scholarly journals Statistics Analysis and Visualization for Big Data of E-commerce Platform Sales Evaluation

CONVERTER ◽  
2021 ◽  
pp. 373-390
Author(s):  
Wei Zhan, Jinhui She, Yangyang Zhang, Chenfan Sun
Keyword(s):  
Big Data ◽  
Mobile Phone ◽  
Big Data Analysis ◽  
Unstructured Data ◽  
Evaluation Data ◽  
Visualization System ◽  
Consumer Evaluation ◽  
Integrated Technology ◽  
Hadoop Platform ◽  
Visualization Technology

With the rapid increase in the sales scale of e-commerce platforms is accompanied by the rapid growth of consumer evaluation data on commodities at the same time. How to use big data analysis and visualization technology to mine the valuable information in the massive consumers evaluation data is an urgent issue in promoting the development of e-commerce platforms. However, the amount of e-commerce evaluation data is huge, growing fast, and mostly unstructured data, which is typical big data. In order to efficiently realize the visualization of e-commerce evaluation big data, this paper proposes an end-to-end four-layer framework for data visualization system. The data acquisition layer uses the Webcollector crawler to crawl a total of 420,000 mobile sales evaluation data on the JD website and stores them in the MySQL database; The data import layer uses the Sqoop tool to import MySQL data into the Hadoop platform; The data processing layer uses HDFS and MapReduce to process and analyze big data; The visualization implementation layer uses Jsp+Servelet+JavaScript+echart integrated technology to visualize the big data of distribution of mobile phone sales, user purchase impressions, and user mobile phone portraits. Which helps consumers choose their favorite mobile phones conveniently, and provide decision-making support for e-commerce companies to more accurately launch products, benefiting both parties

Survelliance of Type I and II Diabetic Subjects on Physical Characteristics

2021 ◽  
pp. 308-344
Author(s):  
Rohit Rastogi ◽  
Devendra Kumar Chaturvedi ◽  
Parul Singhal
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Electronic Health Records ◽  
Analysis Data ◽  
Healthcare Systems ◽  
Clinical Analysis ◽  
Big Data Analysis ◽  
Diagnostic Information ◽  
Unstructured Data ◽  
Type I

The Delhi and NCR healthcare systems are rapidly registering electronic health records and diagnostic information available electronically. Furthermore, clinical analysis is rapidly advancing, and large quantities of information are examined and new insights are part of the analysis of this technology experienced as big data. It provides tools for storing, managing, studying, and assimilating large amounts of robust, structured, and unstructured data generated by existing medical organizations. Recently, data analysis data have been used to help provide care. The present study aimed to analyse diabetes with the latest IoT and big data analysis techniques and its correlation with stress (TTH) on human health. The authors have tried to include age, gender, and insulin factor and its correlation with diabetes. Overall, in conclusion, TTH cases increasing with age in case of males and not following the pattern of diabetes variation with age, while in the case of females, TTH pattern variation is the same as diabetes (i.e., increasing trend up to age of 60 then decreasing).

Big Data Analysis

2021 ◽  
pp. 208-233
Author(s):  
Arpit Kumar Sharma ◽  
Arvind Dhaka ◽  
Amita Nandal ◽  
Kumar Swastik ◽  
Sunita Kumari
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analysis ◽  
Knowledge Extraction ◽  
Big Data Analysis ◽  
Unstructured Data ◽  
Data Sets ◽  
Data Handling ◽  
Software And Hardware

The meaning of the term “big data” can be inferred by its name itself (i.e., the collection of large structured or unstructured data sets). In addition to their huge quantity, these data sets are so complex that they cannot be analyzed in any way using the conventional data handling software and hardware tools. If processed judiciously, big data can prove to be a huge advantage for the industries using it. Due to its usefulness, studies are being conducted to create methods to handle the big data. Knowledge extraction from big data is very important. Other than this, there is no purpose for accumulating such volumes of data. Cloud computing is a powerful tool which provides a platform for the storage and computation of massive amounts of data.

Using Term-Based Partitioning Framework on MongoDB and Elastic Search

2014 ◽  
Vol 590 ◽  
pp. 698-701
Author(s):  
Hye Jin Pyo ◽  
Hoon Jeong ◽  
Nan Ju Kim ◽  
Eui In Choi
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Full Advantage ◽  
Strong Consistency ◽  
Big Data Analysis ◽  
Unstructured Data ◽  
Weak Points ◽  
New Framework

It's a major issue that how can find worthy information in big data. Because big datacan be used in company's success according how to take full advantage of big data analysis. Currently, search technologies aboutbeing stored distributed and duplicated data does not need to strong consistency. Therefore, nowadays we utilize variety of storage based on NoSQL for allowing loosens of strict consistency. MongoDB and Elastic Search have been focused of search and store unstructured data. But they have weak points. So, in this paper, we are going to propose new framework using term-based partitioning which can make up MongoDB and Elastic Search’s limitations.

scholarly journals Big Data Analytics for Improved Care Delivery in the Healthcare Industry

2019 ◽  
Vol 15 (10) ◽  
pp. 40
Author(s):  
Gunasekar Thangarasu ◽  
Kayalvizhi Subramanian
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Care Delivery ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Healthcare Systems ◽  
Big Data Analysis ◽  
Unstructured Data ◽  
Effective Practices ◽  
Healthcare Industry

<p class="0abstract">The big data analytics plays a pivotal role in the field of healthcare services and research to facilitate better service to the patients. It has provided tools to accumulate, manage, analysis the structured and unstructured data produced by the healthcare systems. Recently the utilization of big data analytics has been increased in the healthcare industry for assisting the process of diagnosing diseases and care delivery. However, the adoption and research development of big data analysis in the healthcare industry is still slow down due to facing some fundamental problems inherent within the big data paradigm. In this study, addresses these problems which focus on the upcoming and promising areas of medical research and proposed a novel big data analytics approach using Apache Spark. The proposed approach will improve care delivery in the healthcare industry. Big data analytics can continually evaluate clinical data in order to improve the effective practices of physicians and improved patient care.</p>

Correlating Personality and Actual Phone Usage

2014 ◽  
Vol 35 (3) ◽  
pp. 158-165 ◽  
Author(s):  
Christian Montag ◽  
Konrad Błaszkiewicz ◽  
Bernd Lachmann ◽  
Ionut Andone ◽  
Rayna Sariyska ◽  
...  
Keyword(s):  
Big Data ◽  
Social Network ◽  
Mobile Phone ◽  
Mobile Phones ◽  
Self Report ◽  
Psychological Variables ◽  
New Approach ◽  
Report Data ◽  
Self Report Data ◽  
Voice Calls

In the present study we link self-report-data on personality to behavior recorded on the mobile phone. This new approach from Psychoinformatics collects data from humans in everyday life. It demonstrates the fruitful collaboration between psychology and computer science, combining Big Data with psychological variables. Given the large number of variables, which can be tracked on a smartphone, the present study focuses on the traditional features of mobile phones – namely incoming and outgoing calls and SMS. We observed N = 49 participants with respect to the telephone/SMS usage via our custom developed mobile phone app for 5 weeks. Extraversion was positively associated with nearly all related telephone call variables. In particular, Extraverts directly reach out to their social network via voice calls.

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