https://bura.brunel.ac.uk/bitstream/2438/13152/4/Fulltext.pdf

Critical analysis of Big Data challenges and analytical methods

Industry 4.0 is a concept devised for improving the way modern factories operate through the use of some of the latest technologies, like the ones used for creating the Industrial Internet of Things (IIoT), robotics, or Big Data applications. One of such technologies is blockchain, which is able to add trust, security, and decentralization to different industrial fields. This article focuses on analyzing the benefits and challenges that arise when using blockchain and smart contracts to develop Industry 4.0 applications. In addition, this paper presents a thorough review of the most relevant blockchain-based applications for Industry 4.0 technologies. Thus, its aim is to provide a detailed guide for the future Industry 4.0 developers that allows for determining how the blockchain can enhance the next generation of cybersecure industrial applications.

A Review on the Application of Blockchain to the Next Generation of Cybersecure Industry 4.0 Smart Factories

The future Internet of Things (IoT) is expected to enable a new and wide range of decentralized systems (from small-scale smart homes to large-scale smart cities) in which “things” are able to sense/actuate, compute, and communicate, and thus play a central and crucial role. The growing importance of such novel networked cyber-physical context demands suitable and effective computing paradigms to fulfill the various requirements of IoT systems engineering. In this paper, we propose to explore an agent-based computing paradigm to support IoT systems analysis, design, and implementation. The synergic meeting of agents with IoT makes it possible to develop smart and dynamic IoT systems of diverse scales. Our agent-oriented approach is specifically based on the agent-based cooperating smart object (ACOSO) methodology and on the related ACOSO middleware: they provide effective agent design and programming models along with efficient tools for the actual construction of an IoT system in terms of a multiagent system. A case study concerning the development of a complex IoT system, namely a Smart University Campus , is described to show the effectiveness and efficiency of the proposed approach.

Agent-Oriented Cooperative Smart Objects: From IoT System Design to Implementation

Online fault detection is one of the key technologies to improve the performance of cloud systems. The current data of cloud systems is to be monitored, collected and used to reflect their state. Its use can potentially help cloud managers take some timely measures before fault occurrence in clouds. Because of the complex structure and dynamic change characteristics of the clouds, existing fault detection methods suffer from the problems of low efficiency and low accuracy. In order to solve them, this work proposes an online detection model based on asystematic parameter-search method called SVM U+002D Grid, whose construction is based on a support vector machine U+0028 SVM U+0029. SVM U+002D Grid is used to optimize parameters in SVM. Proper attributes of a cloud system U+02BC s running data are selected by using Pearson correlation and principal component analysis for the model. Strategies of predicting cloud faults and updating fault sample databases are proposed to optimize the model and improve its performance. In comparison with some representative existing methods, the proposed model can achieve more efficient and accurate fault detection for cloud systems.

An online fault detection model and strategies based on SVM-grid in clouds

Managing, processing and understanding big healthcare data is challenging, costly and demanding. Without a robust fundamental theory for representation, analysis and inference, a roadmap for uniform handling and analyzing of such complex data remains elusive. In this article, we outline various big data challenges, opportunities, modeling methods and software techniques for blending complex healthcare data, advanced analytic tools, and distributed scientific computing. Using imaging, genetic and healthcare data we provide examples of processing heterogeneous datasets using distributed cloud services, automated and semi-automated classification techniques, and open-science protocols. Despite substantial advances, new innovative technologies need to be developed that enhance, scale and optimize the management and processing of large, complex and heterogeneous data. Stakeholder investments in data acquisition, research and development, computational infrastructure and education will be critical to realize the huge potential of big data, to reap the expected information benefits and to build lasting knowledge assets. Multi-faceted proprietary, open-source, and community developments will be essential to enable broad, reliable, sustainable and efficient data-driven discovery and analytics. Big data will affect every sector of the economy and their hallmark will be ‘team science’.

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Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data

Monitoring solutions using the Internet of Things (IoT) techniques, can continuously gather sensory data, such as temperature and pressure, and provide abundant information for a monitoring center. Nevertheless, the heterogeneous and massive data bring significant challenges to real-time monitoring and decision making, particularly in time-sensitive industrial environments. This paper presents an online monitoring system based on an IoT system architecture which is composed of four layers: 1) sensing; 2) network; 3) service resource; and 4) application layers. It integrates various data processing techniques including protocol conversion, data filtering, and data conversion. The proposed system has been implemented and demonstrated through a real continuous steel casting production line, and integrated with the TeamCenter platform. Results indicate that the proposed solution well addresses the challenge of heterogeneous data and multiple communication protocols in real-world industrial environments.

An IoT-Based Online Monitoring System for Continuous Steel Casting

Frequent itemset mining is an essential step in the process of association rule mining. Conventional approaches for mining frequent itemsets in big data era encounter significant challenges when computing power and memory space are limited. This paper proposes an efficient distributed frequent itemset mining algorithm (DFIMA) which can significantly reduce the amount of candidate itemsets by applying a matrix-based pruning approach. The proposed algorithm has been implemented using Spark to further improve the efficiency of iterative computation. Numeric experiment results using standard benchmark datasets by comparing the proposed algorithm with the existing algorithm, parallel FP-growth, show that DFIMA has better efficiency and scalability. In addition, a case study has been carried out to validate the feasibility of DFIMA.

A distributed frequent itemset mining algorithm using Spark for Big Data analytics

Evacuation path optimization based on quantum ant colony algorithm

With the coming of the Big Data era, data mining has been confronted with new opportunities and challenges. Some limitations are exposed when traditional association rule mining algorithms are used to deal with large-scale data. In the Apriori algorithm, scanning the external storage repeatedly leads to high I/O load and brings about low performance. As for FP-Growth algorithm, the effectiveness is limited by internal memory size because mining process is on the base of large tree-form data structure. What's more, although remarkable achievements have been scored, there are still problems in dynamic scenarios. The paper presents a parallelized incremental FP-Growth mining strategy based on MapReduce, which aims to process large-scale data. The proposed incremental algorithm realizes effective data mining when threshold value and original database change at the same time. This novel algorithm is implemented on Hadoop and shows great advantages according to the experimental results.

Incremental FP-Growth mining strategy for dynamic threshold value and database based on MapReduce

The invention relates to an incremental frequent pattern increase data mining method. The method comprises the following steps: (1) partitioning an original database into a plurality of data sets, and performing parallel computation on each support count in each data set; (2) grouping data in the original database, constructing a local frequent pattern tree, and extracting each local frequent item set through a recursive process; (3) integrating each local frequent item set; (4) updating a threshold value, and executing support count on the original database under a novel threshold value; (5) updating the local frequent pattern tree, and mining the frequent item set of the original database under the novel threshold value; (6) newly adding data sets to obtain a new database, and mining the strong frequent item set and newly-added frequent item set of the original database under the new threshold value. Compared with the prior art, the method has the advantages that the new frequent item set can be obtained by using the original frequent data item set and the frequent pattern tree and scanning the newly-added data set, thereby solving the problems of threshold value change and database addition at the same time, and greatly increasing the efficiency.

Feng Zhang

Papers

An IoT-Based Online Monitoring System for Continuous Steel Casting

A distributed frequent itemset mining algorithm using Spark for Big Data analytics

Evacuation path optimization based on quantum ant colony algorithm

Incremental FP-Growth mining strategy for dynamic threshold value and database based on MapReduce

Incremental frequent pattern increase data mining method