Big Data and Classical Literature Research Methods

2019 ◽  
Vol 78 ◽  
pp. 7-39
Author(s):  
Baro Kim ◽  
Wookyu Kang
Keyword(s):  
Big Data ◽  
Research Methods ◽  
Classical Literature ◽  
Literature Research

scholarly journals Study on the comprehensive computational thinking transformation of urban planning discipline in the era of big data

2020 ◽  
Author(s):  
Haixuan Zhu ◽  
◽  
Xiaoyu Jia ◽  
Pengluo Que ◽  
Xiaoyu Hou ◽  
...  
Keyword(s):  
Big Data ◽  
Urban Planning ◽  
Computer Science ◽  
Research Methods ◽  
Data Storage ◽  
Computational Thinking ◽  
Rural Planning ◽  
Human Beings ◽  
New Paradigm ◽  
Thinking Mode

In the era of big data, with the development of computer technology, especially the comprehensive popularization of mobile terminal device and the gradual construction of the Internet of Things, the urban physical environment and social environment have been comprehensively digitized and quantified. Computational thinking mode has gradually become a new thinking mode for human beings to recognize and govern urban complex system. Meanwhile computational urban science has become the main discipline development aspect of modern urban planning. Computational thinking is the thinking of computer science using algorithms based on time complexity and space complexity, which provides a new paradigm for the construction of index system, data collection, data storage, data analysis, pattern recognition, dynamic governance in the process of scientific planning and urban management. Based on this, this paper takes the computational thinking mode of urban planning discipline in big data era as the research object, takes the scientific construction of computational urban planning as the research purpose, and adopts literature research methods and interdisciplinary research methods, comprehensively studies the connotation of the computing thinking mode of computer science. Meanwhile, this paper systematically discusses the system construction of urban computing, model generation, the theory and method of digital twinning, as well as the popularization of the computational thinking mode of urban and rural planning discipline and the scientific research of computational urban planning, which responds to the needs of the era of the development of urban and rural planning disciplines in the era of big data.

scholarly journals SUPPORTING AIR TRANSPORT POLICIES USING BIG DATA ANALYTICS: A DESCRIPTIVE APPROACH BASED EMERGING TREND ANALYSIS

2017 ◽  
Vol 8 (1) ◽  
pp. 51-72
Author(s):  
Jin-seo Park
Keyword(s):  
Qualitative Research ◽  
Big Data ◽  
Text Mining ◽  
Research Methods ◽  
Qualitative Research Methods ◽  
Research Papers ◽  
Emerging Trends ◽  
The Future ◽  
Descriptive Approach ◽  
Core Issues

Qualitative research methods based on literature review or expert judgement have been used to find core issues, analyze emerging trends and discover promising areas for the future. Deriving results from large amounts of information under this approach is both costly and time consuming. Besides, there is a risk that the results may be influenced by the subjective opinion of experts. In order to make up for such weaknesses, the analysis paradigm for choosing future emerging trend is undergoing a shift toward mplementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The hange used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and the promising areas for the future from research papers pertaining to overall aviation areas through text mining method, which is one of the big data analysis techniques. This study has limitations in that its analysis for retrieving the aviation-related core issues and promising fields was restricted to research papers containing the keyword "aviation." However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and emerging trends regarding the promising areas for the future in the aviation industry through the application of a big data-based descriptive approach.

The Provenance Problem: Research Methods and Ethics in the Age of WikiLeaks

2021 ◽  
pp. 1-16
Author(s):  
CHRISTOPHER DARNTON
Keyword(s):  
Big Data ◽  
Research Methods ◽  
New Materials ◽  
Research Strategies ◽  
Trade Offs ◽  
Research Designs ◽  
Leading Journals

How should political scientists navigate the ethical and methodological quandaries associated with analyzing leaked classified documents and other nonconsensually acquired sources? Massive unauthorized disclosures may excite qualitative scholars with policy revelations and quantitative researchers with big-data suitability, but they are fraught with dilemmas that the discipline has yet to resolve. This paper critiques underspecified research designs and opaque references in the proliferation of scholarship with leaked materials, as well as incomplete and inconsistent guidance from leading journals. It identifies provenance as the primary concept for improved standards and reviews other disciplines’ approaches to this problem. It elaborates eight normative and evidentiary criteria for scholars by which to assess source legitimacy and four recommendations for balancing their trade-offs. Fundamentally, it contends that scholars need deeper reflection on source provenance and its consequences, more humility about whether to access new materials and what inferences to draw, and more transparency in citation and research strategies.

What is Machine Learning? A Primer for the Epidemiologist

2019 ◽  
Author(s):  
Qifang Bi ◽  
Katherine E Goodman ◽  
Joshua Kaminsky ◽  
Justin Lessler
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Computer Science ◽  
Research Methods ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Epidemiologic Research ◽  
Learning Techniques ◽  
Applications Of Machine Learning

Abstract Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.

Research Methods: Using Big Data in Geriatric Oncology

2017 ◽  
pp. 1-12
Author(s):  
Martine Extermann ◽  
Vonetta L. Williams ◽  
Christine Walko ◽  
Yin Xiong
Keyword(s):  
Big Data ◽  
Research Methods ◽  
Geriatric Oncology

scholarly journals Analysis of Influencing Factors of Social Mental Health Based on Big Data

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yixuan Zhao ◽  
Qinghua Tang
Keyword(s):  
Mental Health ◽  
Big Data ◽  
Research Methods ◽  
Influencing Factors ◽  
Large Scale ◽  
Huge Amount ◽  
Economic Market ◽  
Huge Data ◽  
Proposed Model ◽  
Correlation Degree

Big data is a large-scale rapidly growing database of information. Big data has a huge data size and complexity that cannot be easily stored or processed by conventional data processing tools. Big data research methods have been widely used in many disciplines as research methods based on massively big data analysis have aroused great interest in scientific methodology. In this paper, we proposed a deep computational model to analyze the factors that affect social and mental health. The proposed model utilizes a large number of microblog manual annotation datasets. This huge amount of dataset is divided into six main factors that affect social and mental health, that is, economic market correlation, the political democracy, the management law, the cultural trend, the expansion of the information level, and the fast correlation of the rhythm of life. The proposed model compares the review data of different influencing factors to get the correlation degree between social mental health and these factors.

scholarly journals Research on Xun Yunchang’s Calligraphy Education Thought and Calligraphy Art

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Qiang Sun
Keyword(s):  
Research Methods ◽  
Liberal Arts ◽  
Case Analysis ◽  
Classical Literature ◽  
Great Contribution ◽  
History Of

During his lifetime, Xun Yunchang was a professor at the School of Liberal Arts of Southwest University. He was a master tutor for two majors in classical literature and calligraphy. He has made great achievements in poetry and calligraphy. He has three identities as calligrapher, poet, and scholar, showing his profound learning and superb level. Mr. Xun's calligraphy integrates Han and Wei dynasties, and goes in and out of Jin and Tang dynasties, which is thick and steady. Through case analysis and other research methods, this article believes that Mr. Xun has made a great contribution to the cause of calligraphy in China and should have a lofty status in the history of modern calligraphy.

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