Big Data Testbed for Research and Education Networks Analysis

10.7125/40.16 ◽  
2015 ◽  
Vol 40 (0) ◽  
pp. 109
Author(s):  
Somkiat Dontongdang ◽  
Panjai Tantatsanawong ◽  
Ajchariya Saeung
Keyword(s):  
Big Data ◽  
Networks Analysis ◽  
Research And Education

scholarly journals Mapping the Interaction between Research and Education Fields of Sustainability in Architecture: A retrospective Timeline

2021 ◽  
Author(s):  
Ömer Özgenç ◽  
◽  
Nur Çağlar ◽  
Işıl Ruhi-Sipahioğlu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Research Output ◽  
Architectural Education ◽  
Related Topic ◽  
Bibliometric Data ◽  
Journal Articles ◽  
Research Areas ◽  
Research Fields ◽  
Research And Education

Global research output grows exponentially each year. This paper attempts to drive meaning out of this big data on two fields of research in architecture. It maps the interaction between the research fields of sustainability in architecture and architectural education through the perspective of bibliometric data analysis and its visualization. Based on the analysis of bibliometric data, it draws and juxtaposes two timelines for the field of sustainable architecture and the field of architectural education. The objective is to propose a retrospective method that can provide insight for a broader understanding of sustainability and its impacts on architectural education. It utilizes VOSviewer, CiteSpace, and Gephi to visualize bibliometric networks, along with Tableau to analyze the number of journal articles and publications published across years. The paper presents initial findings concerning the leading scholars, trends, and patterns of the research areas, milestone events, and dominant studies to point out the significance of the cooperation between research and education fields of the related topic.

scholarly journals O sentimento político em redes sociais: big data, algoritmos e as emoções nos tweets sobre o impeachment de Dilma Rousseff | Political sentiment in social networks: big data, algorithims and emotions in tweets about the impeachment of Dilma Rousseff

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Fabio Malini ◽  
Patrick Ciarelli ◽  
Jean Medeiros
Keyword(s):  
Social Networks ◽  
Genetic Algorithms ◽  
Big Data ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Negative Polarization ◽  
Political Networks ◽  
Networks Analysis ◽  
Fear And Anxiety

Resumo Este artigo se propõe a ampliar a metodologia perspectivista (MALINI, 2016) de análise de redes sociais, incorporando um procedimento de análise dos sentimentos das mensagens postadas em redes de controvérsias políticas, em particular, em dois momentos distintos da campanha pelo impeachment da presidenta Dilma. O primeiro é o período da eclosão das manifestações antipetistas, no dia 15 de março de 2015. O segundo, dia 27 de agosto de 2016, quando a presidenta é deposta do cargo. Realiza uma revisão sobre a análise de sentimentos em megadados do Twitter e constrói uma metodologia que combina classificação humana de textos com aplicação de algoritmos genéticos de análise de textos, no intuito de analisar sentimentos genéricos (baseado na polarização positivo/negativos) e sentimento específicos, baseados nas seguintes emoções: Alegria, Raiva, Medo, Antecipação, Desgosto, Tristeza, Surpresa e Confiança. Conclui demonstrando que os movimentos pró e anti-Dilma são marcados pelo predomínio de sentimento de raiva, medo e ansiedade, confirmando a hipótese que a trolagem ofensiva demarca o estilo da indignação propagada em redes políticas no Twitter brasileiro.  Palavras-Chave: Análise de Sentimento; Big Data; Redes; Política; Twitter.Abstract This article aims to expand the perspectivist methodology (Malini, 2016) of social networks analysis, incorporating a proceeding of sentiment analysis of the messages posted in networks of political controversies, in particular, in two distinct moments of the campaign for the impeachment of President Dilma. The first is the period of the outbreak of PT protests, on March 15, 2015. The second, on August 27, 2016, when the president is deposed. We will be doing a theoretical review about sentiment analysis in Big Data on Twitter to build a methodology that combines human classification of texts with the application of genetic algorithms of text analysis and to analyze generic sentiments (based on positive / negative polarization) and specific sentiment, based on emotions like Joy, Anger, Fear, Anticipation, Disgust, Sadness, Surprise and Trust. It concludes by demonstrating that pro and anti-Dilma movements are marked by a predominance of anger, fear and anxiety, confirming the hypothesis that an offensive trolling demarcates the style of indignation propagated by political networks in Brazilian Twitter.Keywords: Sentiment Analysis; Big Data; Social Network; Politics; Twitter. 

Technologies for Handling Big Data

2020 ◽  
pp. 34-49 ◽  
Author(s):  
Rashmi Agrawal
Keyword(s):  
Big Data ◽  
Credit Card ◽  
Scientific Research ◽  
Sloan Digital Sky Survey ◽  
Strategic Intelligence ◽  
Sky Survey ◽  
Central Resource ◽  
Pass Through ◽  
Research And Education ◽  
Cctv Camera

In today's world, every time we connect phone to internet, pass through a CCTV camera, order pizza online, or even pay with credit card to buy some clothes, we generate data and that “ocean of data” is popularly known as big data. The amount of data that's being created and stored on a universal level is almost inconceivable, and it just keeps growing. The amount of data we create is doubled every year. Big data is a critical concept that integrates all kinds of data and plays an important role for strategic intelligence for any modern company. The importance of big data doesn't revolve around how much data you have, but what you do with it. Big data is now the key for competition and growth for new startups, medium, and big enterprises. Scientific research is now on boom using big data. For the astronomers, Sloan Digital Sky Survey has become a central resource. Big data has the potential to revolutionize research and education as well. The aim of this chapter is to discuss the technologies that are pertinent and essential for big data.

scholarly journals Open cyberGIS software for geospatial research and education in the big data era

SoftwareX ◽  
2016 ◽  
Vol 5 ◽  
pp. 1-5 ◽  
Author(s):  
Shaowen Wang ◽  
Yan Liu ◽  
Anand Padmanabhan
Keyword(s):  
Big Data ◽  
Research And Education

scholarly journals Collaborating Data Mining Modeling with Big Data Analytics for Disaster Prediction

2019 ◽  
Vol 8 (2) ◽  
pp. 4812-4819
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Functional Information ◽  
Business Research ◽  
Front End ◽  
Large Databases ◽  
Research And Education ◽  
Disaster Prediction

Data mining is the procedure of bringing out the earlier unfold justifiable, logical, intelligible, functional information from large databases, big data to deliver accurate prediction, decision and implementation systems in engineering, business, research and education world. Data mining will effectively introduce the computing strategies and techniques to retrieve the applicable and convenient information from combined large databases known as big data. This paper signifies and explains Big data, Data mining and the importance and ease of Data mining using big data as a back end for delivering appropriate forecasts, prediction and experimental prospective solution as a front end.

Overview of Digital Health Research: A Global Multi-Disciplinary Collaborative Alliance from the Worldwide Universities Network (Preprint)

2019 ◽  
Author(s):  
Simon Poon ◽  
Mark Latt ◽  
Michelle A Morris ◽  
Owen Johnson ◽  
Nicholas Fuggle ◽  
...  
Keyword(s):  
Big Data ◽  
Health Research ◽  
Communicable Disease ◽  
Digital Health ◽  
Academic Disciplines ◽  
Research Network ◽  
Development Education ◽  
Diverse Range ◽  
Health Related ◽  
Research And Education

BACKGROUND Digital health is an important part of the future of health care, prevention and management of disease and innovative monitoring solutions. With an aging population and rising health related costs, digital health is an essential part of the solution, alongside the emerging big data and associated analytics. To varying extents, digital health and big data are present worldwide. However, consistency in terminology, regulation and implementation differ. As an international network of interdisciplinary experts we review and discuss the digital health and big data landscape. OBJECTIVE We firstly identify current challenges and solutions in digital health development, research, deployment in the management of non-communicable disease and regulation and then go on to establish an ongoing and international collaboration of multidisciplinary researchers and educators; creating opportunities for research and education. METHODS The Digital Health Research Network was established using the Worldwide Universities Network as a platform and a funding resource. The newly formed network harnesses expertise from a wide array of academic disciplines within applications of digital health and big data for health. Meetings took place both electronically and face to face, with a Research Open Day in Sydney and the International Symposium for Digital Health in Hong Kong facilitating wider networking and discussion. RESULTS Many challenges working across disciplines in the digital health area have been identified. These include inconsistent definitions for digital health and big data, a diverse range of digital technologies available across the globe, differences in regulation of such technologies. There is not equity in resources and standards globally. He range of stakeholders involved in digital health and big data relating to health are extensive. It is important that these stakeholders can communicate effectively, with a common technical language. Continued development, education and widening engagement are integral components of developing digital health worldwide. CONCLUSIONS Digital Health is a necessary and sufficient factor in achieving health gains. However, in is critical that digital health is leveraged appropriately and that transformation of interdisciplinary practices can intelligently link digital health with care management processes to make a difference. The new interdisciplinary, International Society for Digital Health aims to provide a platform to facilitate this. CLINICALTRIAL n/a

scholarly journals Development of Stock Networks Using Part Mutual Information and Australian Stock Market Data

Entropy ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. 773
Author(s):  
Yan Yan ◽  
Boyao Wu ◽  
Tianhai Tian ◽  
Hu Zhang
Keyword(s):  
Information Theory ◽  
Big Data ◽  
Mutual Information ◽  
Stock Market ◽  
Correlation Coefficient ◽  
Market Data ◽  
Industrial Sectors ◽  
Large Size ◽  
Networks Analysis ◽  
The Relationship

Complex network is a powerful tool to discover important information from various types of big data. Although substantial studies have been conducted for the development of stock relation networks, correlation coefficient is dominantly used to measure the relationship between stock pairs. Information theory is much less discussed for this important topic, though mutual information is able to measure nonlinear pairwise relationship. In this work we propose to use part mutual information for developing stock networks. The path-consistency algorithm is used to filter out redundant relationships. Using the Australian stock market data, we develop four stock relation networks using different orders of part mutual information. Compared with the widely used planar maximally filtered graph (PMFG), we can generate networks with cliques of large size. In addition, the large cliques show consistency with the structure of industrial sectors. We also analyze the connectivity and degree distributions of the generated networks. Analysis results suggest that the proposed method is an effective approach to develop stock relation networks using information theory.

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