An Efficient Parallel Mining Algorithm Representative Pattern Set of Large-Scale Itemsets in IoT

Zhang, Tianrui; Wei, Mingqi; Liu, Bin

doi:10.1109/access.2018.2884888

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…Tianrui et al 21 presented an online representative pattern‐set parallel‐mining algorithm by using parallel MapReduce. An online demonstrative pattern mining algorithm was used to process the database by horizontal segmentation.…”

Section: Organization Of Data Mining Algorithms In Fog Computingmentioning

confidence: 99%

“…IoT, with millions of connected devices, has recently become one of the most significant building blocks of fog computing for storing and exchanging data. Nowadays, with the advances in portable devices development, IoT can make a large universe of diverse and valuable data which can be collected by users from a wide variety of collection devices for achieving rich knowledge 21 . Consequently, the data mining research issues from the massive IoT data have attracted the researchers' attention 22…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, with the advances in portable devices development, IoT can make a large universe of diverse and valuable data which can be collected by users from a wide variety of collection devices for achieving rich knowledge. 21 Consequently, the data mining research issues from the massive IoT data have attracted the researchers' attention. 22 The present study aims to review the existing techniques of data mining algorithms in fog computing and clarify their advantages and disadvantages.…”

mentioning

confidence: 99%

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Frequent pattern mining algorithms in fog computing environments: A systematic review

Tehrani

Sharifi

Rahmani

2022

Concurrency and Computation

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Recent advances in technology have resulted in generating or collecting massive volumes of data from rich data resources such as sensors and mobile devices in Internet of Things (IoT). Using data mining techniques can help overcome the mining problem in Fog computing environments which include millions of IoT devices.In addition, it can optimize response times, recourse consumption, and scalability in IoT applications. Frequent pattern mining, as one of the fundamental data mining tasks, is used for finding hidden patterns in such large datasets. The traditional data mining algorithms have many challenges such as scalability and resource consumption. This systematic review aimed to investigate the data mining algorithms, which focus on handling massive datasets, and present a technical taxonomy including the transaction-centric, item-centric, distributed, and parallel topics. The transaction-centric and MapReduce-based approaches were mostly utilized by 37% and 38%, respectively. Additionally, item-centric, distributed, and parallel algorithms were employed 12% and 13%, respectively. The response time as a Quality of Service (QoS) factor had the highest percentage in the estimations of data mining algorithms (55%), followed by scalability (25%), and cost (20%). To the best of our knowledge, no study has focused on fog-computing frequent pattern mining algorithms as one of the most important data mining tasks. This article aims to present a systematic review of the frequent pattern mining algorithms in fog computing and discuss the issues, challenges, and research perspectives for helping academia and industry leverage the power of data mining algorithms in fog computing.

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Section: Organization Of Data Mining Algorithms In Fog Computingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Frequent pattern mining algorithms in fog computing environments: A systematic review

Tehrani

Sharifi

Rahmani

2022

Concurrency and Computation

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“…The lack of consideration of attribute dependency enhances the computational cost and reduces mining accuracy. Furthermore, existing techniques (e.g., [86], [87], [90], [92]) are not evaluated by real deployment and not tested or suitable for IoT big data (e.g., [63], [95], [96]). To resolve these shortcomings, here we propose a knowledge-based framework that can mine the pattern in online as well as offline which is shown in Figure 15.…”

Section: An Behavioral Pattern Mining Framework For Iotmentioning

confidence: 99%

A Survey on Behavioral Pattern Mining From Sensor Data in Internet of Things

et al. 2020

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The deployment of large-scale wireless sensor networks (WSNs) for the Internet of Things (IoT) applications is increasing day-by-day, especially with the emergence of smart city services. The sensor data streams generated from these applications are largely dynamic, heterogeneous, and often geographically distributed over large areas. For high-value use in business, industry and services, these data streams must be mined to extract insightful knowledge, such as about monitoring (e.g., discovering certain behaviors over a deployed area) or network diagnostics (e.g., predicting faulty sensor nodes). However, due to the inherent constraints of sensor networks and application requirements, traditional data mining techniques cannot be directly used to mine IoT data streams efficiently and accurately in real-time. In the last decade, a number of works have been reported in the literature proposing behavioral pattern mining algorithms for sensor networks. This paper presents the technical challenges that need to be considered for mining sensor data. It then provides a thorough review of the mining techniques proposed in the recent literature to mine behavioral patterns from sensor data in IoT, and their characteristics and differences are highlighted and compared. We also propose a behavioral pattern mining framework for IoT and discuss possible future research directions in this area. INDEX TERMS Association rules, behavioral patterns, data mining, frequent pattern, Internet of Things, knowledge discovery, wireless sensor networks.

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Data Mining Techniques in IoT Knowledge Discovery: A Survey

Rabdo

Beyene

2021

Advances in Intelligent Systems and Computing

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An Efficient Parallel Mining Algorithm Representative Pattern Set of Large-Scale Itemsets in IoT

Cited by 5 publications

References 21 publications

Frequent pattern mining algorithms in fog computing environments: A systematic review

Frequent pattern mining algorithms in fog computing environments: A systematic review

A Survey on Behavioral Pattern Mining From Sensor Data in Internet of Things

Data Mining Techniques in IoT Knowledge Discovery: A Survey

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