Comparison of the Level of Familiarity of IT Units' Staff and students with Big Data Analyzes

2020 ◽  
Author(s):  
Elham Nazari ◽  
Parnian Asgari ◽  
hamed tabesh
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Work Experience ◽  
Rapid Development ◽  
Big Data Analysis ◽  
Individual Characteristics ◽  
Scientific Study ◽  
Level Of Knowledge ◽  
And Gender ◽  
Analytical Approaches

Abstract introduction The rapid development of technology in recent decades has led to the production of a huge amount of data. This type of data analysis that is called Big Data Analysis obtain Many benefits, including reducing costs. One of the challenges of these analyses is the lack of specialized expertise and knowledge in this area. The purpose of this study was to compare the familiarity of IT staff and students with big data analyzes at various universities and organizations. Materials and method This analytical study was conducted on IT units' staff and students of different organizations and universities in Mashhad, Iran. A questionnaire was designed based on reviewing the texts published in PubMed, google scholar, science direct, and EMBASE databases and using the Delphi method and the attendance of 10 specialists in different disciplines. The designed questionnaire evaluated the participants' knowledge about the Big Data analyzes in two parts. The participants were 265 IT units' staff and students of different organizations, completing the designed questionnaire. Participants' opinion was evaluated using two descriptive and analytical approaches. The relationship between knowledge scores and individual characteristics such as gender, age, work experience, Field of study, degree, the average number of hours’ scientific study and non-scientific study per week was examined. To investigate the synchronous and reciprocal effects GLM was used. Results Scores earned by students and staff were 2.66 ± 1.13 and 2.28 ± 1.21 respectively that p =. 012 represented a significant correlation between the level of knowledge of students and staff. In other words, the level of knowledge of staff about big data was more than the level of knowledge of the students.The correlation of each of the variables was not significant with the score of the Big Data Analysis Knowledge.But There was a significant correlation between experience and gender with the knowledge scores. Conclusions In general, the level of knowledge in analyzing big data in different groups of people was at a low level that implementing measures such as holding training courses in this field seems necessary.

Research on the Data Driven Practice Teaching Mode: Take the Didi Data Set as Example

2021 ◽  
Vol 105 ◽  
pp. 348-355
Author(s):  
Hou Xiang Liu ◽  
Sheng Han Zhou ◽  
Bang Chen ◽  
Chao Fan Wei ◽  
Wen Bing Chang ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Teaching Method ◽  
Rapid Development ◽  
Big Data Analysis ◽  
Theoretical Knowledge ◽  
Data Set ◽  
Teaching Mode ◽  
Practice Teaching ◽  
Practical Teaching

The paper proposed a practice teaching mode by making analysis on Didi data set. There are more and more universities have provided the big data analysis courses with the rapid development and wide application of big data analysis technology. The theoretical knowledge of big data analysis is professional and hard to understand. That may reduce students' interest in learning and learning motivation. And the practice teaching plays an important role between theory learning and application. This paper first introduces the theoretical teaching part of the course, and the theoretical methods involved in the course. Then the practice teaching content of Didi data analysis case was briefly described. And the study selects the related evaluation index to evaluate the teaching effect through questionnaire survey and verify the effectiveness of teaching method. The results show that 78% of students think that practical teaching can greatly improve students' interest in learning, 89% of students think that practical teaching can help them learn theoretical knowledge, 89% of students have basically mastered the method of big data analysis technology introduced in the course, 90% of students think that the teaching method proposed in this paper can greatly improve students' practical ability. The teaching mode is effective, which can improve the learning effect and practical ability of students in data analysis, so as to improve the teaching effect.

scholarly journals Studying Students' Knowledge of the Benefits, Challenges, and Applications of Big Data Analytics In Healthcare

2020 ◽  
Author(s):  
Elham Nazari ◽  
Maryam Edalati Khodabandeh ◽  
Ali Dadashi ◽  
Marjan Rasoulian ◽  
hamed tabesh
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Work Experience ◽  
Medical Records ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Information Modeling ◽  
Field Of Study ◽  
Cross Sectional

Abstract Introdution Today, with the advent of technologies and the production of huge amounts of data, Big Data analytics has received much attention especially in healthcare. Understanding this field and recognizing its benefits, applications and challenges provide useful background for conducting efficient research. Therefore, the purpose of this study was to evaluate the students' familiarity from different universities of Mashhad with the benefits, applications and challenges of Big Data analysis.Method This is a cross-sectional study that was conducted on students of Medical Engineering, Medical Informatics, Medical Records and Health Information Management in Mashhad-Iran. A questionnaire was designed based on literature review in pubmed, google scholar, science direct and EMBASE databases, using Delphi method and presence of 10 experts from different fields of study. The designed questionnaire evaluated the opinion of students regarding benefits, challenges and applications of Big Data analytics. 200 students participated in the study and completed the designed questionnaire. Participants' opinions were evaluated descriptively and analytically. Result Most students were between 20 and 30 years old. 63% of them were male and 43.5% had no work experience. Current and previous field of study of most of the students were HIT, HIM, and Medical Records. Most of the participants in this study were undergraduates. 61.5% were economically active, 54.5% were exposed to Big Data. The mean scores of participants in benefits, applications, and challenges section were 3.71, 3.68, and 3.71, respectively, and process management was significant in different age groups (p=0.046), information, modeling, research, and health informatics across different fields of studies were significant (p=0.015, 0.033, 0.001, 0.024) Information and research were significantly different between groups (p=0.043 and 0.019), research in groups with / without economic activity was significant (p= 0.017) and information in exposure / non exposure to Big Data groups was significant (p=0.02). Conclusion Despite the importance and benefits of Big Data analytics, students' lack of familiarity with the necessity and importance of these analytics in industries and research is significant. The field of study and level of study do not appear to have an effect on the degree of knowledge of individuals regarding Big Data analysis. The design of technical training courses in this field may increase the level of knowledge of individuals regarding Big Data analysis.

scholarly journals Integrated Understanding of Big Data, Big Data Analysis, and Business Intelligence: A Case Study of Logistics

2018 ◽  
Vol 10 (10) ◽  
pp. 3778 ◽  
Author(s):  
Dong-Hui Jin ◽  
Hyun-Jung Kim
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Business Intelligence ◽  
Rapid Development ◽  
Big Data Analysis ◽  
Sustainable Growth ◽  
Actual Application ◽  
Collection Data ◽  
Information And Communication

Efficient decision making based on business intelligence (BI) is essential to ensure competitiveness for sustainable growth. The rapid development of information and communication technology has made collection and analysis of big data essential, resulting in a considerable increase in academic studies on big data and big data analysis (BDA). However, many of these studies are not linked to BI, as companies do not understand and utilize the concepts in an integrated way. Therefore, the purpose of this study is twofold. First, we review the literature on BI, big data, and BDA to show that they are not separate methods but an integrated decision support system. Second, we explore how businesses use big data and BDA practically in conjunction with BI through a case study of sorting and logistics processing of a typical courier enterprise. We focus on the company’s cost efficiency as regards to data collection, data analysis/simulation, and the results from actual application. Our findings may enable companies to achieve management efficiency by utilizing big data through efficient BI without investing in additional infrastructure. It could also give them indirect experience, thereby reducing trial and error in order to maintain or increase competitiveness.

scholarly journals Big Data Analysis and Research on Fracturing Construction Parameters of Shale Gas Horizontal Wells—A Case Study of Horizontal Wells in Fuling Demonstration Area, China

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8357
Author(s):  
Minxuan Li ◽  
Liang Cheng ◽  
Dehua Liu ◽  
Jiani Hu ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Shale Gas ◽  
Optimal Parameter ◽  
Rapid Development ◽  
Horizontal Wells ◽  
Big Data Analysis ◽  
Arithmetic Means ◽  
Correlation Clustering ◽  
Analysis Process

With the rapid development of computer science and technology, the Chinese petroleum industry has ushered in the era of big data. In this study, by collecting fracturing data from 303 horizontal wells in the Fuling Shale Gas Demonstration Area in China, a series of big data analysis studies was conducted using Pearson’s correlation coefficient, the unweighted pair group with arithmetic means method, and the graphical plate method to determine which is best. The fracturing parameters were determined through a series of big data analysis studies. The big data analysis process is divided into three main steps. The first is data preprocessing to screen out eligible, high-yielding wells. The second is a fracturing parameter correlation clustering analysis to determine the reasonableness of the parameters. The third is a big data panel method analysis of specific fracturing construction parameters to determine the optimal parameter range. The analyses revealed that the current amount of 100 mesh sand in the Fuling area is unreasonable; further, there are different preferred areas for different fracturing construction parameters. We have combined different fracturing parameter schemes by preferring areas. This analysis process is expected to provide new ideas regarding fracturing scheme design for engineers working on the frontline.

scholarly journals A Survey of Students’ Attitudes to Big Data Analysis in Iranian Universities

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Nazari E ◽  
◽  
Norouzi S ◽  
Aldaghi T ◽  
Rasoulian M ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Work Experience ◽  
New Technologies ◽  
Master's Degree ◽  
Big Data Analysis ◽  
Cross Sectional ◽  
Academic Degree ◽  
Fields Of Study ◽  
Master’S Degree

Introduction: Today, with the emergence of new technologies and massive data, big data analysis has attracted the attention of researchers, industries and universities on a global scale. The present research aims to explore students’ attitude to big data analysis in different fields of study. Methodology: The present cross-sectional study was conducted with students at different universities and fields of study in Iran. A questionnaire was developed. This questionnaire explored students’ attitude toward big data analysis. To this aim, 359 university students participated in the research. The data were analyzed using descriptive and inferential statistics. Results: The age of the students ranged between 25 and 34 years. 55.2% were female and 54% were economically active. 40.9% had a work experience of less than a year. The academic degree of the majority of participants was master’s degree. 93.9% of the participants believed that big data analysis was essential for the country. 43.2% maintained that big data mostly belonged to the communication industry. 28.1% perceived MATLAB useful software for analysis. 40.9% were familiar with the benefits of analysis. Engage in economic activities, less than 1 year of experience and studies for a Master’s degree showed to be significantly correlated with familiarity with the benefits of big data (p=0.01). Such issues as high costs, managers’ unfamiliarity and lack of expertise and complexity were raised by the respondents.

scholarly journals «BIG DATA» IN EDUCATION: INTERNATIONAL AND RUSSIAN EXPERIENCE

2020 ◽  
Vol 71 (3) ◽  
pp. 199-204
Author(s):  
V.V. Grinshkun ◽  
◽  
O.Yu. Zaslavskaya ◽  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Work Experience ◽  
Big Data Analysis ◽  
Educational Process ◽  
Effective Expansion ◽  
Data Collection And Analysis ◽  
Learning Competencies ◽  
Russian Education ◽  
Qualitative Changes

Big data analysis and their free circulation can serve as a basis for qualitative changes, the formation of a new modern and dynamically developing education system. The article analyzes data on the educational process, which will make it possible to better assess teachers and, if necessary, make changes to the content of their training. As data for the analysis, information about the traits of the student's character, his success in learning, and previous work experience are used. One of the significant points in terms of collecting and structuring big data in Russian education is the creation and implementation of the Moscow Electronic School project. Big data collection and analysis technologies open up great prospects for creating new positive learning experiences and highly effective expansion of lifelong learning competencies.

Thrombocytopenia and Anemia Among Patients with Cerebral Palsy: A Big-Data Analysis

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 30-31
Author(s):  
Ioannis Milioglou ◽  
Sanjay P Ahuja ◽  
Nathan Richard Stehouwer
Keyword(s):  
Big Data ◽  
Cerebral Palsy ◽  
Data Analysis ◽  
General Population ◽  
Big Data Analysis ◽  
Age And Gender ◽  
Aggregated Data ◽  
Infectious Process ◽  
Gender Based ◽  
And Gender

Title: Thrombocytopenia and Anemia among patients with Cerebral Palsy: A big-data analysis Introduction : The incidence of thrombocytopenia and anemia in patients with cerebral palsy (CP) is not well reported in the medical literature . Patients with CP, particularly those with the most severe phenotypes, may be repeatedly exposed to medications and infections associated with blood dyscrasias. The aim of this study was to assess the the prevalence of thrombocytopeniaand anemia in patients with CP comparing it to the general population. We also compared the prevalence of hospitalizations between patients with CP, with and without these blood dyscrasias. Methods: Utilizing a commercial database (Explorys Inc, IBM), we identified a cohort of patients diagnosed with CP based on the Systematized Nomenclature of Medicine-Clinical Terms. We calculated the overall prevalence rate of diagnosis of CP, described age, and gender-based prevalence rates of CP, and identified associated diagnoses of thrombocytopenia and anemia associated with CP. Explorys is a largereservoir of de-identified, HIPAA-compliant aggregated data of more than 63 million unique patients. First we compared the incidence of thrombocytopenia and anemia between patients with CP and the general population. To generate a cohort of patients representative of the general population, a cohort of patients with any coded disease was pooled except for CP. Moreover, relevant covariates including hospitalization, use of antiepileptic drugs (AED), infections, age, and gender were recorded and will be included in a multivariate analysis. We further sub-grouped patients with CP based on diagnoses of wheelchair dependency and AED exposure as markers of morbidity and risk for blood dyscrasias respectively. Results: We identified 49,492,350 unique patients of whom 93,850 were patients with CP. The prevalence of thrombocytopenia was higher in patients with CP than in the general population (Table 1; 4.91% vs 1.76%, RR 2.8, p<0.0000001). The same case was also noted for anemia (24,4% vs 11,4%, RR 2.1, p<0.0000001). When comparing the baseline characteristics of the two populations it seems that patients with CP are less aged compared to the general population (prevalence of patients >65 yoa , 53% vs 28%). Moreover, the infection rate in patient with CP surpasses that of the general population (infectious disease prevalence 93% vs 77%). On subgroup analysis, wheelchair dependence,AED exposure and any infectious process in patients with CP are risk factors for both anemia and thrombocytopenia. Nonetheless, an infectious process seems to be a stronger risk factor for both thrombocytopenia and anemia compared to AED exposure and wheelchair dependence (WD). More specifically the RR for thrombocytopenia in patients with CP is 7.4 compared to 4.8 and 2.4 for AED exposure and WD respectively. The same pattern is noticed for anemia (RR 4.7 vs 2.6 vs 1.8, p<0.001).Finally, we compared the total hospitalization rate in the past 5 years between patients with CP and with or withoutthrombocytopenia, anemia and cytopenia (defined as thrombocytopenia and anemia). Higher hospitalizations rates were found for patients with CP and with any of the above defined blood cell dyscrasias (Relative risk for hospitalization for patients with CP and thrombocytopenia/anemia/cytopenia vs without, RR 10/9/12.5, P<0.001). Discussion: Our study using large, aggregated data from Explorys demonstrates there is a higher prevalence of thrombocytopenia and anemia in patients with CP. In our cohort, patients with CP with thrombocytopenia and/or anemia were more likely to be hospitalized. Further research is needed to confirm this observed association and to clarify the mechanism of the relationship between CP and cytopenias. Disclosures Ahuja: Genentech: Consultancy, Honoraria; Sanofi Genzyme: Consultancy, Honoraria; XaTek, Inc.: Consultancy, Patents & Royalties, Research Funding.

scholarly journals Big Data from A to Z

2019 ◽  
Vol 8 (1) ◽  
pp. 20
Author(s):  
Elham Nazari ◽  
Marziyeh Afkanpour ◽  
Hamed Tabesh
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Science ◽  
Rapid Development ◽  
Big Data Analysis ◽  
Data Sources ◽  
The Past ◽  
Logical Time ◽  
Definition Of

The rapid development of technology over the past 20 years has led to explosive data growth in various industries, including defense industries, healthcare. The analysis of generated Big Data has recently been addressed by many researchers, because today's Big Data analysis are one of the most important and most profitable areas of development in Data Science and companies that are able to extract valuable knowledge among the massive amount of data at logical time can earn significant advantages . Accordingly, in this survey, we investigate definition of the Big Data and the data sources. Also look at advantages, challenges, applications, analysis and platforms used in the Big Data.

scholarly journals Frontiers in the emerging development of blockchain and Bitcoin: visual research based on big data analysis

2021 ◽  
Vol 235 ◽  
pp. 03017
Author(s):  
Chung-Lien Pan ◽  
Zizhen Chen ◽  
Zhixiang Zhou ◽  
Zhuocheng Cai ◽  
Xuanyan Liu ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Web Of Science ◽  
Rapid Development ◽  
Visual Presentation ◽  
Big Data Analysis ◽  
Research Progress ◽  
Visual Research ◽  
Key Points ◽  
External Development

In the era of the rapid development of information technology, the innovation of Fintech continues to send emerging research hotspots to the financial market. Based on the analysis of documents retrieved from the Web of Science database, this article provides a comprehensive data analysis and visualization of keywords such as “blockchain”, “bitcoin”, and “business and economic”. Using big data analysis technology and visual presentation, the author analyzed the details of the author’s keywords, popular organizations, countries, sources, and other key points of the correlation and external development status. Show the researchers the influence of the intersection of keywords, and point out the leading status of the organizations or countries with large resource occupancy in the research progress; at the same time, provide the researchers with an accurate grasp of the direction of the field and provide a reliable basis.

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