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.

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.

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.

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.

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.

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.

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.

Big Data Analytics Using Graph Signal Processing

The networks are fundamental to our modern world and they appear throughout science and society.Access to a massive amount of data presents a unique opportunity to the researcher’s community.As networks grow in size the complexity increases and our ability to analyze them using the current state of the art is at severe risk of failing to keep pace.Therefore,this paper initiates a discussion on graph signal processing for large-scale data analysis.We first provide a comprehensive overview of core ideas in Graph signal processing(GSP)and their connection to conventional digital signal processing(DSP).We then summarize recent developments in developing basic GSP tools,including methods for graph filtering or graph learning,graph signal,graph Fourier transform(GFT),spectrum,graph frequency,etc.Graph filtering is a basic task that allows for isolating the contribution of individual frequencies and therefore enables the removal of noise.We then consider a graph filter as a model that helps to extend the application of GSP methods to large datasets.To show the suitability and the effeteness,we first created a noisy graph signal and then applied it to the filter.After several rounds of simulation results.We see that the filtered signal appears to be smoother and is closer to the original noise-free distance-based signal.By using this example application,we thoroughly demonstrated that graph filtration is efficient for big data analytics.

Research on the Path of Higher Education Teaching Reform from the Perspective of Big Data

The advent of the digital age has pointed out the direction for the development of higher education.In this paper,the undergraduate course“Science of Interrogation”is taken as the research subject,the challenges of big data teaching reform in universities are analyzed systematically,and concrete measures,such as adhering to the concept of teaching reform under the condition of big data,establishing professional teaching teams,improving the curriculum construction level,emphasizing on being student-centered,and optimizing the reform of the curriculum evaluation system,are proposed to address these challenges.

Analysis and Enlightenment of Big Data Platform for Adverse Drug Reaction Supervision in China and the United States

Objective To introduce the relevant big data platforms of FDA regulatory sciences and to provide reference for the construction of big data platform for China’s regulatory science under the“14th five-year plan”to deepen the reform of medical and health system.Methods A comparative analysis was made on China’s big data for regulatory science after studying the development process,operation mode,practical significance and characteristics of the big data platform for FDA regulatory science,which would help China to establish a perfect big database.Results and Conclusion The construction of big data platform for China’s regulatory science is not comprehensive compared with that in the United States.It is necessary to build data platforms in line with China’s national conditions through efforts in law,talents,standards,and other aspects.

近十年国际大数据领域研究进展:基于Web of Science的实证分析

为了揭示近十年来大数据领域的研究趋势与发展特征,为相关研究提供借鉴和参考。本文使用文献计量学和Vosviewer可视化分析软件,以Web of Science数据库所收集的2010年—2020期间所刊载的研究性论文为样本数据来源,从总体特征上分析大数据领域的论文发表年度、主要国家及地区、研究机构和作者分布。根据所得关键词与聚类分析,绘制相关知识图谱,以高被引文献为基础,进行作者、机构合作网络关系分析。文献计量结果显示,总体研究规模一直呈现稳步增长趋势,美国在大数据研究领域仍然占据重要地位,中国在近十年来的研究成果同样引人瞩目,但是还存在更进一步的空间;知识图谱归纳出大数据领域相关研究的六大热点领域。分析发现,大数据在医疗健康、高等教育行业的应用正日渐广泛起来;与社交媒体相关的研究也正在不断升温;个人信息保护是大数据时代背景下急需解决的问题。

2012–2014 China's Earth Observation and Earth Science Development

Human beings are now facing global and regional sustainable development challenges.In China, Earth observation data play a fundamental role in Earth system science research. The support given by Earth observation data is required by many studies, including those on Earth's limited natural resources, the rapid development of economic and social needs, global change, extreme events, food security, water resources, sustainable economic and urban development, and emergency response. Application operation systems in many ministries and departments in China have entered a stage of sustainable development, and the State Key Project of High-Resolution Earth Observation Systems has been progressing since 2006. Earth observation technology in China has entered a period of rapid development.

Understanding Big Data for Industrial Innovation and Design:The Missing Information Systems Perspective

This paper identifies a need to complement the current rich technical and mathematical research agenda on big data with a more information systems and information science strand, which focuses on the business value of big data. An agenda of research for information systems would explore motives for using big data in real organizational contexts, and consider proposed benefits, such as increased effectiveness and efficiency, production of high-quality products/services, creation of added business value, and stimulation of innovation and design. Impacts of such research on the academic community, the industrial and business world, and policy-makers are discussed.

Call for Papers Special Issue on"Big Data in Brain Science"

The journal Genomics, Proteomics ＆ Bioinformatics （GPB） is now inviting submissions for a special issue （to be published in the summer of 2018） on the topic of＂Big data in brain science＂.

Dirty Data between Errors and Their Handling—A Firsthand Experience in Solving Dirty Data from Within

Managing large amounts of data is becoming part of everyday life in most organizations. Handling, analyzing, searching, and making predictions from big data is becoming the norm for many organizations of many interests. Big data provides the foundations for more benefits and higher values to be extracted from big data. As big data comes with countless benefits, it also comes with many challenges to fulfilling its expectations. Some of those problems haunting big data banks are being termed dirty data. This paper focuses on dirty data while working on an organization’s natural live information system. The author was responsible for studying and analyzing a faltering information system and planning and carrying out the required solutions and fixes. The importance of the work carried out lies in the high level of dirty data observed in the system. Therefore, this paper is based on the part of dirty data—the paper focuses on how the team suffered from dirty data and how it was dealt with.

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.

我国遗产型景区“平台(platform)+数据(data)+标准(standard)”管服体系构建初探

探索构建我国遗产型景区“平台(platform)+数据(data)+标准(standard)”管服体系是深入贯彻落实习近平新时代中国特色社会主义思想和党的二十大精神,推进科技治理体系和遗产型景区管理能力现代化发展的重要实践。随着文化旅游产业蓬勃发展,针对国家和地方遗产型景区管理现状,构建符合我国遗产型景区科技创新规律、适应遗产型景区新需求的科技创新管理体系,成为国家、省、市、区县中各类遗产型景区优化管理资源的重要途径。本文通过论述大数据、云计算、人工智能等相关技术,设计“横向到边、纵向到底”的总体建设思路,旨在构建统一标准、信息统筹、资源共享、监测预警、管理优化、决策支持等功能于一体的系统运行体系,为政府推动遗产型景区管理创新制定有效措施提出建议。

数智技术时代的中国档案学学科发展

数智技术,直接导致我国千行百业的原始数据形成,其管理正发生前所未有的变化。为适应从人人相连走向万物智联的大数据安全管理,需要对我国高等教育相关学科专业进行系统研究。梳理数字技术对我国组织的业务活动及其原始记录形成和管理的作用影响之后,提出传统的“中国档案学”学科将演进为“档案大数据工程”学科。并且指出,档案大数据工程学科高等教育包括“管理文秘”“数据资源管理”“大数据技术”等专业,尝试提出以上几个专业建设的发展方向。

大数据时代地理信息科学专业创新教育改革探索与实践

大数据为新时代高校专业创新教育带来了全新机遇与挑战。本文以地理信息科学专业为例,系统剖析并总结了当前我国高校创新教育现状及其面临的主要挑战,对大数据时代地理信息科学专业创新教育改革进行了探索与实践,具体围绕创新理念渗透、创新意识提升、创新思维培养与创新能力考核,提出了高校创新教育改革新举措,可为全面构建高校创新教育体系提供参考。