A Review of the Status and Development Strategies of Computer Science and Technology Under the Background of Big Data

This article discusses the current status and development strategies of computer science and technology in the context of big data.Firstly,it explains the relationship between big data and computer science and technology,focusing on analyzing the current application status of computer science and technology in big data,including data storage,data processing,and data analysis.Then,it proposes development strategies for big data processing.Computer science and technology play a vital role in big data processing by providing strong technical support.

Standard Framework Construction of Technology and Equipment for Big Data in Crop Phenomics

Crop phenomics has rapidly progressed in recent years due to the growing need for crop functional geno-mics,digital breeding,and smart cultivation.Despite this advancement,the lack of standards for the cre-ation and usage of crop phenomics technology and equipment has become a bottleneck,limiting the industry’s high-quality development.This paper begins with an overview of the crop phenotyping indus-try and presents an industrial mapping of technology and equipment for big data in crop phenomics.It analyzes the necessity and current state of constructing a standard framework for crop phenotyping.Furthermore,this paper proposes the intended organizational structure and goals of the standard frame-work.It details the essentials of the standard framework in the research and development of hardware and equipment,data acquisition,and the storage and management of crop phenotyping data.Finally,it discusses promoting the construction and evaluation of the standard framework,aiming to provide ideas for developing a high-quality standard framework for crop phenotyping.

Construction and Application Exploration of Smart Agriculture Based on Big Data Technology

Big data finds extensive application and many fields.It brings new opportunities for the development of agriculture.Using big data technology to promote the development of smart agriculture can greatly improve the effect of agricultural planting,reduce the input of manpower and material resources,and lay a solid foundation for the realization of agricultural modernization.In this regard,this paper briefly analyzes the construction and application of smart agriculture based on big data technology,hoping to provide some valuable insights for readers.

Research on the Application of Big Data and Cloud Computing Technology in Smart Campus

The current education field is experiencing an innovation driven by big data and cloud technologies,and these advanced technologies play a central role in the construction of smart campuses.Big data technology has a wide range of applications in student learning behavior analysis,teaching resource management,campus safety monitoring,and decision support,which improves the quality of education and management efficiency.Cloud computing technology supports the integration,distribution,and optimal use of educational resources through cloud resource sharing,virtual classrooms,intelligent campus management systems,and Infrastructure-as-a-Service(IaaS)models,which reduce costs and increase flexibility.This paper comprehensively discusses the practical application of big data and cloud computing technologies in smart campuses,showing how these technologies can contribute to the development of smart campuses,and laying the foundation for the future innovation of education models.

Application and Research of Big Data Technology in Intelligent Ward

With the rapid development of information science and technology and the progress of the health service industry,the integration of medical services and new technologies such as big data technology has become more in-depth,and the construction of smart hospitals and smart wards has received more and more attention and application.This article conducts in-depth research and analysis on the application and exploration of big data technology in smart wards and explores the role of big data technology in improving the quality,efficiency,and cost of medical services in smart wards,as well as the application prospects and challenges of big data technology in patient monitoring,disease prevention,and diagnosis and treatment processes in smart wards.The focus is on analyzing and studying the use of big data technology to assist in the construction of smart neonatal surgery wards.This article aims to provide useful references and lessons for the construction and development of smart wards.

Study on Growth Laws of Marine Science and Technology Talents Based on the Perspective of Big Data

Based on the perspective of big data,the growth characteristics of marine science and technology talents were analyzed,and the growth of marine science and technology talents was divided into five periods:study period,adaptation period,growth period,promotion period and stability period.Moreover,some suggestions for the training of marine science and technology talents were proposed from the aspects of students,families,schools and society.

Research and Application Progress of Agricultural Big Data Technology and Resources

With the development of Internet of things, cloud computing, mobile Inter- net, the scale of the data shows an alarming growth trend. Agricultural information is an important part of modern agricultural construction, and the development of a- gricultural industry is becoming more and more deeply with the application of infor- mation technology. This paper reviewed the concept and characteristic of big data, development history of big data at home and abroad, and emphatically expounded the connotation of agricultural big data, development status of agricultural big data at home and abroad, as well as the applications of agricultural big data technology, agriculture big data resources and agricultural big data in various fields.

Research on the Construction of Application-Oriented Undergraduate Data Science and Big Data Technology Courses

In order to conduct research and analysis on the construction of application-oriented undergraduate data science and big data technology courses,the professional development characteristics of universities and enterprises should be taken into consideration,the development trend of the big data industry should be scrutinized,and professional application-oriented talents should be cultivated in line with job requirements.This paper expounds the demand for capacity-building professional development in application-oriented undergraduate data science and big data technology courses,conducts research and analysis on the current situation of professional development,and puts forward strategies in hope to provide reference for capacity-building professional development.

Research on the Teaching Reform of “Big Data Analysis and Visualization” Course for College Students

Under the background of the big data era,the education of big data majors is undergoing a profound teaching reform and innovation.With the increasing role of big data technology in analysis and decision-making,updating and expanding the teaching content of big data majors has become particularly important.In the era of big data,modern enterprises have put forward new and higher demands for big data talents,which not only include traditional data analysis skills but also knowledge of data visualization and information technology.To address these challenges,big data education needs to reform and innovate in the development and utilization of teaching content,methods,and resources.This paper proposes teaching models and reform methods for big data majors and analyzes corresponding teaching reforms and innovations to meet the requirements of the new development of big data majors.The traditional classroom teaching method is no longer sufficient to meet the learning needs of students,and more dynamic and interactive teaching methods,such as case studies,flipped classrooms,and project-based learning,are becoming increasingly essential.These innovative teaching methods can more effectively cultivate students’practical operation skills and independent thinking while allowing them to better learn advanced knowledge in a real big-data environment.In addition,the paper also discusses the construction of big data processing and analysis platforms,as well as innovative teaching management and evaluation systems to improve teaching quality.

Application Technologies and Challenges of Big Data Analytics in Anti-Money Laundering and Financial Fraud Detection

As financial criminal methods become increasingly sophisticated, traditional anti-money laundering and fraud detection approaches face significant challenges. This study focuses on the application technologies and challenges of big data analytics in anti-money laundering and financial fraud detection. The research begins by outlining the evolutionary trends of financial crimes and highlighting the new characteristics of the big data era. Subsequently, it systematically analyzes the application of big data analytics technologies in this field, including machine learning, network analysis, and real-time stream processing. Through case studies, the research demonstrates how these technologies enhance the accuracy and efficiency of anomalous transaction detection. However, the study also identifies challenges faced by big data analytics, such as data quality issues, algorithmic bias, and privacy protection concerns. To address these challenges, the research proposes solutions from both technological and managerial perspectives, including the application of privacy-preserving technologies like federated learning. Finally, the study discusses the development prospects of Regulatory Technology (RegTech), emphasizing the importance of synergy between technological innovation and regulatory policies. This research provides guidance for financial institutions and regulatory bodies in optimizing their anti-money laundering and fraud detection strategies.

Analysis of the Impact of Big Data Technology on Environmental Pollution Control Audit

As China strives towards the second centenary goal,increasing attention is being paid to environmental pollution and other related issues.Concurrently,with the rapid development of big data technology,many big data solutions have been applied to environmental pollution control audits,exerting a significant impact.This paper presents the current situation of environmental pollution audits,summarizing the application of big data from the perspectives of both domestic and international research.In terms of data collection and data analysis for environmental pollution audits,cloud platform technology,and visualization technology are selected based on multiple data sources.The impact in the field of environmental pollution control audits is further analyzed.It is found that the environmental pollution audit cloud platform is not yet perfect,the technical skills of audit personnel are insufficient,and some technologies are not mature.Relevant suggestions are put forward to provide a reference for the future development of big data technology and its integration with environmental pollution control audits.

Big Data and Data Science:Opportunities and Challenges of iSchools

Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and future jobs, and thus student careers. At the heart of this digital transformation is data science, the discipline that makes sense of big data. With many rapidly emerging digital challenges ahead of us, this article discusses perspectives on iSchools＇ opportunities and suggestions in data science education. We argue that iSchools should empower their students with ＂information computing＂ disciplines, which we define as the ability to solve problems and create values, information, and knowledge using tools in application domains. As specific approaches to enforcing information computing disciplines in data science education, we suggest the three foci of user-based, tool-based, and application- based. These three loci will serve to differentiate the data science education of iSchools from that of computer science or business schools. We present a layered Data Science Education Framework （DSEF） with building blocks that include the three pillars of data science （people, technology, and data）, computational thinking, data-driven paradigms, and data science lifecycles. Data science courses built on the top of this framework should thus be executed with user-based, tool-based, and application-based approaches. This framework will help our students think about data science problems from the big picture perspective and foster appropriate problem-solving skills in conjunction with broad perspectives of data science lifecycles. We hope the DSEF discussed in this article will help fellow iSchools in their design of new data science curricula.

Big Metadata,Smart Metadata,and Metadata Capital:Toward Greater Synergy Between Data Science and Metadata

Purpose： The purpose of the paper is to provide a framework for addressing the disconnect between metadata and data science. Data science cannot progress without metadata research.This paper takes steps toward advancing the synergy between metadata and data science, and identifies pathways for developing a more cohesive metadata research agenda in data science. Design/methodology/approach： This paper identifies factors that challenge metadata research in the digital ecosystem, defines metadata and data science, and presents the concepts big metadata, smart metadata, and metadata capital as part of a metadata lingua franca connecting to data science. Findings： The ＂utilitarian nature＂ and ＂historical and traditional views＂ of metadata are identified as two intersecting factors that have inhibited metadata research. Big metadata, smart metadata, and metadata capital are presented as part ofa metadata linguafranca to help frame research in the data science research space. Research limitations： There are additional, intersecting factors to consider that likely inhibit metadata research, and other significant metadata concepts to explore. Practical implications： The immediate contribution of this work is that it may elicit response, critique, revision, or, more significantly, motivate research. The work presented can encourage more researchers to consider the significance of metadata as a research worthy topic within data science and the larger digital ecosystem. Originality/value： Although metadata research has not kept pace with other data science topics, there is little attention directed to this problem. This is surprising, given that metadata is essential for data science endeavors. This examination synthesizes original and prior scholarship to provide new grounding for metadata research in data science.

The Impact of Online Networks and Big Data in Life Sciences

Advances in Information Technology (IT) have enhanced our ability to gather, collect and analyze information from individuals and specific groups of people online. The emergence of online networks has facilitated connections between individuals by leveraging data exchange in a variety of fields. Online networking in life sciences transforms data collection into actionable information that will improve individual and population health, deliver effective therapies and, consequently, reduce the cost of healthcare. These novel tools might also have a direct impact in personalized medicine programs, since the adoption of new products by health care professionals in life sciences and peer-to-peer learning could be improved using social networks and big data analytics. However, one of the main concerns of information exchange online is data privacy. In this article, we will review how online networks and big data analytics are impacting the life sciences sector.

Climatic and Environmental Characteristics of Beijing Based on Rapid Processing Technology of Urban Meteorological Big Data

The meteorological big data in Beijing area are typical multi-dimensional big data containing spatiotemporal characteristics,which have important research value for researches related to urban human settlement environment.With the help of computer programming and software processing,big data crawling,integration,extraction and multi-dimensional information fusion can be realized quickly and effectively,so as to obtain the data set needed for research and realize the target of visualization.Through big data analysis of wind environment,thermal environment and total atmospheric suspended particulate pollutants in Beijing area,it was found that the average wind speed in Beijing area decreased signifi cantly in recent 40 years,while the surface temperature increased signifi cantly;urban heat island effect was signifi cant,and the phenomenon of atmospheric suspended particulate pollution was relatively common.The spatial distribution of the three climatic and environmental data was not balanced and had signifi cant regularity and correlation.Improving urban ventilation corridors and improving urban ventilation capacity is a feasible way to improve urban heat island effect and reduce urban climate issues such as atmospheric particulate pollution.

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.

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.

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.

Big Data Analytics in Telecommunications: Literature Review and Architecture Recommendations

This paper focuses on facilitating state-of-the-art applications of big data analytics(BDA) architectures and infrastructures to telecommunications(telecom) industrial sector.Telecom companies are dealing with terabytes to petabytes of data on a daily basis. Io T applications in telecom are further contributing to this data deluge. Recent advances in BDA have exposed new opportunities to get actionable insights from telecom big data. These benefits and the fast-changing BDA technology landscape make it important to investigate existing BDA applications to telecom sector. For this, we initially determine published research on BDA applications to telecom through a systematic literature review through which we filter 38 articles and categorize them in frameworks, use cases, literature reviews, white papers and experimental validations. We also discuss the benefits and challenges mentioned in these articles. We find that experiments are all proof of concepts(POC) on a severely limited BDA technology stack(as compared to the available technology stack), i.e.,we did not find any work focusing on full-fledged BDA implementation in an operational telecom environment. To facilitate these applications at research-level, we propose a state-of-the-art lambda architecture for BDA pipeline implementation(called Lambda Tel) based completely on open source BDA technologies and the standard Python language, along with relevant guidelines.We discovered only one research paper which presented a relatively-limited lambda architecture using the proprietary AWS cloud infrastructure. We believe Lambda Tel presents a clear roadmap for telecom industry practitioners to implement and enhance BDA applications in their enterprises.

Challenges and Solutions of Information Security Issues in the Age of Big Data

Big data has been taken as a Chinese national strategy in order to satisfy the developments of the social and economic requirements and the development of new information technology. The prosperity of big data brings not only convenience to people's daily life and more opportunities to enterprises, but more challenges with information security as well. This paper has a research on new types and features of information security issues in the age of big data, and puts forward the solutions for the above issues: build up the big data security management platform, set up the establishment of information security system and implement relevant laws and regulations.