scholarly journals Analysis and Data Mining of Call Detail Records using Big Data Technology

IJARCCE ◽  
2016 ◽  
Vol 5 (12) ◽  
pp. 280-283 ◽  
Author(s):  
Nirmal Ghotekar
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Big Data Technology ◽  
Call Detail Records

scholarly journals Underpinnings of Big Data Analytics and Its Applications

2020 ◽  
pp. 75-81
Author(s):  
Anand Kumar Pandey ◽  
Rashmi Pandey ◽  
Ashish Tripathi
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Research Issues ◽  
New Developments ◽  
New Ideas ◽  
Analytical Assessment ◽  
Data Analytic ◽  
Big Data Technology

Big data and Data Mining are co-related to each other and also emphasize the phenomena of extracting and analysis useful data from considerable database. The concept of Big Data analytics plays a very significant role in several fields, such as Data Mining, Education and Training, cloud computing, E-commerce, healthcare and life science, Banking and Agriculture. Big data Analytic is a technique for looking at big set of data to expose hidden patterns. A large amount of data is continuously generated every day using modern information system and technologies. As a result this paper provides a platform to investigate applications of big data at various stages. In future, it come forward to be a required for an analytical assessment of new developments in the big data technology. In addition, it also explores a new and suitable outlook for researchers to expand the solution, based on the literature survey, challenges, new ideas and open research issues.

scholarly journals Big Data Technology Application in Mechanical Intelligent Fault Diagnosis

2021 ◽  
Vol 2066 (1) ◽  
pp. 012064
Author(s):  
Huiteng Cao
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Big Data ◽  
Fault Diagnosis ◽  
Machining Process ◽  
Mechanical Equipment ◽  
Data Mining Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Mining Algorithm ◽  
Big Data Technology

Abstract With the rapid implementation of made in China 2025 plan and the rapid development and application of information technology such as artificial intelligence, big data technology, industrial Internet of things and 5G, information technology has been integrated into every link of the whole life management cycle of mechanical products, such as tool condition detection and mechanical fault diagnosis in machining process. Based on this, the purpose of this study is to study the application of big data technology in mechanical intelligent fault diagnosis. In the process of this study, the decision number algorithm and data mining algorithm are used to study the experiment, and some mechanical faults in the past are analyzed and studied. Summary of the experimental results show that the use of decision number algorithm and data mining algorithm in the experiment has achieved good results, through these methods and big data technology, we can quickly diagnose the fault of mechanical equipment, accurately locate the fault location of mechanical equipment. Mechanical intelligent fault diagnosis based on big data technology can improve the efficiency of fault diagnosis, reduce enterprise costs and improve economic performance.

scholarly journals Application Analysis Based on Big Data Technology in Stroke Rehabilitation Nursing

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
WeiHua Xu ◽  
LiangJin Liu ◽  
JiuXia Zhang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Ischemic Stroke ◽  
Clinical Pathway ◽  
Clinical Pathways ◽  
Treatment Plan ◽  
Science And Technology ◽  
Medical Institution ◽  
Level 3 ◽  
Big Data Technology

According to the statistical analysis, the incidence of stroke disease has gradually increased, particularly in recent years, which poses a huge threat to the safety of human life. Due to the advancement in science and technology specifically big data and sensors, a new research dome known as data mining technology has been introduced, which has the potential value from the perspective of large amount of data analysis. Information has become a new trend of science and technology, and data mining has been used in various application areas to analyze and predict strokes at home and abroad. In this study, big data technology is utilized to collect potential information and explores clinical pathways of level-3 rehabilitation in certain regions of China. Moreover, application effects of data mining in the rehabilitation of patients with the first ischemic stroke have been evaluated and reported. For this purpose, fifty (50) first-time ischemic stroke patients have been screened through big data and were nonartificially assigned to level-3 clinical pathway and conventional rehabilitation groups, respectively, specifically through software. The first group of patients enters the clinical path of the corresponding level according to the way of three-level referral. These patients were analyzed based on the collected results of completing the unified rehabilitation treatment plan of the three-level rehabilitation medical institution in the patient record form. The second group was selected according to the routine rehabilitation model and method of the medical institution where the patients visited were divided into four stages: before treatment, three weeks after treatment, nine weeks after treatment, and seventeen weeks after treatment. For this purpose, a simplified Fugl-Meyer analysis (FMA), recording of various functions of limb movement, and modified Barthel index (MBI) scale were used to analyze and evaluate the ability of daily activities and compare their effects. The final results showed that FMA and MBI scores of the two groups were improved in the three stages after treatment. The FMA and MBI scores of the clinical pathway group on 3rd and 9th weekends were significantly different from those of the conventional rehabilitation group (which is p < 0.05 ). Moreover, difference in FMA and MBI scores between the two at the 17th weekend was not significant. The total cost of the clinical pathway group, particularly at the ninth weekend, was higher than that of the conventional rehabilitation group, but the cost-benefit ratio was better and the incidence of complications was lower than that of the other group.

scholarly journals Research on the Personal Privacy in the Health Big Data

2020 ◽  
Vol 214 ◽  
pp. 03022
Author(s):  
Cangcang JIA ◽  
Han LIU
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Conflicts Of Interest ◽  
Personal Privacy ◽  
Conflict Of Interests ◽  
Data Prediction ◽  
Data Application ◽  
Digital Monitoring ◽  
Big Data Application ◽  
Big Data Technology

Data mining, data prediction and all-round digital monitoring of health big data make the dilemma of personal privacy control prominent. The weakening of the control of personal privacy by big data technology, the people’s data belief, the diversity of interests and the conflict of interests are the main causes of the problem of personal privacy in the context of the application of health big data. Therefore, in the application of health big data, the solution to the problem of personal privacy in the context of health big data application is to enhance the value transparency of big data technology, return and reshape humanism, and explore common values to reduce conflicts of interest.

scholarly journals Application of Association Rule Data Mining in Statistical Analysis of College Students' Mental Health

CONVERTER ◽  
2021 ◽  
pp. 716-724
Author(s):  
Hongrui Zhang
Keyword(s):  
Mental Health ◽  
College Students ◽  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Statistical Data ◽  
Knowledge Map ◽  
Statistical Indicators ◽  
Big Data Technology

Strengthening the application of big data technology in data analysis can effectively improve the service capability and level of relevant statistics, and provide comprehensive and reliable information support for macro decision-making and trend analysis. This paper comprehensively reviews the research status of big data technology in the field of college students' mental health at home and abroad. Combining with the characteristics of college students' mental health statistical data and the weaknesses in statistical analysis, the feasibility of using knowledge mapping technology is demonstrated. On this basis, the blood relationship graph and influence analysis among the statistical indicators of college students' mental health were constructed through the knowledge map. The application of the knowledge map of college students' mental health statistical indicators in statistical data analysis, statistical indicator identification and statistical data quality management is proposed. Specifically, based on the concept of big data, we can establish a decision analysis platform for college students' mental health. Based on the big data technology, the data mining and analysis ability can be enhanced. In addition, it can change the traditional thinking of college students' mental health statistics and strengthen the construction of statistical team.

Challenges and Cloud Computing Environments Towards Big Data

2014 ◽  
pp. 203-208
Author(s):  
Kiran Kumar S V N Madupu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Technology Development ◽  
Computing Environments ◽  
Modern Technologies

Big Data has terrific influence on scientific discoveries and also value development. This paper presents approaches in data mining and modern technologies in Big Data. Difficulties of data mining as well as data mining with big data are discussed. Some technology development of data mining as well as data mining with big data are additionally presented.

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