Concepts and applications of data mining and analysis of social networks

Social media has become an important reference for information during the last few decades. They have been able to be effective in various fields such as business, entertainment, science, crisis management, politics, etc. For this reason, a social media analysis has become very important for researchers and large companies. The widespread use of social media leads to a complex problem called "accumulation of data". Many data science specialists seek to analyze this data in order to identify the behavioral characteristics of users, analyze interests and needs, and improve marketing processes. Different social media platforms have the ability to use all kinds of media, including text data, video, video, audio, and location information, etc. Therefore, data analysis in social networks is very important. In this research, the concepts and applications of data analysis in social networks will be investigated

Internet of things and the future of digital marketing

In the digital world these days, everywhere you look, traces of the Internet of Things (IoT) can be seen. Businesses will improve with the use of this technology because devices will be able to collect a huge amount of user information. . If this information is used appropriately, commercial activities are able to expand their activities, reduce costs and provide users with devices with more and better features. These devices are only allowed to be accessed by certain people because they contain a large amount of information. However, there are many concerns about these devices. Even though the technology is advancing day by day, the risks related to it also increase. With the trend of marketing toward digitalization, we are witnessing its spread in various businesses. But since the Internet of Things has a solution for every issue and challenge, it also has new and fresh solutions for this issue. In this article, an attempt has been made to explain the role of the Internet of Things in digital marketing

Internet of Things for Customer Relationship Management (CRM) Software: Opportunities and Benefits

Customer relationship management software has many advantages for different businesses and economic enterprises. Today, the Internet has become the main tool for information exchange, business, communication, and an integral part of daily life. Internet technologies have witnessed fundamental changes in the way of interaction over a decade. With new technology, objects are also connected to the Internet and with humans; Intelligent systems and data are interacting and exchanging. The Internet of Things will increase the maturity level of customer relationships and will have a profound impact on customer relationship management. This is because the huge data of the Internet of Things contains information about the customer, and textual inputs can greatly increase the capabilities of managing relationships with customers. Therefore, the future of customer relationship management will be provided by cognitive computing, big data analysis, and creating deep knowledge in organizations. In this paper, the vital features and opportunities, and benefits of CRM systems based on the Internet of Things have been examined

Machine learning with big data to solve real-world problems

Machine learning algorithms use big data to learn future trends and predict them for businesses. Machine learning can be very efficient for deciphering data in industries where understanding consumer patterns can lead to big improvements. The use of machine learning can be a giant leap for businesses and cannot simply be integrated as the top layer. This requires redefining workflow, architecture, data collection and storage, analytics, and other modules. The magnitude of the system overhaul should be assessed and clearly communicated to the appropriate stakeholders. The main focus of machine learning is to develop computer programs that can access data and use it to learn. The learning process starts with observations or data, to find a pattern in the data and make better decisions. The main goal of data analysis using machine learning is that it allows the computer to learn automatically without human intervention and help and can adjust its actions accordingly. Considering the many applications that data analysis has found in the real world, therefore, in this article, a review of the basic applications of machine learning as one of the tools of artificial intelligence has been done with an emphasis on big data analysis. The purpose of this article is to understand the dimensions, components and applications, and challenges of using machine learning in the real world

Investigation of IoT applications in supply chain management with fuzzy hierarchical analysis

The IoT is currently growing rapidly and uses technologies such as smart barcode sensors, RFID, wireless communications, cloud computing, and more. The Internet of Things, in addition to being a revolutionary technology for all industries; has also demonstrated its potential in processes such as supply chain. Management, forecasting, and monitoring applications help managers improve the operational efficiency of their company distribution and increase transparency in their decisions. So more than ever, the benefits of using the Internet of Things are evident in the supply chain. The existence of comprehensive and valid information platforms is one of the requirements of supply chain management. Therefore, the most accurate use of integrated information devices such as Internet technology of objects in this part of the management of the organization is important. Coverage of this information accurately and in an instant facilitates matters and makes the process progress more transparent. To improve this process, cloud computing is used as a solution. In addition, other cloud computing capabilities can be used, such as facilitating object communication, integrating monitoring devices, and IoT storage, analyzing data, and paving the way for cyberspace to provide the customer with supply chain management. This requires a model that defines how Internet technology relates to objects, cloud computing, and supply chain management. The purpose of this study is to identify and prioritize IoT applications in the supply chain management sector with a multi-criteria decision-making approach. The results show that applications such as intelligent control and intelligent maintenance have the highest priorities

Big data based on IoT in the agriculture industry: developments, opportunities, and challenges ahead

With the creation of mobile phones and new technologies, new opportunities have been created in industries; This is while among these technologies are big data and the Internet of Things, which can help increase the potential of the agricultural industry. Meanwhile, technological advances have also changed the way data is collected, shared, and used. Governments and non-governmental actors are increasingly using digital technologies, and new sources and approaches to data have increased the determination of governments and professionals to further integrate data science with agriculture. Our goal is to evaluate the acceptance conditions of big data technologies in agricultural applications, based on the research conducted in this direction based on the growing technologies in this field. In this regard, the literature of the subject has been reviewed and the most important features, opportunities and challenges facing the agricultural industry in the face of the huge amount of data have been reviewed and evaluated. The results show that among the main characteristics of big data (volume, speed, variety, accuracy) in the field of agriculture, the most emphasis is on speed and variety. The main concern of the stakeholders is the cost, user-friendliness and embedding of the solution in their current working procedure

Big data and cloud computing: roles and relationships, techniques and tools

In the past years, the increase in data has been accompanied by rapid growth in various fields. It is difficult to analyze large volumes of data using traditional and relational database technology. Therefore, new databases have emerged, and for this reason, big data has become one of the new topics in IT and business today. Also, the cloud environment is increasingly used to store and process big data. Cloud processing refers to processing anything, including Big Data Analytics, on the "cloud". A "cloud" is a collection of high-powered servers from providers that can often view and query large data sets much faster than a regular computer. These two topics differ from each other in various aspects, including definition, collection references, usage method, form and format, and application. In this research, the dimensions and basic concepts, characteristics, tools and techniques, classification, and communication of data are examined. Big has been dealt with cloud computing, and in addition, storage systems, opportunities and challenges, and big data design principles in the cloud environment have been analyzed

The role of big data in digital transformation

Today, we are creating and collecting more information than ever before. All of this data comes from a variety of sources, including social media platforms, our phones and computers, health gadgets and wearable technologies, scientific tools, financial institutions, and more. When combined with big data, it gives businesses the opportunity to understand consumers better than ever before. Businesses are now using specific information as well as insights about customers and their behavior from the data they collect to transform themselves while simultaneously changing their sales and marketing strategies. Big data is at the center of all this. Digital transformation is an inevitable necessity and no organization or business will be safe. In the next few years, many existing businesses will be removed and new businesses based on digital technologies will be formed, and the digital economy will show off its true and complete meaning. Paying attention to human factors and their impact on the success of a digital transformation program is also very important. Correct leadership of the digital journey and change management and the culture governing a business are among the most important factors in the success of a digital transformation program. Undoubtedly, big data and its strategic management is one of the vital capabilities in achieving the success of a digital transformation program. Because the success of a business will be highly dependent on the level of intelligence regarding everything (from customers to competitors and the market situation, etc.). In this research, the dimensions and roles of big data management in digital transformations are investigated and the place of this technology among transformational technologies is examined and analyzed

Investigating the dimensions, components, and key indicators of the use of big data in the health industry

oai:ojs2.journal-data.ir:article/1Using big data analytics in healthcare has positive as well as life-saving results. Big data refers to the vast amounts of information generated by the digitization of everything that is synthesized and analyzed by specific technologies. Here Big Data uses health services to use specific health data of a population (or a specific individual) and potentially help prevent disease pandemics, treat diseases, reduce costs, and more. In the field of health, big data covers a wide range of information, including physiological, behavioral, molecular, clinical, medical imaging, disease management, medication history, nutrition, or exercise parameters. Big Data Analysis In the field of health, it is a complex process of examining big data to discover information. This information includes hidden patterns, market trends, unknown correlations, and customer preferences. Information analysis can help organizations make informed business and clinical decisions. The medical data-driven industry is the most complex among industries. Not only is this data available from a variety of sources, but it must also comply with government regulations. This process is difficult and delicate and requires some level of security and communication. Due to the importance of this issue, in this article, after introducing the types of data available in the health industry, the characteristics and sources of big data in health are defined and an analytical model for the use of large data in the health industry is presented. This model helps to understand the dimensions, components, and key elements of using big data in the health industry

Review the challenges of using big data in the supply chain

The increasing growth of computer networks and Internet-based technologies, followed by the growth of data and information required by their users and consumers, has led to the emergence of new concepts in this field. Big data is one of these concepts that has been considered by researchers in various fields of business in recent years. When looking at it from the outside, it is fair to assume that the more data a company or organization has, the better, because the company in question will have a larger amount of data for mining, and as a result their data will be more accurate. However, this is not always the case, because learning how to effectively manage Big Data has become a very challenging task for many businesses around the world. Working with big data involves collecting data from information sources, exploring and analyzing them, modeling them based on the desired features, and providing data security measures. For this reason, this paper examines the challenges of working with big data and the big data revolution in general and big data mining in the business supply chain as fundamental business processes