Query: google scholar and big data

title: Big data and new social relations in higher education Academia.edu, Google Scholar and ResearchGate

title: Google Scholar: the 'big data' bibliographic tool
abstract: The launch of Google Scholar back in 2004 meant a revolution not only in the scientific information search market but also in research evaluation processes. Its dynamism, unparalleled coverage, and uncontrolled indexing make of Google Scholar an unusual product, especially when compared to traditional bibliographic databases. Conceived primarily as a discovery tool for academic information, it presents a number of limitations as a bibliometric tool. The main objective of this chapter is to show how Google Scholar operates and how its core database may be used for bibliometric purposes. To do this, the general features of the search engine (in terms of document typologies, disciplines, and coverage) are analysed. Lastly, several bibliometric tools based on Google Scholar data, both official (Google Scholar Metrics, Google Scholar Citations) and some developed by third parties (H Index Scholar, Publishers Scholar Metrics, ) as well as software to collect and process data from this source (Publish or Perish, Scholarometer) are introduced, aiming to illustrate the potential bibliometric uses of this source.

title: The Google Scholar Revolution: A big data bibliometric tool
abstract: Google Scholar Day: Changing current evaluation paradigms, organizado por Cybermetrics Lab (IPP CSIC) Madrid, 20 February 2017.

title: Clinical efficacy of chloroquine derivatives in COVID 19 infection: comparative meta analysis between the big data and the real world
abstract: In the context of the current coronavirus disease 2019 (COVID 19) pandemic, we conducted a meta analysis on the effects of chloroquine derivatives in patients, based on unpublished and published reports available publicly on the internet as of 27 May 2020. The keywords 'hydroxychloroquine' 'chloroquine' 'coronavirus' 'COVID 19' and 'SARS Cov 2' were used in the PubMed, Google Scholar and Google search engines without any restrictions as to date or language. Twenty studies were identified involving 105 040 patients (19 270 treated patients) from nine countries (Brazil, China, France, Iran, Saudi Arabia, South Korea, Spain and the USA) Big data observational studies were associated with conflict of interest, lack of treatment dosage and duration, and absence of favourable outcome. Clinical studies were associated with favourable outcomes and details on therapy. Among clinical studies, three of four randomized controlled trials reported a significant favourable effect. Among clinical studies, a significant favourable summary effect was observed for duration of cough (OR 0.19, p 0.00003) duration of fever (OR 0.11, p 0.039) clinical cure (OR 0.21, p 0.0495) death (OR 0.32, p 4.1 x 10 6) and viral shedding (OR 0.43, p 0.031) A trend for a favourable effect was noted for the outcome 'death and/or intensive care unit transfer' (OR 0.29, p 0.069) with a point estimate remarkably similar to that observed for death ~0.3) In conclusion, a meta analysis of publicly available clinical reports demonstrates that chloroquine derivatives are effective to improve clinical and virological outcomes, but, more importantly, they reduce mortality by a factor of 3 in patients with COVID 19. Big data are lacking basic treatment definitions and are linked to conflict of interest. The retraction of the only big data study associated with a significantly deleterious effect the day after (June 5, 2020) the acceptance of the present work (June 4, 2020) confirms the relevance of this work.

title: Discovering the relationship of disasters from big scholar and social media news datasets
abstract: The construction method for chains of disasters or events is still one of the core scientific questions in studying the common rules of disaster's evolution. Especially when dealing with the complexity and diversity of disasters, it is critical to make a further investigation on reducing the dependency of prior knowledge and supporting the comprehensive chains of disasters. This paper tries to propose a novel approach, through collecting the big scholar and social news data with disaster related keywords, analysing the strength of their relationships with the co word analysis method, and constructing a complex network of all defined disaster types, in order to finally intelligently extract the unique disaster chain of a specific disaster type. Google Scholar, Baidu Scholar and Sina News search engines are employed to acquire the needed data, and the respectively obtained disaster chains are compared with each other to show the robustness of our proposed approach. The achieved disaster chains are also compared with the ones concluded from existing research methods, and the very reasonable result is demonstrated. There is a great potential to apply this novel method in disaster management domain to find more secrets about disasters.

title: Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis
abstract: Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top tier and high impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors' names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS) data imputation, building learning models, dividing computations into sub tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field specific applications of big data in two promising and integrated fields, i.e. smart real estate and disaster management, were investigated, and a framework for field specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.

title: Research on Precision Evaluation Model of College English Teaching Based on Big Data
abstract: Innovations and technological advances have resulted in several web based products. Among them are social networking sites that are dominating internet usage among users. This article discusses many types of cybercrime that are prevalent today, namely, hacking, cyber fraud, spamming and cyber harassment. This article review was conducted as a systematic using computer based electronic search to access all journals related for cyber crime by using Google Scholar and Researchgate to collect the information for get the high impact on this article. The objective of this article is to discuss about CyberSecurity is a service created by the Ministry of Technology and Innovation of Malaysia that receives complaints from users who are victims of cybercrime and then the body will make statistics based on complaints received. Malaysia use the MyCERT service created by the Ministry of Science and Technology to receive any complaints related to this cybercrime activity. Based on the statistic in MyCERT type of cybercrime like fraud had the highest cases that have been recorded by MyCERT. Recorded by BruCERT from 2011 to 2015, with 61% of Malicious Software activities recording the highest percentage compared to cyber harrasment, website, spam and scam. In addition, it was followed by cyber harassment of 31% website 5% and scam 3% In this case, shows in Brunei also experience similar criminal activity in Malaysia. The support and responsibility of certain parties and societies play an important role in providing awareness and exposure to cybercrime. IT education is also able to curb issues related to cybercrime as a precautionary measure for students and adolescents to avoid becoming victims of cybercrime.

title: Entity deduplication in big data graphs for scholarly communication
abstract: Several online services offer functionalities to access information from "big research graphs" (e.g. Google Scholar, OpenAIRE, Microsoft Academic Graph) which correlate scholarly/scientific communication entities such as publications, authors, datasets, organizations, projects, funders, etc. Depending on the target users, access can vary from search and browse content to the consumption of statistics for monitoring and provision of feedback. Such graphs are populated over time as aggregations of multiple sources and therefore suffer from major entity duplication problems. Although deduplication of graphs is a known and actual problem, existing solutions are dedicated to specific scenarios, operate on flat collections, local topology drive challenges and cannot therefore be re used in other contexts.,This work presents GDup, an integrated, scalable, general purpose system that can be customized to address deduplication over arbitrary large information graphs. The paper presents its high level architecture, its implementation as a service used within the OpenAIRE infrastructure system and reports numbers of real case experiments.,GDup provides the functionalities required to deliver a fully fledged entity deduplication workflow over a generic input graph. The system offers out of the box Ground Truth management, acquisition of feedback from data curators and algorithms for identifying and merging duplicates, to obtain an output disambiguated graph.,To our knowledge GDup is the only system in the literature that offers an integrated and general purpose solution for the deduplication graphs, while targeting big data scalability issues. GDup is today one of the key modules of the OpenAIRE infrastructure production system, which monitors Open Science trends on behalf of the European Commission, National funders and institutions.

title: Investigating the Challenges of Big Data Analytics from the Viewpoints of Students in Mashhad in 2019
abstract: IntroductionNowadays Big Data Analytics has attracted students for research due to its very high capabilities, but there are also obstacles to analyses that need to be addressed. Therefore, the purpose of this study is to investigate the viewpoints of students of different disciplines at Mashhad universities on the challenges of this analysis.MethodThis study is a cross sectional study conducted on students of different universities and fields such as computer engineering, pharmacy, industry and biology in Mashhad, Iran. A questionnaire based on literature review in Pubmed, Google scholar, and science direct databases was designed by 10 experts from different disciplines using Delphi method. 185 students participated in the study. Students' viewpoints on the challenges were also collected. Descriptive and analytical results were reported using SPSS 21 and Maxqda software.ResultsThe age range of most students was 25 34 years. 54.2% were female. Most of the participants in this study were students of engineering and medical informatics. Of the participants in this study, 96.4% considered big data analytics necessary, 50.6% were familiar with the benefits of analytics. Lack of awareness, inadequate management, lack of managers' knowledge, lack of expertise, and lack of priority were the most important challenges for students.ConclusionDespite the importance and benefits of big data analytics, challenges are a major barrier to use that need to be addressed.

title: Big Data for Natural Disasters in an Urban Railroad Neighborhood: A Systematic Review
abstract: Landslides and floods are among the most common disasters in Brazil and are responsible for losses on social, environmental, and economic scales, even resulting in deaths. Floods can negatively affect the structure and operations of a railway network, causing travel delays, train service cancellations, and major fines for the railway. The objective of this article is to conduct a bibliographic review of what is available in publications on natural disasters, particularly landslides and floods, big data techniques, and railroads, at international and national levels. A bibliometric analysis was carried out according to the "PRISMA Flow Diagram" guidelines. The analysis in this study was conducted through searches of the following reference databases: Scopus, Web of Science, Scielo, and Google Scholar. After the keyword search was completed, the absence of available data and references relating to Brazil was verified. This justified the development of this and other related papers, and the efforts necessary to turn these data into useful information for the managers of cities and environmental institutions. The aim of this study is to fill the gap in the research, focusing on Brazil, related to big data, smart cities, and natural disasters (particularly, landslides and floods) and to propose other papers that can be developed in this subject area.