Application of Big Data Analysis Based on Artificial Intelligence in Accurate Communication of New Media

Big data analytics (BDA) is an advanced analytic technique used with very large and diverse sets of data from different sources. Natural language processing (NLP) is a technology that interfaces with different fields such as computer science, linguistics, and human-computer interactions. Over the past few years, there is a growing number of firms, which are using different BDA and NLP applications in their businesses. Only a few of the research have investigated different dimensions of NLP and BDA and their impacts on the overall organizational performance. There is a growing interest among researchers and practitioners in understanding the consequences for firms that adopt BDA and NLP applications. In this context, the aim of this article is to determine the factors for the usage of BDA and NLP applications in business. With the help of dynamic capability view theory and existing literature, a theoretical model was developed conceptually. Later, the model was validated using structural equation modeling approach considering 1287 samples from 23 firms, primarily based in Asia and Europe, which use NLP and BDA applications. The article finds that NLP and BDA applications help the firms to improve their operational efficiency, which in turn improves the overall firm performance.

The healthcare industry generates vast amounts of data that are crucial for improving patient outcomes and advancing medical research. However, traditional on premise solutions for data storage and analysis can become inadequate to handle the increasing volume, variety and velocity of healthcare data. The study aims to investigate the potential benefits and challenges of using cloud-based solutions for data analytics in healthcare. This paper reports about latest development and detailed role of using Artificial intelligence and capabilities of cloud Computing in health care sector/industry to foster innovative thinking, optimum wellbeing of the patient, focused medicinal support. This paper discusses various applications, algorithms and future of big data analytics with a focus on architecture, application and applicability of big data analytics using Hadoop and Cloud Computing in healthcare industry such as monitoring, prediction, performance, management etc including intensive care unit. many cloud platforms, like MMAP, are working in this field to provide a fast, reliable cost effective, efficient, and patient centric and solution to community health issues with capability of forecasting the health impact of various diseases on community for a given region or nation. Cloud computing framework, along with Artificial intelligence and Hadoop, aids healthcare management in completing analytical computations to identify logical, pertinent, and factual trends essential to strategize and enhanced readiness in event of catastrophes by facilitating data exchange among all stake holders.

Big Data are growing at an exponential rate and it becomes necessary the use of tools and technologies to manage, process and visualize them in order to extract value. In this paper a micro-service based platform is presented for the composition, deployment and execution of Big Data Analytics (BDA) application workflows in several domains and scenarios is presented. ALIDA is a result coming from previous research activities by ENGINEERING. It aims to achieve a unified platform that allows both BDA application developers and data analysts to interact with it. Developers will be able to register new BDA applications through the exposed API and/or through the web user interface. Data analysts will be able to use the BDA applications provided to create batch/stream workflows through a dashboard user interface to manipulate and subsequently visualize results from one or more sources. The platform also supports the auto-tuning of Big Data frameworks deployment properties to improve metrics for analytics application. ALIDA has been properly extended and integrated into a software solution for the analysis of large amounts of data from the avionic industries. A use case within this context is then presented.

Big data analytics (BDA) applications use advanced analysis algorithms to extract valuable insights from large, fast, and heterogeneous data sources. These complex BDA applications require software design, development, and deployment strategies to deal with volume, velocity, and variety (3vs) while sustaining expected performance levels. BDA software complexity frequently leads to delayed deployments, longer development cycles and challenging performance monitoring. This paper proposes a DevOps and Domain Specific Model (DSM) approach to design, deploy, and monitor performance Quality Scenarios (QS) in BDA applications. This approach uses high-level abstractions to describe deployment strategies and QS enabling performance monitoring. Our experimentation compares the effort of development, deployment and QS monitoring of BDA applications with two use cases of near mid-air collisions (NMAC) detection. The use cases include different performance QS, processing models, and deployment strategies. Our results show shorter (re)deployment cycles and the fulfillment of latency and deadline QS for micro-batch and batch processing.

The healthcare sector in India is witnessing phenomenal growth, such that by the year 2022, it will be a market worth trillions of INR. Increase in income levels, awareness regarding personal health, the occurrence of lifestyle diseases, better insurance policies, low-cost healthcare services, and the emergence of newer technologies like telemedicine are driving this sector to new heights. Abundant quantities of healthcare data are being accumulated each day, which is difficult to analyze using traditional statistical and analytical tools, calling for the application of Big Data Analytics in the healthcare sector. Through provision of evidence-based decision-making and actions across healthcare networks, Big Data Analytics equips the sector with the ability to analyze a wide variety of data. Big Data Analytics includes both predictive and descriptive analytics. At present, about half of the healthcare organizations have adopted an analytical approach to decision-making, while a quarter of these firms are experienced in its application. This implies the lack of understanding prevalent in healthcare sector toward the value and the managerial, economic, and strategic impact of Big Data Analytics. In this context, this chapter on “Predictive Analytics in Healthcare” discusses sources, areas of application, possible future areas, advantages and limitations of the application of predictive Big Data Analytics in healthcare.

When changes happen to big data analytics (BDA) applications in the Cloud at runtime, the affected BDA applications have to be re-deployed to accommodate the changes. Deciding the most suitable deployment is critical and complicated. Although there have been various research studies working on BDA application management, autonomic deployment decision making is still an open research issue. This paper proposes a deployment decision making solution for BDA applications in the Cloud: first, we propose a novel language, named DepPolicy, to specify runtime deployment information as policies; second, we model the deployment decision making problem as a constraint programming problem using MiniZinc; third, we propose a decision making algorithm that can make different deployment decisions for different jobs in a way that maximises overall utility while satisfying all given constraints (e.g., cost limit); fourth, we design and implement a decision making middleware, named DepWare, for BDA application deployment in the Cloud. The proposed solution is evaluated in terms of feasibility, functional correctness, performance and scalability.

The application of big data analytics in satellite network management has emerged as a transformative approach to optimize performance and enhance reliability in the satellite telecommunications industry. This paper reviews the current state of big data analytics in satellite network management, highlighting its key applications and benefits. By analyzing large volumes of data generated by satellite networks, big data analytics enables satellite telecommunications companies to gain valuable insights into network performance, identify potential issues, and take proactive measures to ensure optimal performance. One of the key applications of big data analytics in satellite network management is predictive maintenance. By analyzing historical data and equipment performance metrics, companies can predict when equipment is likely to fail and take preventive measures to avoid downtime. This not only improves network reliability but also reduces maintenance costs and improves overall operational efficiency. Another important application is network optimization. Big data analytics can analyze network traffic, weather conditions, and other factors to optimize satellite beam coverage, frequency allocation, and routing. This helps companies maximize bandwidth utilization, reduce interference, and improve service quality. The implications of big data analytics for future technology developments in satellite network management are significant. As the volume of data generated by satellite networks continues to grow, there is a need for advanced analytics tools and techniques to process and analyze this data efficiently. Future technology developments in areas such as AI, machine learning, and data visualization are expected to play a key role in enhancing the capabilities of big data analytics in satellite network management. In conclusion, the application of big data analytics in satellite network management offers significant benefits in terms of optimizing performance and enhancing reliability. By leveraging the insights provided by big data analytics, satellite telecommunications companies can improve operational efficiency, reduce costs, and deliver better services to their customers. Future technology developments will further enhance the capabilities of big data analytics, paving the way for more efficient and reliable satellite network management.

These days, Big Data (BD) and Big Data Analytics (BDA) applications have increased intensively among public and private organisations. Most organisations are aware that BDA has an enormous potential in aiding them to better understand their business environments and their customers’ needs. Nevertheless, many organisations have yet to implement BD as they are concerned that poor quality of data will have an adverse impact on establishing worthful insight, and leading to severe mistakes during their decision-making process. In addition, the different BD characteristics or traits could affect data quality. Therefore, to determine the value of data generated from BD, the collected data must be analysed for accuracy and quality. This paper aims to present findings to better understand quality requirements for BDA implementation in the public sector, specifically in Malaysia. This study explored the influence of Data Quality Dimensions (DQD) on BDA application, identified the influence of Big Data Traits (BDT) on DQD, and evaluated the integration of BDT and DQD in BDA applications using expert validation approach. A conceptual model that incorporates DQD and BDT for BDA application in the public sector was proposed as the study outcome. The conceptual model was developed based on eight BDT (variety, velocity, veracity, validity, volume, value, volatility, and variability) and four data quality categories (intrinsic, contextual, representational, and accessibility). The expert validation results showed that five out of eight BDT are important. The outcomes from this study would deliver important knowledge to the current body of studies that may prove useful for potential use in the future.

With the widespread promotion of intelligent manufacturing and the rise of cloud computing and artificial intelligence, big data analysis plays an increasingly important role in the production and operation of manufacturing enterprises. Mining and visual analysis of the massive data of manufacturing enterprises is conducive to discovering the hidden laws and causal relationships of the data, so as to extract useful information and help enterprises implement better decision-making. This paper expounds big data analysis from three aspects: data life cycle management, big data analysis types and key technologies involved, analyzes the application of big data analysis in the field of intelligent manufacturing, and proposes a big data analysis architecture for manufacturing enterprises. Finally, corresponding countermeasures and suggestions are put forward for the problems existing in big data analysis under the background of intelligent manufacturing.

With the introduction of information technology, the construction and effective operation of University intelligent campus is a stage that all universities must go through. Based on digital campus, intelligent campus comprehensively utilizes next-generation information technologies such as Internet of things, cloud computing, big data, social network and artificial intelligence to fully perceive the campus physical environment. It is an advanced form of university information development, the promotion and extension of digital campus, provides extensive IT services for teachers and students, establishes an intelligent application information system for data sharing throughout the University, and carries out campus education and scientific research. Promote the all-round innovative development of management and campus services. High school is different from traditional project construction. It is a complex system engineering integrating the next generation information technology and education management. This is mainly based on the development of Internet communication technology and big data analysis technology based on intelligent algorithms. Its construction pays more attention to the later operation, maintenance and management, which is mainly reflected in safety, applicability and sustainable development innovation. This is an information system project with a long construction cycle. We study the application of big data analysis technology based on Intelligent Algorithm in the construction of intelligent campus.KeywordsIntelligent algorithmBig dataSmart campus

Recently, along with the continuous progress of science and technology, big data analytics has started to be applied in all works of life. From the perspective of consumer behavior studies, big data analytics is a tool that can effectively predict future consumer behavior. Consumer behavior studies is a discipline that focuses on understanding the behavior and motivations of consumers during the process of purchasing products or services. In the past, researchers mainly relied on traditional research methods such as questionnaire surveys and field observations to understand consumer behavior and needs. When the Internet era arrives, the amount of data generated by consumers has exploded, providing vast opportunities for the application of big data analytics. This article provides a brief overview of the application of big data analytics in analyzing consumer behavior in social media and mobile payment, and briefly reveals the conclusions drawn from these analyses. It aims to provide a small contribution to further understanding the application of big data analytics in the field of consumer behavior studies.

Since the computer has been used in accounting work and with the current economic development, the enterprise accounting system has formed a large amount of data in the accumulation. The application of big data technology in enterprises is mainly in the R&D department, and the application rate of the finance department is not very high. In recent years, the development of big data technology and cloud computing technology has become increasingly mature, and the function of financial accounting has also gradually shifted to manage accounting. Previously under the traditional accounting method, the data that is not fully utilized by the technology can be fully explored today by relying on big data analysis. This article expounds the application of big data analysis method in modern business accounting data, and compares a series of advantages of big data analysis method with traditional analysis method. Finally, it is mentioned that in the era of highly developed Internet, there may be problems in applying big data analysis.

The history of oil and gas development and production is a history of data development. The generation of a large amount of information data has laid the cornerstone for the application of big data analysis. How to effectively mine data resources, use big data analysis to guide oilfield production practices, and provide a theoretical basis for decision-making to improve quality and efficiency is the technology core. In recent years, Huabei Oilfield has explored the application of big data analysis in oil and gas production. According to the types and characteristics of oilfield data, it has proposed and created a closed-loop big data analysis “seven-step method” system from acquisition, processing, tracking, and evaluation, preliminary designed and developed a data mining platform for oil production engineering based on Hadoop/Spark; The platform has been applied in 6 oil and gas production units and achieved remarkable social and economic benefits.

The application of big data analytics, artificial intelligence, and machine learning to solving challenging problems is ever-increasing in the oil and gas industry. These techniques have already been proven to be powerful tools that can not only improve safety and reliability but can also provide more consistent and accurate decision-making capabilities as compared to conventional methods. This paper will detail a real-world application of big data analytics and machine learning to the tubular connection make-up process, realizing significant benefits over traditional human-evaluated methods. While the focus of the paper will be on a single application, similar approaches may be beneficial to other applications and other industries, achieving similar benefits.

Cardiovascular diseases exert a wide-reaching epidemiological impact as the number one cause of death worldwide. Emerging technologies such as big data and artificial intelligence (AI) are poised to significantly change the field of cardiology. However, their applications are still emerging. We aimed to define the role of big data and AI in cardiovascular disease with a focus on research. There are zettabyte levels (1021 bytes) of big data in the US that can be directed towards healthcare research. There are applications of big data analytics already being put to use with genomics, heart failure readmissions, echocardiography, and many other areas within cardiology. We profile in this paper an extensive listing of various datasets used throughout the globe to study big data. Within cardiology, there is tremendous potential for the application of big data analytics in personalized patient care; however, they still require validation.