List of Articles big data

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  1 - Comparative Study, Applications and Challenges of Big Data Analysis Technologies
  Yaser Ghasemi nejad Abbass Ketabchi
  10.52547/jstpi.20796.15.60.66
  oday, receiving and sharing information is easier and cheaper than before, enabling organizations to handle large volumes of data at a high speed and variety in the name of big data. Big data technology provides many opportunities when problems are resolved correctly. D More

  oday, receiving and sharing information is easier and cheaper than before, enabling organizations to handle large volumes of data at a high speed and variety in the name of big data. Big data technology provides many opportunities when problems are resolved correctly. Data processing technologies in the past are not suitable for dealing with large quantities of generated data. While Suggested frameworks for big data applications help to store, analyze and process data. In this study, we first reviewed and summarized the big data definitions, and challenges of using it and then a number of important big data frameworks (Hadoop, Flink, Storm, Spark and Samza) have been studied and compared comparatively. The studied framework of big data is generally classified into two categories: (1) batch mode; and (2) stream mode. The Hadoop framework processes data in batch mode, while other frameworks allow stream or real time processing. Ultimately, the most important applications of using big data technology have been described. The most important applications for big data analysis are healthcare applications, advisory systems, smart cities and social networks analysis. Due to the growth of Internet-connected devices, social networking data is growing widely and requires more big data technology. Also, the most challenges of big data application, including confidentiality in storage systems, software deficiencies and the limitation of existing hardware and equipment, the need for large initial investment and the lack of technical skills and expert workforce. Manuscript profile
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  2 - Big IoT Data from the Perspective of Smart Agriculture
  Bahareh Jamshidi Hossein  Dehghanisani
  10.52547/jstpi.20875.16.63.12
  Internet of Things (IoT) as an emerging technology in the field of Information and Communication Technology is the next revolution related to the Internet application. IoT focuses on the communication of things such as sensors, drivers, devices, etc., with data collecti More

  Internet of Things (IoT) as an emerging technology in the field of Information and Communication Technology is the next revolution related to the Internet application. IoT focuses on the communication of things such as sensors, drivers, devices, etc., with data collection capability controlling remote communication rather than focusing on the communication between people. Development of smart solutions and new technologies of IoT in agriculture can pave the way to a new paradigm of farming called “Smart Agriculture” by making a fundamental change in all aspects of current practices. IoT-based Smart Agriculture can improve agricultural productivity with more food production through the optimal utilization of the basic resources, minimizing environmental impacts, reducing the costs, and increasing the incomes with linking to the business market that facilitates sustainable agricultural development goals. IoT-based data is a collection of large data called “Big Data” that cannot be processed and managed by traditional databases and conventional management tools. IoT and Big Data technologies are interconnected and it can be predicted that the future of optimal agriculture in the world would not be possible to meet the food demand and sustainability of production without these technologies and Smart Agriculture. This article introduces IoT and Big Data technologies, as well as the relationship between them from the vision of Smart Agriculture. Moreover, the article aims to assist in the decision-making of the strategy from the pre-production stage to the business marketing in the country by assessing life cycle and technology trends. Some of the big IoT data applications in the Smart Agriculture cycle are also introduced. Manuscript profile
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  3 - An Approximate Binary Tree-Based Solution to Speed Up the Search for the Nearest Neighbor in Big Data
  Hosein Kalateh M. D.
  Due to the increasing speed of information production and the need to convert information into knowledge, old machine learning methods are no longer responsive. When using classifications with the old machine learning methods, especially the use of inherently lazy class More

  Due to the increasing speed of information production and the need to convert information into knowledge, old machine learning methods are no longer responsive. When using classifications with the old machine learning methods, especially the use of inherently lazy classifications such as the k-nearest neighbor (KNN) method, the operation of classifying large data sets is very slow. Nearest Neighborhood is a popular method of data classification due to its simplicity and practical accuracy. The proposed method is based on sorting the training data feature vectors in a binary search tree to expedite the classification of big data using the nearest neighbor method. This is done by finding the approximate two farthest local data in each tree node. These two data are used as a criterion for dividing the data in the current node into two groups. The data set in each node is assigned to the left and right child of the current node based on their similarity to the two data. The results of several experiments performed on different data sets from the UCI repository show a good degree of accuracy due to the low execution time of the proposed method. Manuscript profile
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  4 - Clustering Iranian Gas Industry Managers and Ranking Their Competencies via the EFQM Excellence Model-based Evaluation with an Artificial Intelligence Approach
  Ali reza Zamanian Majid Jahangirfard Farshad Hajalian
  10.52547/istd.31542.8.31.51
  This study attempted to lay the ground for linking human resources data based on the results of the organizational excellence model for about 51 parent and subsidiary companies of the National Iranian Gas Company using artificial intelligence (AI) and machine learning m More

  This study attempted to lay the ground for linking human resources data based on the results of the organizational excellence model for about 51 parent and subsidiary companies of the National Iranian Gas Company using artificial intelligence (AI) and machine learning methods. The goal was to present a model for clustering chief organizational managers based on the companies’ evaluation using the European Foundation for Quality Management (EFQM)-based excellence model. The unique characteristic of this method is that it is formed based on the actual performance and output of successful organizations, headed by successful managers and leaders. Accordingly, a performance-based excellence model can be achieved in the future. The outcomes of model evaluation for 2017, 2018, and 2019 for 51 companies affiliated with the National Iranian Gas Company were first clustered. Clustering was performed for 3776 pieces of data via AI-based methods, and coding was done in Python. This applied study aimed to design and develop a novel method for discovering the experts and scientifically classifying the organization’s human resources based on credible data. It also aimed to integrate novel scientific domains of AI, including clustering, to pave the ground for human resources research. In the applied dimension, the results were used in organizational planning and decision-making to generate a tool whereby the future managerial performance of the organization and staff can be predicted based on appropriate human resources data. Finally, a ranking is presented based on the competency gap by using Fisher discriminant ratio (FDR). Manuscript profile
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  5 - Data-driven Marketing in Digital Businesses from Dynamic Capabilities View
  Maede  Amini vlashani ayoub mohamadian Seyed Mohammadbagher Jafari
  Despite the enormous volume of data and the benefits it can bring to marketing activities, it is unclear how to use it in the literature, and very few studies have been conducted in this field. In this regard, this study uses dynamic capabilities view to identify the dy More

  Despite the enormous volume of data and the benefits it can bring to marketing activities, it is unclear how to use it in the literature, and very few studies have been conducted in this field. In this regard, this study uses dynamic capabilities view to identify the dynamic capabilities of data-driven marketing to focus on data in the development of marketing strategies, make effective decisions, and improve efficiency in marketing processes and operations. This research has been carried out in a qualitative method utilizing the content analysis strategy and interviews with specialists. The subjects were 18 professionals in the field of data analytics and marketing. They were selected by the purposeful sampling method. This study provides data-driven marketing dynamic capabilities, including; Ability to absorb marketing data, aggregate and analyze marketing data, the ability to data-driven decision-making, the ability to improve the data-driven experience with the customer, data-driven innovation, networking, agility, and data-driven transformation. The results of this study can be a step towards developing the theory of dynamic capabilities in the field of marketing with a data-driven approach. Therefore, it can be used in training and creating new organizational capabilities to use big data in the marketing activities of organizations, to develop and improve data-driven products and services, and improve the customer experience Manuscript profile
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  6 - The main components of evaluating the credibility of users according to organizational goals in the life cycle of big data
  Sogand Dehghan shahriyar mohammadi rojiar pirmohamadiani
  Social networks have become one of the most important decision-making factors in organizations due to the speed of publishing events and the large amount of information. For this reason, they are one of the most important factors in the decision-making process of inform More

  Social networks have become one of the most important decision-making factors in organizations due to the speed of publishing events and the large amount of information. For this reason, they are one of the most important factors in the decision-making process of information validity. The accuracy, reliability and value of the information are clarified by these networks. For this purpose, it is possible to check the validity of information with the features of these networks at the three levels of user, content and event. Checking the user level is the most reliable level in this field, because a valid user usually publishes valid content. Despite the importance of this topic and the various researches conducted in this field, important components in the process of evaluating the validity of social network information have received less attention. Hence, this research identifies, collects and examines the related components with the narrative method that it does on 30 important and original articles in this field. Usually, the articles in this field are comparable from three dimensions to the description of credit analysis approaches, content topic detection, feature selection methods. Therefore, these dimensions have been investigated and divided. In the end, an initial framework was presented focusing on evaluating the credibility of users as information sources. This article is a suitable guide for calculating the amount of credit of users in the decision-making process. Manuscript profile
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  7 - Providing a New Solution in Selecting Suitable Databases for Storing Big Data in the National Information Network
  Mohammad Reza Ahmadi davood maleki ehsan arianyan
  The development of infrastructure and applications, especially public services in the form of cloud computing, traditional models of database services and their storage methods have faced sever limitations and challenges. The increasing development of data service produ More

  The development of infrastructure and applications, especially public services in the form of cloud computing, traditional models of database services and their storage methods have faced sever limitations and challenges. The increasing development of data service productive tools and the need to store the results of large-scale processing resulting from various activities in the national network of information and data produced by the private sector and pervasive social networks has made the process of migrating to new databases with appropriate features inevitable. With the expansion and change in the size and composition of data and the formation of big data, traditional practices and patterns do not meet new needs. Therefore, it is necessary to use data storage systems in new and scalable formats and models. This paper reviews the essential solution regarding the structural dimensions and different functions of traditional databases and modern storage systems and technical solutions for migrating from traditional databases to modern ones suitable for big data. Also, the basic features regarding the connection of traditional and modern databases for storing and processing data obtained from the national information network are presented and the parameters and capabilities of databases in the standard platform context and Hadoop context are examined. As a practical example, a combination of traditional and modern databases using the balanced scorecard method is presented as well as evaluated and compared. Manuscript profile