Big Data Analytics In Cloud Computing

Big Data Analytics In Cloud Computing – Resources and Information Access to SME Organizations in the Era of IR 4.0: Mediators and Partners of Innovation and Corporate Responsibility.

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Big Data Analytics In Cloud Computing

Big Data Analytics In Cloud Computing

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Big Data Analytics For Cloud, Iot And Cognitive Computing Ebook By Kai Hwang

Dynamic Control Strategy of Main Supplier and Subcontractors Using Industrial Internet of Things and Cloud System.

Received: 10 February 2019 / Revised: 20 March 2019 / Accepted: 20 March 2019 / Published: 24 March 2019

(This article is from the Special Edition Business 4.0: Small and Medium Enterprises (SME) Application Research)

Big Data Analytics In Cloud Computing

Gathering intelligent computer data is essential in managing a large network in today’s manufacturing environment. Data and information are shared using the cloud environment, and more knowledge is driven by the use of intelligent analytics. This study used this phenomenon to control the production plan of the main supplier for achieving joint production goals with the downstream producers. Since the main supplier has many downstream companies, there are many uncertainties in the supply chain such as product quality problems and the result of unexpected decisions to of companies below. Although the management of the distribution plan is difficult to determine these changes in the traditional system, the planning process can identify the change using the cloud design. . In addition, real-time control is achieved by considering unpredictable events as well as knowledge extracted from the Internet of Things (IIoT) and control models. based on a simulation using a stochastic network. To demonstrate the effectiveness of the production process, production data and their numerical values ​​are provided.

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Participation in the development of rivers; The Internet of Things (IIoT); cloud environment; stochastic management; collaborative simulation optimization in production/downstream processes; The Internet of Things (IIoT); cloud environment; stochastic energy; simulation-based optimization

The development of Information and Communication Technology (ICT) has changed many aspects of manufacturing and connected devices. Cloud computing and intelligent data analysis are the main technologies among them [1]. Many companies and organizations in the world complete this technology with the necessary procedures to ensure the positive results of the environment. This model comes from the fact that more knowledge can be extracted from the data stored in the cloud array. Many researchers, eg. Chen et al. [2], Hu et al. [3] and Abdollahi et al. [4], an in-depth analysis of cloud environments and related big data analytics.

This study focused on the dynamic power of a key supplier in a large retail chain. Although the main supplier has followed most of the downstream producers, the established producers are responsible for fulfilling the needs of the downstream producers. However, real-time quality issues from the supplier and the resulting unpredictable decision making from downstream manufacturers can affect production goals. . In order to better manage the situation, business development partners and production partners have set up a cloud that collects data and the execution of the order high and distributed using the Industrial Internet of Things (IIoT). This paper presents a new and effective strategy of supplier critical management using IIoT and cloud-based system. To connect to the cloud, an Internet of Things device will be created. Although the success and quality of data are collected by using IIoT devices and cloud environments, the relationship between data processing as data input and results is good. as the output data is determined using the input data. In addition, the causal law and various production factors are incorporated into the stochastic network-based control model.

This study provides effective control strategy of the main supplier using stochastic control model and cloud-based intelligent data environment. While other research studies have focused on the importance of the seller and the producer with a theoretical or theoretical framework, this study has focused on the role of the supplier importance and advancement of information technology for solving all production problems.

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Part 2 is a review of key documents and information. Chapter 3 provides cloud computing network architecture with the main service providers and some manufacturers that use real-world architecture. Chapter 4 describes the development of cloud and intelligent data analysis for applications. Chapter 5 describes energy management using cloud design and simulation-based control models.

This chapter describes the importance of a key salesperson in various manufacturing settings and reviews empirical studies. Therefore, in order to improve the relationship between the main service providers and the producers in the slope, the relationship between the relationship is shown using the cloud environment.

Many researchers maintain that Teller et al. [5] investigated the importance of key supplier relationships for effective supply chain management. Especially Nair et al. [6] conducted a survey of companies in the downstream industry. This trend shows that the opinions and ideas of the main salesperson are not analyzed. Although joint problem solving and joint planning are some good strategies to improve the relationship between products [7], most of the management is not the supplier. goods, but low producers. In addition, previous research examines these relationships and interactions with theoretical factors. This study focused on the role of the key supplier in solving production problems and planning production and operations.

Big Data Analytics In Cloud Computing

The ability and coordination of one of the managers of integrated solutions will result from the development in the cloud environment and big data analytics related to the supply chain and the global production. Advances in disruptive technologies leading to the fourth industrial revolution have enabled new changes in the supply chain management (SCM) field. Aryal et al. [8] show how IIoT and big data analytics affect SCM innovation. Müller et al. [9] provide the rules and strategies in the providers of integrated services using this technology. Hofmann and Rusch [10] analyzed the effects of Business 4.0 technologies on SCM sequences such as production planning, production order, delivery/production and export. In particular, they have shown that using the cloud can combine information with appropriate methods.

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Figure 1 shows the current research in computer science and technology. Although the cloud is now being used for data sharing between related organizations, their main problem is using shared data to achieve multiple goals. Critical studies investigate issues related to architectures and their implementation. Thus, the cloud environment provides analytical tools and intelligence. These cases show that the process of thinking and knowing about knowledge is a very important process for the success of using the cloud that has been created. Ikram et al. [11] used a neural network for analyzing big data on a cloud platform. A number of machine learning techniques including Support Vector Machine (SVM) [12] and many heuristic algorithms [13, 14, 15] are used for cognitive and task-specific systems. So, the cloud is now being developed and integrated with deep learning. Zhang et al. [16] used the Canonical Polyadic Decomposition (CPD) deep learning model for forecasting air traffic. Roob et al. [17] applied the deep learning method to the control of mass air. These analyzes are called big data analysis, because the data in the cloud is so large that its processing time is not limited.

The knowledge extracted from the air is used for many applications. One of the most popular applications is to find faults in a large production facility. Gao and Zhu [18] used cloud computing to detect data errors and Lee [1] used cloud computing to explain.

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