Big Data, HIPAA, and the Common Rule

2018 ◽  
pp. 251-264 ◽  
Author(s):  
Margaret Foster Riley
Keyword(s):  
Big Data ◽  
Common Rule ◽  
The Common

Sentiment Analysis with Text Mining in Contexts of Big Data

2017 ◽  
Vol 13 (3) ◽  
pp. 47-67 ◽  
Author(s):  
Carina Sofia Andrade ◽  
Maribel Yasmina Santos
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Decision Making Process ◽  
Practical Applications ◽  
Information Models ◽  
Evolution Of Technology ◽  
The Common ◽  
Analyse Data

The evolution of technology, along with the common use of different devices connected to the Internet, provides a vast growth in the volume and variety of data that are daily generated at high velocity, phenomenon commonly denominated as Big Data. Related with this, several Text Mining techniques make possible the extraction of useful insights from that data, benefiting the decision-making process across multiple areas, using the information, models, patterns or tendencies that these techniques are able to identify. With Sentiment Analysis, it is possible to understand which sentiments and opinions are implicit in this data. This paper proposes an architecture for Sentiment Analysis that uses data from the Twitter, which is able to collect, store, process and analyse data on a real-time fashion. To demonstrate its utility, practical applications are developed using real world examples where Sentiment Analysis brings benefits when applied. With the presented demonstration case, it is possible to verify the role of each used technology and the techniques adopted for Sentiment Analysis.

scholarly journals Abnormal Detection to Big Data Using Deep Neural Networks

2021 ◽  
Vol 2068 (1) ◽  
pp. 012025
Author(s):  
Jian Zheng ◽  
Zhaoni Li ◽  
Jiang Li ◽  
Hongling Liu
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Risk Model ◽  
Small Sample ◽  
Small Samples ◽  
Small Data ◽  
Data Segmentation ◽  
Sample Data ◽  
Segmentation Algorithms ◽  
The Common

Abstract It is difficult to detect the anomalies in big data using traditional methods due to big data has the characteristics of mass and disorder. For the common methods, they divide big data into several small samples, then analyze these divided small samples. However, this manner increases the complexity of segmentation algorithms, moreover, it is difficult to control the risk of data segmentation. To address this, here proposes a neural network approch based on Vapnik risk model. Firstly, the sample data is randomly divided into small data blocks. Then, a neural network learns these divided small sample data blocks. To reduce the risks in the process of data segmentation, the Vapnik risk model is used to supervise data segmentation. Finally, the proposed method is verify on the historical electricity price data of Mountain View, California. The results show that our method is effectiveness.

Big Data Analysis for Cardiovascular Diseases

2018 ◽  
pp. 102-119
Author(s):  
Miguel A. Sánchez-Acevedo ◽  
Zaydi A. Acosta-Chí ◽  
Beatriz A. Sabino-Moxo ◽  
José A. Márquez-Domínguez ◽  
Rosa M. Canton-Croda
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Medical Devices ◽  
Clinical Analysis ◽  
Big Data Analysis ◽  
Health Issues ◽  
Research Papers ◽  
Healthcare Data ◽  
Common Risk Factors ◽  
The Common

In the healthcare field, plenty of clinical data is generated every day from patient records, surveys, research papers, medical devices, among others sources. These data can be exploited to discover new insights about health issues. For helping decision makers and healthcare data managers, a survey of research works and tools covering the process of handling big data in the healthcare field is included. A methodology for CVD prevention, detection and management through the use of tools for big data analysis is proposed. Also, it is important to maintain privacy of patients when handling healthcare data; therefore, a list of recommendations for maintaining privacy when handling healthcare data is presented. Specific clinical analysis are recommended on those regions where the incidence rate of CVD is high, but a weak relation with the common risk factors is observed according to historical data. Finally, challenges which need to be addressed are presented.

The Components of Big Data and Knowledge Management Will Change Radically How People Collaborate and Develop Complex Research

2019 ◽  
pp. 241-257
Author(s):  
Amitava Choudhury ◽  
Ambika Aggarwal ◽  
Kalpana Rangra ◽  
Ashutosh Bhatt
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Knowledge Management ◽  
Competitive Environment ◽  
Economic Success ◽  
Common Goal ◽  
The Common ◽  
Entire World ◽  
Amount Of Knowledge

Emerging as a rapidly growing field, big data is already known for promising success and having considerable synergies with knowledge management. The common goal of this collaboration is to improve and facilitate decision making, fueling the competition, fostering innovation, and achieving economic success through acquisition of knowledge to various applications. Knowledge in the entire world or inside any organization has already expanded itself in various directions and is exponentially increasing with time. To withstand the current competitive environment, an intensive collaboration of knowledge management with different approaches and algorithms of big data is required. Classical structuring is becoming obsolete with the increasing amount of knowledge components.

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