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.

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.

scholarly journals Judicial Big Data and Big-Data-Based Legal Research in China

2020 ◽  
pp. 1-20
Author(s):  
Weimin ZUO ◽  
Chanyuan WANG
Keyword(s):  
Big Data ◽  
Computer Science ◽  
Research Methods ◽  
New Products ◽  
Current Data ◽  
Small Data ◽  
Legal Research ◽  
The Future ◽  
Statistical Science

Abstract The newly established judicial-transparency platforms, like China Judgements Online, have provided access to a new resource—judicial big data—making it possible to conduct empirical, big-data-based legal research. However, as is often the case with new products, these platforms—China Judgements Online, in particular—pose a few problems for big-data-based legal research: insufficient academic depth; immature technical methods; and lack of innovation due to flawed data, strict technical thresholds, and lack of theoretical ambition and ability. In the future, big-data-based legal research should make use of current data resources, continue to promote statistical science and computer science in research, and apply small-data research methods, and in the meanwhile pay attention to the combination of data and theory.

Data Science: A New Paradigm in the Age of Big-Data Science and Analytics

2017 ◽  
Vol 13 (02) ◽  
pp. 119-143 ◽  
Author(s):  
Claude E. Concolato ◽  
Li M. Chen
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Big Data ◽  
Computer Science ◽  
Data Science ◽  
Big Data Analytics ◽  
Modern World ◽  
Growth Data ◽  
New Paradigm ◽  
Great Opportunity

As an emergent field of inquiry, Data Science serves both the information technology world and the applied sciences. Data Science is a known term that tends to be synonymous with the term Big-Data; however, Data Science is the application of solutions found through mathematical and computational research while Big-Data Science describes problems concerning the analysis of data with respect to volume, variation, and velocity (3V). Even though there is not much developed in theory from a scientific perspective for Data Science, there is still great opportunity for tremendous growth. Data Science is proving to be of paramount importance to the IT industry due to the increased need for understanding the insurmountable amount of data being produced and in need of analysis. In short, data is everywhere with various formats. Scientists are currently using statistical and AI analysis techniques like machine learning methods to understand massive sets of data, and naturally, they attempt to find relationships among datasets. In the past 10 years, the development of software systems within the cloud computing paradigm using tools like Hadoop and Apache Spark have aided in making tremendous advances to Data Science as a discipline [Z. Sun, L. Sun and K. Strang, Big data analytics services for enhancing business intelligence, Journal of Computer Information Systems (2016), doi: 10.1080/08874417.2016.1220239]. These advances enabled both scientists and IT professionals to use cloud computing infrastructure to process petabytes of data on daily basis. This is especially true for large private companies such as Walmart, Nvidia, and Google. This paper seeks to address pragmatic ways of looking at how Data Science — with respect to Big-Data Science — is practiced in the modern world. We also examine how mathematics and computer science help shape Big-Data Science’s terrain. We will highlight how mathematics and computer science have significantly impacted the development of Data Science approaches, tools, and how those approaches pose new questions that can drive new research areas within these core disciplines involving data analysis, machine learning, and visualization.

EMERGING BIG DATA STORAGE ARCHITECTURES: A NEW PARADIGM

2017 ◽  
Vol 4 (2) ◽  
pp. 31
Author(s):  
DHANAPAL AASHA ◽  
SARAVANAKUMAR VENKATESH .M ◽  
SABIBULLAH M ◽  
◽  
◽  
...  
Keyword(s):  
Big Data ◽  
Data Storage ◽  
New Paradigm ◽  
Big Data Storage

scholarly journals Spatial Analysis of Urban Residential Sensitivity to Heatwave Events: Case Studies in Five Megacities in China

2021 ◽  
Vol 13 (20) ◽  
pp. 4086
Author(s):  
Guoqing Zhi ◽  
Bin Meng ◽  
Juan Wang ◽  
Siyu Chen ◽  
Bin Tian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Spatial Analysis ◽  
Urban Planning ◽  
Language Processing ◽  
Temporal Trends ◽  
New Paradigm ◽  
Geographic Space ◽  
Low Sensitivity ◽  
The Relationship

Urban heatwaves increase residential health risks. Identifying urban residential sensitivity to heatwave risks is an important prerequisite for mitigating the risks through urban planning practices. This research proposes a new paradigm for urban residential sensitivity to heatwave risks based on social media Big Data, and describes empirical research in five megacities in China, namely, Beijing, Nanjing, Wuhan, Xi’an and Guangzhou, which explores the application of this paradigm to real-world environments. Specifically, a method to identify urban residential sensitive to heatwave risks was developed by using natural language processing (NLP) technology. Then, based on remote sensing images and Weibo data, from the perspective of the relationship between people (group perception) and the ground (meteorological temperature), the relationship between high temperature and crowd sensitivity in geographic space was studied. Spatial patterns of the residential sensitivity to heatwaves over the study area were characterized at fine scales, using the information extracted from remote sensing information, spatial analysis, and time series analysis. The results showed that the observed residential sensitivity to urban heatwave events (HWEs), extracted from Weibo data (Chinese Twitter), best matched the temporal trends of HWEs in geographic space. At the same time, the spatial distribution of observed residential sensitivity to HWEs in the cities had similar characteristics, with low sensitivity in the urban center but higher sensitivity in the countryside. This research illustrates the benefits of applying multi-source Big Data and intelligent analysis technologies to the understand of impacts of heatwave events on residential life, and provide decision-making data for urban planning and management.

scholarly journals Evaluating User Behaviour in a Cooperative Environment

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 303 ◽  
Author(s):  
Enrico Bazzi ◽  
Nunziato Cassavia ◽  
Davide Chiggiato ◽  
Elio Masciari ◽  
Domenico Saccà ◽  
...  
Keyword(s):  
Big Data ◽  
Data Storage ◽  
User Preferences ◽  
Competitive Environment ◽  
New Paradigm ◽  
Management Tools ◽  
Analysis Strategies ◽  
Tangible Rewards ◽  
Advanced Analysis ◽  
Management Techniques

Big Data, as a new paradigm, has forced both researchers and industries to rethink data management techniques which has become inadequate in many contexts. Indeed, we deal everyday with huge amounts of collected data about user suggestions and searches. These data require new advanced analysis strategies to be devised in order to profitably leverage this information. Moreover, due to the heterogeneous and fast changing nature of these data, we need to leverage new data storage and management tools to effectively store them. In this paper, we analyze the effect of user searches and suggestions and try to understand how much they influence a user’s social environment. This task is crucial to perform efficient identification of the users that are able to spread their influence across the network. Gathering information about user preferences is a key activity in several scenarios like tourism promotion, personalized marketing, and entertainment suggestions. We show the application of our approach for a huge research project named D-ALL that stands for Data Alliance. In fact, we tried to assess the reaction of users in a competitive environment when they were invited to judge each other. Our results show that the users tend to conform to each other when no tangible rewards are provided while they try to reduce other users’ ratings when it affects getting a tangible prize.

Asserting parallel computational thinking into an undergraduate computer science curriculum

2011 ◽  
Author(s):  
Edusmildo Orozco ◽  
Rafael Arce-Nazario ◽  
Peter Musial ◽  
Cynthia Lucena-Roman ◽  
Zoraida Santiago
Keyword(s):  
Computer Science ◽  
Science Curriculum ◽  
Computational Thinking ◽  
Computer Science Curriculum

Using Mobile Network Big Data for Informing Transportation and Urban Planning in Colombo

2014 ◽  
Author(s):  
Sriganesh Lokanathan ◽  
Gabriel Kreindler ◽  
Nisansa Dilushan de Silva ◽  
Yuhei Miyauchi ◽  
Dedunu Dhananjaya
Keyword(s):  
Big Data ◽  
Urban Planning ◽  
Mobile Network ◽  
Network Big Data

An Efficient Approach for Detection and Verification of Infected Cases of Covid-19 Virus through Block Chain: A Survey

2020 ◽  
Vol 10 ◽  
Author(s):  
Kanika Gupta ◽  
Aatif Jamshed
Keyword(s):  
Data Storage ◽  
Operations Management ◽  
World Health ◽  
Chain Model ◽  
Human Beings ◽  
Healthcare Applications ◽  
Food Market ◽  
Block Chain ◽  
Novel Coronavirus ◽  
Health Organization

: Some unknown cases of pneumonia were communicated to World Health Organization (WHO) on 31 December,2019 in China’s Wuhan state. The higher authorities of China informed novel coronavirus as the root cause and labelled as “nCov-2019”. This virus is lying into the virus’s family which propagates the diseases like cold flu, lungs infection and more serious diseases. It is not detected earlier in human beings as it is considered to be a new patch on life. Many countries have increased their surveillance forces around the globe to detect any new novel coronavirus cases. An efficient and safe network for secure data storage i.e. Block chain is used in several applications such as food market, healthcare applications, finance, operations management, Internet of Things (IoT). In this paper, with the use of this emerging technology, are able to track useful information and accelerate the treatment process of patients. It also preserves the person’s identity. Correct implementation of block chain model has the chances to restrict the coronavirus transmissions and its related mortality rate where there are inadequate facilities of testing. Other infectious diseases will also be curbed by this model. The advantages of this model can reach to various stakeholders who are involved in the healthcare field which helps us to restrict the transmission of various diseases.

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