Constrained Big Data Mining in an Edge Computing Environment

Leung, Carson K.; Deng, Dayong; Hoi, Calvin S. H.; Lee, Wookey

doi:10.1007/978-981-13-0695-2_8

Cited by 13 publications

(5 citation statements)

References 27 publications

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“…This study focuses on FPM among the data mining tasks for identifying the frequently occurring item sets and most interesting relations between patterns in a given dataset, especially for the IoT‐related applications. Figure 6 shows the taxonomy of data mining in Fog computing, which includes transaction‐centric or serial, item‐centric, distributed/parallel, and MapReduce 24–26 . The articles on the data mining algorithms in fog computing were surveyed.…”

Section: Research Selection Methodsmentioning

confidence: 99%

“…The reduce function combines and aggregates the list related to the output of the map function. To deal with a huge data set, the MapReduce‐based algorithms fetch and separate input data into some partitions 25 …”

Section: Organization Of Data Mining Algorithms In Fog Computingmentioning

confidence: 99%

“…Figure 6 shows the taxonomy of data mining in Fog computing, which includes transaction-centric or serial, item-centric, distributed/parallel, and MapReduce. [24][25][26] The articles F I G U R E 3 The conditions for selecting the studies…”

Section: Phase 2: Conducting the Reviewmentioning

confidence: 99%

“…To deal with a huge data set, the MapReduce-based algorithms fetch and separate input data into some partitions. 25 Rani et al 36 proposed an effective algorithm named vertical partitioning parallel distributed algorithm (VPPDA) which used MapReduce for mining sensors by FPM. The VPPDA eliminated the overhead of inter-process communication by introducing overlapped window concepts.…”

Section: Mapreduce-based Algorithmsmentioning

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: Research Selection Methodsmentioning

confidence: 99%

Section: Organization Of Data Mining Algorithms In Fog Computingmentioning

confidence: 99%

Section: Phase 2: Conducting the Reviewmentioning

confidence: 99%

Section: Mapreduce-based Algorithmsmentioning

confidence: 99%

See 2 more Smart Citations

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

Tehrani

Sharifi

Rahmani

2022

Concurrency and Computation

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“…However, it is often difficult to derive value from the data of a single user. More user data needs to be involved in the analysis and refinement to get comprehensive information [4]. In traditional centralized machine learning, data is often stored centrally in a centralized server.…”

Section: Introductionmentioning

confidence: 99%

FLPP: A Federated-Learning-Based Scheme for Privacy Protection in Mobile Edge Computing

Cheng,

Ji,

You

et al. 2023

Entropy

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Data sharing and analyzing among different devices in mobile edge computing is valuable for social innovation and development. The limitation to the achievement of this goal is the data privacy risk. Therefore, existing studies mainly focus on enhancing the data privacy-protection capability. On the one hand, direct data leakage is avoided through federated learning by converting raw data into model parameters for transmission. On the other hand, the security of federated learning is further strengthened by privacy-protection techniques to defend against inference attack. However, privacy-protection techniques may reduce the training accuracy of the data while improving the security. Particularly, trading off data security and accuracy is a major challenge in dynamic mobile edge computing scenarios. To address this issue, we propose a federated-learning-based privacy-protection scheme, FLPP. Then, we build a layered adaptive differential privacy model to dynamically adjust the privacy-protection level in different situations. Finally, we design a differential evolutionary algorithm to derive the most suitable privacy-protection policy for achieving the optimal overall performance. The simulation results show that FLPP has an advantage of 8∼34% in overall performance. This demonstrates that our scheme can enable data to be shared securely and accurately.

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An Intelligent Predictive Analytics System for Transportation Analytics on Open Data Towards the Development of a Smart City

Audu

Cuzzocrea

Leung

et al. 2019

Advances in Intelligent Systems and Computing

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Constrained Big Data Mining in an Edge Computing Environment

Cited by 13 publications

References 27 publications

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

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

FLPP: A Federated-Learning-Based Scheme for Privacy Protection in Mobile Edge Computing

An Intelligent Predictive Analytics System for Transportation Analytics on Open Data Towards the Development of a Smart City

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