Big Data 4.0: The Era of Big Intelligence

Big data has had significant impacts on our lives,economies,academia and industries over the past decade.The current equations are:What is the future of big data?What era do we live in?This article addresses these questions by looking at meta as an operation and argues that we are living in the era of big intelligence through analyzing from meta(big data)to big intelligence.More specifically,this article will analyze big data from an evolutionary perspective.The article overviews data,information,knowledge,and intelligence(DIKI)and reveals their relationships.After analyzing meta as an operation,this article explores Meta(DIKE)and its relationship.It reveals 5 Bigs consisting of big data,big information,big knowledge,big intelligence and big analytics.Applying meta on 5 Bigs,this article infers that 4 Big Data 4.0=meta(big data)=big intelligence.This article analyzes how intelligent big analytics support big intelligence.The proposed approach in this research might facilitate the research and development of big data,big data analytics,business intelligence,artificial intelligence,and data science.

Sports Prediction Model through Cloud Computing and Big Data Based on Artificial Intelligence Method

This article delves into the intricate relationship between big data, cloud computing, and artificial intelligence, shedding light on their fundamental attributes and interdependence. It explores the seamless amalgamation of AI methodologies within cloud computing and big data analytics, encompassing the development of a cloud computing framework built on the robust foundation of the Hadoop platform, enriched by AI learning algorithms. Additionally, it examines the creation of a predictive model empowered by tailored artificial intelligence techniques. Rigorous simulations are conducted to extract valuable insights, facilitating method evaluation and performance assessment, all within the dynamic Hadoop environment, thereby reaffirming the precision of the proposed approach. The results and analysis section reveals compelling findings derived from comprehensive simulations within the Hadoop environment. These outcomes demonstrate the efficacy of the Sport AI Model (SAIM) framework in enhancing the accuracy of sports-related outcome predictions. Through meticulous mathematical analyses and performance assessments, integrating AI with big data emerges as a powerful tool for optimizing decision-making in sports. The discussion section extends the implications of these results, highlighting the potential for SAIM to revolutionize sports forecasting, strategic planning, and performance optimization for players and coaches. The combination of big data, cloud computing, and AI offers a promising avenue for future advancements in sports analytics. This research underscores the synergy between these technologies and paves the way for innovative approaches to sports-related decision-making and performance enhancement.

The Path to Reshaping the Structure and Chain System of the Publishing Industry in the Era of Big Data

Today,we are living in the era of“big data”where massive amounts of data are used for quantitative decisions and communication management.With the continuous penetration of big data-based intelligent technology in all fields of human life,the enormous commercial value inherent in the data industry has become a crucial force that drives the aggregation of new industries.For the publishing industry,the introduction of big data and relevant intelligent technologies,such as data intelligence analysis and scenario services,into the structure and value system of the publishing industry,has become an effective path to expanding and reshaping the demand space of publishing products,content decisions,workflow chain,and marketing direction.In the integration and reconstruction of big data,cloud computing,artificial intelligence,and other related technologies,it is expected that a generalized publishing industry pattern dominated by virtual interaction will be formed in the future.

Big data challenge for monitoring quality in higher education institutions using business intelligence dashboards

As big data becomes an apparent challenge to handle when building a business intelligence(BI)system,there is a motivation to handle this challenging issue in higher education institutions(HEIs).Monitoring quality in HEIs encompasses handling huge amounts of data coming from different sources.This paper reviews big data and analyses the cases from the literature regarding quality assurance(QA)in HEIs.It also outlines a framework that can address the big data challenge in HEIs to handle QA monitoring using BI dashboards and a prototype dashboard is presented in this paper.The dashboard was developed using a utilisation tool to monitor QA in HEIs to provide visual representations of big data.The prototype dashboard enables stakeholders to monitor compliance with QA standards while addressing the big data challenge associated with the substantial volume of data managed by HEIs’QA systems.This paper also outlines how the developed system integrates big data from social media into the monitoring dashboard.

Particle Swarm Optimization-Based Hyperparameters Tuning of Machine Learning Models for Big COVID-19 Data Analysis

Analyzing big data, especially medical data, helps to provide good health care to patients and face the risks of death. The COVID-19 pandemic has had a significant impact on public health worldwide, emphasizing the need for effective risk prediction models. Machine learning (ML) techniques have shown promise in analyzing complex data patterns and predicting disease outcomes. The accuracy of these techniques is greatly affected by changing their parameters. Hyperparameter optimization plays a crucial role in improving model performance. In this work, the Particle Swarm Optimization (PSO) algorithm was used to effectively search the hyperparameter space and improve the predictive power of the machine learning models by identifying the optimal hyperparameters that can provide the highest accuracy. A dataset with a variety of clinical and epidemiological characteristics linked to COVID-19 cases was used in this study. Various machine learning models, including Random Forests, Decision Trees, Support Vector Machines, and Neural Networks, were utilized to capture the complex relationships present in the data. To evaluate the predictive performance of the models, the accuracy metric was employed. The experimental findings showed that the suggested method of estimating COVID-19 risk is effective. When compared to baseline models, the optimized machine learning models performed better and produced better results.

Key issues and progress of industrial big data-based intelligent blast furnace ironmaking technology

Blast furnace (BF) ironmaking is the most typical “black box” process, and its complexity and uncertainty bring forth great challenges for furnace condition judgment and BF operation. Rich data resources for BF ironmaking are available, and the rapid development of data science and intelligent technology will provide an effective means to solve the uncertainty problem in the BF ironmaking process. This work focused on the application of artificial intelligence technology in BF ironmaking. The current intelligent BF ironmaking technology was summarized and analyzed from five aspects. These aspects include BF data management, the analyses of time delay and correlation, the prediction of BF key variables, the evaluation of BF status, and the multi-objective intelligent optimization of BF operations. Solutions and suggestions were offered for the problems in the current progress, and some outlooks for future prospects and technological breakthroughs were added. To effectively improve the BF data quality, we comprehensively considered the data problems and the characteristics of algorithms and selected the data processing method scientifically. For analyzing important BF characteristics, the effect of the delay was eliminated to ensure an accurate logical relationship between the BF parameters and economic indicators. As for BF parameter prediction and BF status evaluation,a BF intelligence model that integrates data information and process mechanism was built to effectively achieve the accurate prediction of BF key indexes and the scientific evaluation of BF status. During the optimization of BF parameters, low risk, low cost, and high return were used as the optimization criteria, and while pursuing the optimization effect, the feasibility and site operation cost were considered comprehensively.This work will help increase the process operator’s overall awareness and understanding of intelligent BF technology. Additionally, combining big data technology with the process will improve the practicality of data models in actual production and promote the application of intelligent technology in BF ironmaking.

Systematic Survey on Big Data Analytics and Artificial Intelligence for COVID-19 Containment

Artificial Intelligence(AI)has gained popularity for the containment of COVID-19 pandemic applications.Several AI techniques provide efficient mechanisms for handling pandemic situations.AI methods,protocols,data sets,and various validation mechanisms empower the users towards proper decision-making and procedures to handle the situation.Despite so many tools,there still exist conditions in which AI must go a long way.To increase the adaptability and potential of these techniques,a combination of AI and Bigdata is currently gaining popularity.This paper surveys and analyzes the methods within the various computational paradigms used by different researchers and national governments,such as China and South Korea,to fight against this pandemic.The process of vaccine development requires multiple medical experiments.This process requires analyzing datasets from different parts of the world.Deep learning and the Internet of Things(IoT)revolutionized the field of disease diagnosis and disease prediction.The accurate observations from different datasets across the world empowered the process of drug development and drug repurposing.To overcome the issues generated by the pandemic,using such sophisticated computing paradigms such as AI,Machine Learning(ML),deep learning,Robotics and Bigdata is essential.

From Big Data to Intelligent Tutoring: Exploring the Application of Big Data Technology in the Ideological and Political Education for College Counselors

This paper explores the application of big data technology in ideological and political education for college counselors.Big data-driven personalized counseling for students,social network analysis,and public opinion monitoring can optimize the efficiency of counselor work.However,there are still challenges concerning data privacy protection,technical support,and training.By scientifically evaluating and addressing these challenges,big data technology is expected to enhance the effectiveness of college counselors and improve the quality of ideological and political education.In the future,with technological advancements,big data technology will become an essential tool for higher education management.

Urban Big Data and the Development of City Intelligence

This study provides a definition for urban big data while exploring its features and applications of Chi- na＇s city intelligence. The differences between city intelligence in China and the ＂smart city＂ concept in other countries are compared to highlight and contrast the unique definition and model for China＇s city intelligence in this paper. Furthermore, this paper examines the role of urban big data in city intel- ligence by showing that it not only serves as the cornerstone of this trend as it also plays a core role in the diffusion of city intelligence technology and serves as an inexhaustible resource for the sustained development of city intelligence. This study also points out the challenges of shaping and developing of China＇s urban big data. Considering the supporting and core role that urban big data plays in city intel- ligence, the study then expounds on the key points of urban big data, including infrastructure support, urban governance, public services, and economic and industrial development. Finally, this study points out that the utility of city intelligence as an ideal policy tool for advancing the goals of China＇s urban de- velopment. In conclusion, it is imperative that China make full use of its unique advantages-including using the nation＇s current state of development and resources, geographical advantages, and good hu- man relations-in subjective and objective conditions to promote the development of city intelligence through the proper application of urban big data.

Research on the Application of Big Data and Artificial Intelligence Technology in Computer Network

With the continuous development of social economy,science and technology are also in continuous progress,relying on the Internet technology of big data era has come in an all-round way.On the basis of the development of cloud computing and Internet technology,artificial intelligence technology has emerged as the times require.It also has more advantages.Applying it to computer network technology can effectively improve the data processing efficiency and quality of computer network technology,and improve the convenience for people’s life and production.This paper studies and analyzes the practical application requirements of computer network,and discusses the application characteristics and timeliness of artificial intelligence technology.

Theory and practice of urban spatial planning in big data era- Taking Hunan as an example

In recent years, the rapid development of information and communication technologies accelerates the arrival of the big data era. But it still needs to be explored further for what large data brought on the impact of urban planning and how to respond to the implementation and formation of urban planning to the end. We will research from two aspects of the practice study and formation of urban planning in Hunan to discuss based on brief review of research about urban space big data era.

International Papers Contribution on Artificial Intelligence Promotes the Application and Development of Big Data in the Petroleum Industry

Artificial intelligence is a new technological science that researches and develops theories,methods,technologies and application systems for simulating,extending and expanding human intelligence.It simulates certain human thought processes and intelligent behaviors(such as learning,reasoning,thinking,planning,etc.),and produces a new type of intelligent machine that can respond in a similar way to human intelligence.In the past 30 years,it has achieved rapid development in various industries and related disciplines such as manufacturing,medical care,finance,and transportation.

Applications and Challenges of Smart Technologies in Supply Chain Management

The advent of intelligent technologies in supply chain management(SCM)through the use of artificial intelligence(AI),the Internet of Things(IoT),blockchain,big data analytics,and others is a game changer.These improvements increase clarity,effectiveness,and flexibility,allowing companies to quickly deal with market changes and interruptions.Nevertheless,adopting such technologies comes with difficulties,such as information security,compatibility,exorbitant prices,and employee opposition.The applications of intelligent technologies for SCM are examined in this article,as well as the importance of their implementation and solutions that provide a competitive advantage for businesses.

Design of a Student Recommendation Platform Based on Learning Behavior and Habit Training

This study innovatively built an intelligent analysis platform for learning behavior,which deeply integrated the cutting-edge technology of big data and Artificial Intelligence(AI),\mined and analyzed students’learning data,and realized the personalized customization of learning resources and the accurate matching of intelligent learning partners.With the help of advanced algorithms and multi-dimensional data fusion strategies,the platform not only promotes positive interaction and collaboration in the learning environment but also provides teachers with comprehensive and in-depth students’learning portraits,which provides solid support for the implementation of precision education and the personalized adjustment of teaching strategies.In this study,a recommender system based on user similarity evaluation and a collaborative filtering mechanism is carefully designed,and its technical architecture and implementation process are described in detail.

Big data in healthcare:Conceptual network structure,key challenges and opportunities

Big data is a concept that deals with large or complex data sets by using data analysis tools(e.g.,data mining,machine learning)to analyze information extracted from several sources systematically.Big data has attracted wide attention from academia,for example,in supporting patients and health professionals by improving the accuracy of decision-making,diagnosis and disease prediction.This research aimed to perform a Bibliometric Performance and Network Analysis(BPNA)supported by a Scoping Review(SR)to depict the strategic themes,thematic evolution structure,main challenges and opportunities related to the concept of big data applied in the healthcare sector.With this goal in mind,4857 documents from the Web of Science covering the period between 2009 to June 2020 were analyzed with the support of SciMAT software.The bibliometric performance showed the number of publications and citations over time,scientific productivity and the geographic distribution of publications and research fields.The strategic diagram yielded 20 clusters and their relative importance in terms of centrality and density.The thematic evolution structure presented the most important themes and how it changes over time.Lastly,we presented the main challenges and future opportunities of big data in healthcare.

Data,Analytics,and Intelligence

We are living in an age of big data,analytics,and artificial intelligence(AI).After reviewing a dozen different books on big data,data analytics,data science,AI,and business intelligence(BI),there are the current questions:(1)What are the relationships between data,analytics,and intelligence?(2)What are the relationships between big data and big data analytics?(3)What is the relationship between BI and data analytics?This article first discusses the heuristics of the Greek philosopher Plato and French mathematician Descartes and how to reshape the world.Then it addresses the above questions based on a Boolean structure,which destructs big data,data analytics,data science,and AI into data,analytics,and intelligence as the Boolean atoms.Data,analytics,and intelligence are reorganized and reassembled,based on the Boolean structure,to data analytics,analytics intelligence,data intelligence,and data analytics intelligence.The research will analyse each of them after examining the system intelligence.The proposed approach in this research might facilitate the research and development of big data,data analytics,AI,and data science.

“Deep-time Digital Basin” Based on Big Data and Artificial Intelligence

1 Introduction Information technology has been playing an ever-increasing role in geoscience.Sphisicated database platforms are essential for geological data storage,analysis and exchange of Big Data(Feblowitz,2013;Zhang et al.,2016;Teng et al.,2016;Tian and Li,2018).The United States has built an information-sharing platform for state-owned scientific data as a national strategy.

Smart Society and Artificial Intelligence:Big Data Scheduling and the Global Standard Method Applied to Smart Maintenance

The implementation of artificial intelligence(AI)in a smart society,in which the analysis of human habits is mandatory,requires automated data scheduling and analysis using smart applications,a smart infrastructure,smart systems,and a smart network.In this context,which is characterized by a large gap between training and operative processes,a dedicated method is required to manage and extract the massive amount of data and the related information mining.The method presented in this work aims to reduce this gap with near-zero-failure advanced diagnostics(AD)for smart management,which is exploitable in any context of Society 5.0,thus reducing the risk factors at all management levels and ensuring quality and sustainability.We have also developed innovative applications for a humancentered management system to support scheduling in the maintenance of operative processes,for reducing training costs,for improving production yield,and for creating a human–machine cyberspace for smart infrastructure design.The results obtained in 12 international companies demonstrate a possible global standardization of operative processes,leading to the design of a near-zero-failure intelligent system that is able to learn and upgrade itself.Our new method provides guidance for selecting the new generation of intelligent manufacturing and smart systems in order to optimize human–machine interactions,with the related smart maintenance and education.

Artificial Intelligence and the Future of Education： Big Promises -Bigger Challenges

The history of educational technology in the last 50 years contains few instances of dramatic improvements in learning based on the adoption of a particular technology.An example involving artificial intelligence occurred in the 1990s with the development of intelligent tutoring systems( ITSs). What happened with ITSs was that their success was limited to well-defined and relatively simple declarative and procedural learning tasks(e. g.,learning how to write a recursive function in LISP; doing multi-column addition),and improvements that were observed tended to be more limited than promised(e. g.,one standard deviation improvement at best rather than the promised standard deviation improvement).Still,there was some progress in terms of how to conceptualize learning. A seldom documented limitation was the notion of only viewing learning from only content and cognitive perspectives( i. e.,in terms of memory limitations,prior knowledge,bug libraries,learning hierarchies and sequences etc.). Little attention was paid to education conceived more broadly than developing specific cognitive skills with highly constrained problems. New technologies offer the potential to create dynamic and multi-dimensional models of a particular learner,and to track large data sets of learning activities,resources,interventions,and outcomes over a great many learners. Using those data to personalize learning for a particular learner developing knowledge,competence and understanding in a specific domain of inquiry is finally a real possibility. While the potential to make significant progress is clearly possible,the reality is less not so promising. There are many as yet unmet challenging some of which will be mentioned in this paper. A persistent worry is that educational technologists and computer scientists will again promise too much,too soon at too little cost and with too little effort and attention to the realities in schools and universities.

The Role of Big Data and Machine Learning in the Integration and Implementation of Historical,Current,and Continuously Gathered Earth Data

We are faced with more challenges than ever with respect to the amount,variety,and sheer volume of earth data.Not only do we have data of many different ages and origins,we also have a wide variety of data,much of which has just been recently digitized,or is in a format that can cannot easily be integrated into algorithms,georeferenced data.