplication is possible, and as such a MapReduce framework is created that can be used to handle datasets that fall into these categories. One of the main objectives of this research is to be able to reduce the frequency of database scans while simultaneously maximizing parallel processing. Using experimental analysis, our Hadoop based algorithm performs well to mine high utility itemsets from big databases.

Original language	English
Title of host publication	Artificial Intelligence and Soft Computing - 20th International Conference, ICAISC 2021, Proceedings
Editors	Leszek Rutkowski, Rafał Scherer, Marcin Korytkowski, Witold Pedrycz, Ryszard Tadeusiewicz, Jacek M. Zurada
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	205-216
Number of pages	12
ISBN (Print)	9783030878962
DOIs	https://doi.org/10.1007/978-3-030-87897-9_19
Publication status	Published - 2021
Event	20th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2021 - Virtual, Online Duration: 21 Jun 2021 → 23 Jun 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12855 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2021
City	Virtual, Online
Period	21/06/21 → 23/06/21

Keywords

Data mining
IoT data analytics
Utility patterns

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-87897-9_19

Cite this

Wu, J. M. T., Srivastava, G., Lin, J. C. W., Djenouri, Y., Wei, M., & Polap, D. (2021). Mining of High-Utility Patterns in Big IoT Databases. In L. Rutkowski, R. Scherer, M. Korytkowski, W. Pedrycz, R. Tadeusiewicz, & J. M. Zurada (Eds.), Artificial Intelligence and Soft Computing - 20th International Conference, ICAISC 2021, Proceedings (pp. 205-216). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12855 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-87897-9_19

Wu, Jimmy Ming Tai ; Srivastava, Gautam ; Lin, Jerry Chun Wei et al. / Mining of High-Utility Patterns in Big IoT Databases. Artificial Intelligence and Soft Computing - 20th International Conference, ICAISC 2021, Proceedings. editor / Leszek Rutkowski ; Rafał Scherer ; Marcin Korytkowski ; Witold Pedrycz ; Ryszard Tadeusiewicz ; Jacek M. Zurada. Springer Science and Business Media Deutschland GmbH, 2021. pp. 205-216 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{dc63c92137ea4224ae3c67f8ffaff1cb,

title = "Mining of High-Utility Patterns in Big IoT Databases",

abstract = "In general data mining, HUIM also known as high-utility itemset mining is an offshoot of frequent item set mining (FIM). HUIM is known to give more emphasis to many factors which can give HUIM a distinct edge over FIM. PHIUM, or Potential high-utility item set mining has been created to give intrinsic patterns in databases that tend to be uncertain. Despite most previous methods being highly effective and powerful miners, PHUIM needs to work fast. Most current mining techniques do not handle databases with extremely large number of records when performing HUIM. In this paper, we make the assumption that the dataset is bigger than a direct load into RAM could handle. Furthermore, the dataset is not of the size where modification or duplication is possible, and as such a MapReduce framework is created that can be used to handle datasets that fall into these categories. One of the main objectives of this research is to be able to reduce the frequency of database scans while simultaneously maximizing parallel processing. Using experimental analysis, our Hadoop based algorithm performs well to mine high utility itemsets from big databases.",

keywords = "Data mining, IoT data analytics, Utility patterns",

author = "Wu, \{Jimmy Ming Tai\} and Gautam Srivastava and Lin, \{Jerry Chun Wei\} and Youcef Djenouri and Min Wei and Dawid Polap",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 20th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2021 ; Conference date: 21-06-2021 Through 23-06-2021",

year = "2021",

doi = "10.1007/978-3-030-87897-9\_19",

language = "English",

isbn = "9783030878962",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "205--216",

editor = "Leszek Rutkowski and Rafa{\l} Scherer and Marcin Korytkowski and Witold Pedrycz and Ryszard Tadeusiewicz and Zurada, \{Jacek M.\}",

booktitle = "Artificial Intelligence and Soft Computing - 20th International Conference, ICAISC 2021, Proceedings",

address = "Germany",

}

Wu, JMT, Srivastava, G, Lin, JCW, Djenouri, Y, Wei, M & Polap, D 2021, Mining of High-Utility Patterns in Big IoT Databases. in L Rutkowski, R Scherer, M Korytkowski, W Pedrycz, R Tadeusiewicz & JM Zurada (eds), Artificial Intelligence and Soft Computing - 20th International Conference, ICAISC 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12855 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 205-216, 20th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2021, Virtual, Online, 21/06/21. https://doi.org/10.1007/978-3-030-87897-9_19

Mining of High-Utility Patterns in Big IoT Databases. / Wu, Jimmy Ming Tai; Srivastava, Gautam; Lin, Jerry Chun Wei et al.
Artificial Intelligence and Soft Computing - 20th International Conference, ICAISC 2021, Proceedings. ed. / Leszek Rutkowski; Rafał Scherer; Marcin Korytkowski; Witold Pedrycz; Ryszard Tadeusiewicz; Jacek M. Zurada. Springer Science and Business Media Deutschland GmbH, 2021. p. 205-216 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12855 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Mining of High-Utility Patterns in Big IoT Databases

AU - Wu, Jimmy Ming Tai

AU - Srivastava, Gautam

AU - Lin, Jerry Chun Wei

AU - Djenouri, Youcef

AU - Wei, Min

AU - Polap, Dawid

PY - 2021

Y1 - 2021

N2 - In general data mining, HUIM also known as high-utility itemset mining is an offshoot of frequent item set mining (FIM). HUIM is known to give more emphasis to many factors which can give HUIM a distinct edge over FIM. PHIUM, or Potential high-utility item set mining has been created to give intrinsic patterns in databases that tend to be uncertain. Despite most previous methods being highly effective and powerful miners, PHUIM needs to work fast. Most current mining techniques do not handle databases with extremely large number of records when performing HUIM. In this paper, we make the assumption that the dataset is bigger than a direct load into RAM could handle. Furthermore, the dataset is not of the size where modification or duplication is possible, and as such a MapReduce framework is created that can be used to handle datasets that fall into these categories. One of the main objectives of this research is to be able to reduce the frequency of database scans while simultaneously maximizing parallel processing. Using experimental analysis, our Hadoop based algorithm performs well to mine high utility itemsets from big databases.

AB - In general data mining, HUIM also known as high-utility itemset mining is an offshoot of frequent item set mining (FIM). HUIM is known to give more emphasis to many factors which can give HUIM a distinct edge over FIM. PHIUM, or Potential high-utility item set mining has been created to give intrinsic patterns in databases that tend to be uncertain. Despite most previous methods being highly effective and powerful miners, PHUIM needs to work fast. Most current mining techniques do not handle databases with extremely large number of records when performing HUIM. In this paper, we make the assumption that the dataset is bigger than a direct load into RAM could handle. Furthermore, the dataset is not of the size where modification or duplication is possible, and as such a MapReduce framework is created that can be used to handle datasets that fall into these categories. One of the main objectives of this research is to be able to reduce the frequency of database scans while simultaneously maximizing parallel processing. Using experimental analysis, our Hadoop based algorithm performs well to mine high utility itemsets from big databases.

KW - Data mining

KW - IoT data analytics

KW - Utility patterns

UR - https://www.scopus.com/pages/publications/85117692152

U2 - 10.1007/978-3-030-87897-9_19

DO - 10.1007/978-3-030-87897-9_19

M3 - Conference contribution

AN - SCOPUS:85117692152

SN - 9783030878962

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 205

EP - 216

BT - Artificial Intelligence and Soft Computing - 20th International Conference, ICAISC 2021, Proceedings

A2 - Rutkowski, Leszek

A2 - Scherer, Rafał

A2 - Korytkowski, Marcin

A2 - Pedrycz, Witold

A2 - Tadeusiewicz, Ryszard

A2 - Zurada, Jacek M.

PB - Springer Science and Business Media Deutschland GmbH

T2 - 20th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2021

Y2 - 21 June 2021 through 23 June 2021

ER -

Wu JMT, Srivastava G, Lin JCW, Djenouri Y, Wei M, Polap D. Mining of High-Utility Patterns in Big IoT Databases. In Rutkowski L, Scherer R, Korytkowski M, Pedrycz W, Tadeusiewicz R, Zurada JM, editors, Artificial Intelligence and Soft Computing - 20th International Conference, ICAISC 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 205-216. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-87897-9_19

Mining of High-Utility Patterns in Big IoT Databases

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Conference

Keywords

ASJC Scopus subject areas

Access to Document

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