Data and knowledge mining with big data towards smart production

Cheng, Ying; Chen, Ken; Sun, Hemeng; Zhang, Yongping; Tao, Fei

doi:10.1016/j.jii.2017.08.001

Cited by 155 publications

(80 citation statements)

References 42 publications

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“…The DPC algorithm can achieve efficient clustering of arbitrary-shape data sets. The algorithm process is based on (1) and (2). Suppose there are n participants and each participant a i has a q-dimensional sample data a ( ) i .…”

Section: Dpc Algorithmmentioning

confidence: 99%

A privacy‐preserving density peak clustering algorithm in cloud computing

Sun

Liu

et al. 2020

Concurrency and Computation

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Aiming at preventing the privacy disclosure of sensitive information, issues related to privacy protection in cloud computing have attracted the interest of researchers. To protect the privacy of users during clustering in a cloud computing environment, we present a privacy-preserving density peak clustering (PPDPC) algorithm that neither discloses personal privacy information nor leaks the cluster centers. Our scheme contains two steps of density peak clustering: First, a cloud service provider calculates the cluster centers without knowing each participant's private data and without disclosing any cluster center information to the other participants, and second, participant allocation is secure and every participant is prevented from identifying the other members of the same cluster. Security analysis and comparison experiments show that the proposed PPDPC algorithm not only obtains good accuracy with respect to density peak clustering but also resists collusion attacks even if the cloud service provider is collaborating with all except one participant. Both theoretical analysis and experimental results confirm the security and accuracy of our method. KEYWORDS cloud computing, data mining, density peak clustering, homomorphic encryption, privacy preservation INTRODUCTIONWith the rapid development of mobile social networks and computer technology, all types of mobile terminals and servers have started generating huge amounts of data at all times, presenting a serious challenge to the computing ability of enterprises. 1,2 Cloud computing technology, which is used to address this challenge, is growing. More and more enterprises are storing data in cloud servers to save economic costs; its powerful computing power is convenient for handling huge amounts of data. [3][4][5] Additionally, data mining technology can help users analyze and extract key value information from a large amount of data in scientific research and business applications. The analysis of these data allows the prediction of future development trends and directions. 6 Clustering, as one of the important research methods of data mining, aims to divide data objects into several clusters such that object similarity in a cluster is high, while the similarity between each cluster is low.In the process of clustering analysis, a large amount of user-based privacy data, such as geographical location, electricity consumption data, and spatiotemporal sensing data, is collected and analyzed. [7][8][9] The security and privacy of this data depends on the security of cloud services.The sensitive data is directly outsourced to the cloud server for calculation, at which point the user's privacy may be leaked if the cloud service provider is malicious or dishonest. If multiple users collude with each other, they combine their own information to calculate their respective distances and then calculate the cluster centers by distance. If user's privacy and cluster centers are disclosed, serious consequences can result.Therefore, the development of a data mining techn...

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Section: Dpc Algorithmmentioning

confidence: 99%

A privacy‐preserving density peak clustering algorithm in cloud computing

Sun

Liu

et al. 2020

Concurrency and Computation

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“…Big data has been used by Raytheon Corp to enable smart factory with a high capacity of managing information from various sources, such as sensors, equipment, Internet transactions, simulations, and digital records of the company [6]. More detailed information about big data concept and implementations are provided in [7][8].…”

Section: Smart Manufacturing In Shipbuilding Industrymentioning

confidence: 99%

Smart Manufacturing Concept in Shipbuilding Process with Related Optimization Issues and Strategies

Endharta¹,

Norita²,

Singgih³

et al. 2018

Proceeding of Marine Safety and Maritime Installation (MSMI 2018)

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Smart manufacturing is mainly dealing with the collection of real time data using sensors and the data utilization to improve a system. In this study, important characteristics of smart manufacturing, which must be considered in shipbuilding are presented. Shipbuilding process itself consists of various complex operations, starting from the ship design until the delivery to customers. However, this paper only focuses on the production and control activities for the shipbuilding product. Some required smart technologies, such as augmented reality, big data, and smart sensors, are listed. Various optimization problems in shipbuilding process that utilizes the collected data are defined. In addition, strategies related with smart manufacturing concept, which are required to solve problems are addressed. Relations between the optimization problems and strategies are described, and expected increase in the company's productivity after the implementation of the smart manufacturing concept is discussed. In addition, suggestions for future researches are proposed.

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“…The general process of DM also known as knowledge discovery in databases (KDD) includes problem clarification, data preparation, preprocessing, DM in the narrow sense, and interpretation and evaluation of results [4]. DM in the narrow sense as a step in the KDD process consists of applying data analysis and particular discovery algorithm within an acceptable computational efficiency limit [4]. DM tasks can be classified into descriptive and predictive two groups [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…DM in the narrow sense as a step in the KDD process consists of applying data analysis and particular discovery algorithm within an acceptable computational efficiency limit [4]. DM tasks can be classified into descriptive and predictive two groups [4,5]. Descriptive function of DM mainly aims to explore the potential or recessive rules, characteristics, and relationships (dependency, similarity, etc.)…”

Section: Introductionmentioning

confidence: 99%

Data Mining for Material Feeding Optimization of Printed Circuit Board Template Production

Zheng

Kim

et al. 2018

Journal of Electrical and Computer Engineering

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Improving the accuracy of material feeding for printed circuit board (PCB) template orders can reduce the overall cost for factories. In this paper, a data mining approach based on multivariate boxplot, multiple structural change model (MSCM), neighborhood component feature selection (NCFS), and artificial neural networks (ANN) was developed for the prediction of scrap rate and material feeding optimization. Scrap rate related variables were specified and 30,117 samples of the orders were exported from a PCB template production company. Multivariate boxplot was developed for outlier detection. MSCM was employed to explore the structural change of the samples that were finally partitioned into six groups. NCFS and ANN were utilized to select scrap rate related features and construct prediction models for each group of the samples, respectively. Performances of the proposed model were compared to manual feeding, ANN, and the results indicate that the approach exhibits obvious superiority to the other two methods by reducing surplus rate and supplemental feeding rate simultaneously and thereby reduces the comprehensive cost of raw material, production, logistics, inventory, disposal, and delivery tardiness compensation.

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Data and knowledge mining with big data towards smart production

Cited by 155 publications

References 42 publications

A privacy‐preserving density peak clustering algorithm in cloud computing

A privacy‐preserving density peak clustering algorithm in cloud computing

Smart Manufacturing Concept in Shipbuilding Process with Related Optimization Issues and Strategies

Data Mining for Material Feeding Optimization of Printed Circuit Board Template Production

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