A novel clustering algorithm for time-series data based on precise correlation coefficient matching in the IoT

Li, Hai Bo; Tong, Jun Cheng

doi:10.3934/mbe.2019331

Cited by 10 publications

(2 citation statements)

References 40 publications

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“…Li, Haibo, and Juncheng Tong (16). Proposed a time series based clustering algorithm based on precise correlation coefficient matching (CCPM).…”

Section: Review Of Literaturementioning

confidence: 99%

IoT Data Analysis Using Different Clustering Algorithms: A Survey

Das¹,

Bora²

2022

ECS Trans.

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There has been a major change in the rise of data creation and consumption due to the quick development of contemporary technology and the advent of the Internet of Things (IoT). With this shift comes a significant challenge in data management, with the goal of extracting useful information and patterns from the vast amount of unstructured IoT data. However, due to some limitations, we are still encountering issues and challenges in obtaining patterns and meaning information, and thus formulating a research agenda to get a sense of the challenges that one might encounter while conducting research on clustering of this massive chunk of unstructured IoT data. In light of these considerations, this study looks at some clustering methods that may be utilized when working with unstructured IoT data.

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“…Li, Haibo, and Juncheng Tong (16). Proposed a time series based clustering algorithm based on precise correlation coefficient matching (CCPM).…”

Section: Review Of Literaturementioning

confidence: 99%

IoT Data Analysis Using Different Clustering Algorithms: A Survey

Das¹,

Bora²

2022

ECS Trans.

View full text Add to dashboard Cite

show abstract

“…In [22], a recommendation of service chains was proposed. Another kind of composition is for the purpose of detecting a business anomaly [23] and correlation coefficient matching [24].…”

Section: Introductionmentioning

confidence: 99%

Composition of Resource-Service Chain Based on Evolutionary Algorithm in Distributed Cloud Manufacturing Systems

Weng

Tong

et al. 2020

IEEE Access

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In distributed cloud manufacturing (CMfg) systems, multi-resource service can complete more complex manufacturing tasks than single resource service. Especially in business process, all the resource services are invoked in a certain sequence, which is called the Resource-Service Chain (RSC). The RSC, as a sequential composition of resource services, expresses the scheduling and the flow of servicing to a distributed business process. A perfect composition can improve utilization ratio and efficient matching availability of resource services greatly. However, most of the existing methods for resource service composition paid no attention to the temporal relationship between resource services. Moreover, the methods strongly depend on relevant element to be considered. Inspired by biological evolution, a Resource-Service Chain Composition Evolutionary (RSCCE) algorithm is proposed. Specifically, RSCCE tries to find multiple optimal solutions, namely all RSCs in a workflow with given constraints. To begin, initial sets of composite resource service are resolved by calculating the degree of dependency between resource services, so as to obtain initial RSCs by workflow. Then, RSCCE algorithm applies genetic algorithm to search for the extended of each initial RSC, a longer chain composing of it, to improve the reuse of RSC. Under this approach, gene and chromosome represent resource service and the entire RSC respectively. If the propagated chromosomes violate the sequence of resource service, as constraint in RSCCE algorithm, they will be repaired to obtain a valid solution. Finally, we take a multi-enterprise collaborative business process as an example to simulate our approach. Experimental results confirm the effectiveness of the approach.

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Discovering the Coopetition Relationship Between Agents Using Clustering

Chen

et al. 2021

Advances in Intelligent Automation and Soft Computing

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A novel clustering algorithm for time-series data based on precise correlation coefficient matching in the IoT

Cited by 10 publications

References 40 publications

IoT Data Analysis Using Different Clustering Algorithms: A Survey

IoT Data Analysis Using Different Clustering Algorithms: A Survey

Composition of Resource-Service Chain Based on Evolutionary Algorithm in Distributed Cloud Manufacturing Systems

Discovering the Coopetition Relationship Between Agents Using Clustering

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