Extracting Relevant Information from Big Data to Anticipate Forced Migration

2017 ◽  
pp. 71-78
Author(s):  
Jiashu Zhao ◽  
Susan McGrath ◽  
Jimmy Xiangji Huang ◽  
Jianhong Wu ◽  
Shicheng Wu
Keyword(s):  
Big Data ◽  
Forced Migration ◽  
Relevant Information

Big Data Cognitive Storage: An Optimum Way of handling the Educational Data in Indian Scenario

2017 ◽  
Vol SED2017 (01) ◽  
pp. 5-7
Author(s):  
Ruchi Jain ◽  
Neelesh Kumar Jain
Keyword(s):  
Big Data ◽  
Online Learning ◽  
Online Education ◽  
Data Protection ◽  
Teaching And Learning ◽  
Relevant Information ◽  
Indian Society ◽  
Digital Learning ◽  
Modern Education ◽  
Indian Scenario

The concept of big data has been incorporated in majority of areas. The educational sector has plethora of data especially in online education which plays a vital in modern education. Moreover digital learning which comprises of data and analytics contributes significantly to enhance teaching and learning. The key challenge for handling such data can be a costly affair. IBM has introduced the technology "Cognitive Storage" which ensures that the most relevant information is always on hand. This technology governs the incoming data, stores the data in definite media, application of levels of data protection, policies for the lifecycle and retention of different classes of data. This technology can be very beneficial for online learning in Indian scenario. This technology will be very beneficial in Indian society so as to store more information for the upliftment of the students’ knowledge.

scholarly journals An Approach for Big Data to Evolve the Auspicious Information from Cross-Domains

2017 ◽  
Vol 7 (2) ◽  
pp. 967 ◽  
Author(s):  
Preeti Arora ◽  
Deepali Virmani ◽  
P.S. Kulkarni
Keyword(s):  
Big Data ◽  
Sentiment Analysis ◽  
Relevant Information ◽  
Domain Analysis ◽  
The Self ◽  
The Other ◽  
Classification Approach ◽  
Cross Domain ◽  
The Cross

Sentiment analysis is the pre-eminent technology to extract the relevant information from the data domain. In this paper cross domain sentimental classification approach Cross_BOMEST is proposed. Proposed approach will extract <strong>†</strong>ve words using existing BOMEST technique, with the help of Ms Word Introp, Cross_BOMEST determines <strong>†</strong>ve words and replaces all its synonyms to escalate the polarity and blends two different domains and detects all the self-sufficient words. Proposed Algorithm is executed on Amazon datasets where two different domains are trained to analyze sentiments of the reviews of the other remaining domain. Proposed approach contributes propitious results in the cross domain analysis and accuracy of 92 % is obtained. Precision and Recall of BOMEST is improved by 16% and 7% respectively by the Cross_BOMEST.

scholarly journals Novel IT Technologies on the Digital Battlefield: The Application of Big Data and Data Mining Technologies

Hadmérnök ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. 141-158
Author(s):  
Eszter Katalin Bognár
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Processing ◽  
Relevant Information ◽  
Military Operations ◽  
Textual Data ◽  
Modern Warfare ◽  
Tools And Techniques

In modern warfare, the most important innovation to date has been the utilisation of information as a  weapon. The basis of successful military operations is  the ability to correctly assess a situation based on  credible collected information. In today’s military, the primary challenge is not the actual collection of data.  It has become more important to extract relevant  information from that data. This requirement cannot  be successfully completed without necessary  improvements in tools and techniques to support the acquisition and analysis of data. This study defines  Big Data and its concept as applied to military  reconnaissance, focusing on the processing of  imagery and textual data, bringing to light modern  data processing and analytics methods that enable  effective processing.

scholarly journals Territorial and Digital Borders and Migrant Vulnerability Under a Pandemic Crisis

2021 ◽  
pp. 45-64
Author(s):  
Petra Molnar
Keyword(s):  
Decision Making ◽  
Human Rights ◽  
Big Data ◽  
Private Sector ◽  
Guinea Pigs ◽  
Technological Development ◽  
Forced Migration ◽  
Population Movements ◽  
Humanitarian Crises ◽  
Automated Decision Making

AbstractPeople on the move are often left out of conversations around technological development and become guinea pigs for testing new surveillance tools before bringing them to the wider population. These experiments range from big data predictions about population movements in humanitarian crises to automated decision-making in immigration and refugee applications to AI lie detectors at European airports. The Covid-19 pandemic has seen an increase of technological solutions presented as viable ways to stop its spread. Governments’ move toward biosurveillance has increased tracking, automated drones, and other technologies that purport to manage migration. However, refugees and people crossing borders are disproportionately targeted, with far-reaching impacts on various human rights. Drawing on interviews with affected communities in Belgium and Greece in 2020, this chapter explores how technological experiments on refugees are often discriminatory, breach privacy, and endanger lives. Lack of regulation of such technological experimentation and a pre-existing opaque decision-making ecosystem creates a governance gap that leaves room for far-reaching human rights impacts in this time of exception, with private sector interest setting the agenda. Blanket technological solutions do not address the root causes of displacement, forced migration, and economic inequality – all factors exacerbating the vulnerabilities communities on the move face in these pandemic times.

Statistical Features for Extractive Automatic Text Summarization

2020 ◽  
pp. 619-637
Author(s):  
Yogesh Kumar Meena ◽  
Dinesh Gopalani
Keyword(s):  
Big Data ◽  
Performance Measures ◽  
Relevant Information ◽  
Text Summarization ◽  
Statistical Features ◽  
Information Need ◽  
Evaluation Measures ◽  
Large Size ◽  
Automatic Text Summarization ◽  
Automatic Text

Automatic Text Summarization (ATS) enables users to save their precious time to retrieve their relevant information need while searching voluminous big data. Text summaries are sensitive to scoring methods, as most of the methods requires to weight features for sentence scoring. In this chapter, various statistical features proposed by researchers for extractive automatic text summarization are explored. Features that perform well are termed as best features using ROUGE evaluation measures and used for creating feature combinations. After that, best performing feature combinations are identified. Performance evaluation of best performing feature combinations on short, medium and large size documents is also conducted using same ROUGE performance measures.

Big Data Paradigm for Healthcare Sector

2016 ◽  
pp. 169-186
Author(s):  
Jyotsna Talreja Wassan
Keyword(s):  
Health Care ◽  
Big Data ◽  
Electronic Health Records ◽  
Real Time ◽  
Real Data ◽  
Relevant Information ◽  
Healthcare Sector ◽  
Clinical Environment ◽  
Time Dynamic ◽  
Health Care Records

The digitization of world in various areas including health care domain has brought up remarkable changes. Electronic Health Records (EHRs) have emerged for maintaining and analyzing health care real data online unlike traditional paper based system to accelerate clinical environment for providing better healthcare. These digitized health care records are form of Big Data, not because of the fact they are voluminous but also they are real time, dynamic, sporadic and heterogeneous in nature. It is desirable to extract relevant information from EHRs to facilitate various stakeholders of the clinical environment. The role, scope and impact of Big Data paradigm on health care is discussed in this chapter.

Reinventing Water-Landscape Monitoring and Management in the Age of Geoenabled Environmental Sensor Webs and Social Networks

2017 ◽  
Author(s):  
Demetrio P. Zourarakis
Keyword(s):  
Big Data ◽  
Relevant Information ◽  
Water Systems ◽  
The Internet ◽  
The Status ◽  
Landscape Monitoring ◽  
Environmental Sensor ◽  
Sensor Information ◽  
Past Experiences ◽  
The Internet Of Things

Future humans interacting with water in Kentucky will bring to their experience not only the panoply of expectations, assumptions, background knowledge, and past experiences but also ultra-smart gadgetry which will shape the outcome of the event. The technoscapes inhabited by human communities and individuals are over imposed on the natural rhythms which hydrology obeys, providing opportunities for sensorial fusion. An ongoing evolutionary explosion in diversity, mobility and interconnectedness of sensors is manifesting itself as the Internet of Things, all denizens of the “Cloud”, allowing the citizen scientist to easily generate georeferenced sensor information. This augmented, hybrid sensorial ecosystem challenges us to rethink how we tap into big data, mostly unstructured, representing the status of water systems, and how we extract relevant information.

P4344An example that big data analysis is ready for the prime time

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
C Cortina ◽  
M Sarrion ◽  
L Mora ◽  
V Suberviola ◽  
C Beltran ◽  
...  
Keyword(s):  
Big Data ◽  
Heart Disease ◽  
Data Analysis ◽  
Mitral Regurgitation ◽  
Aortic Stenosis ◽  
Aortic Regurgitation ◽  
Relevant Information ◽  
Big Data Analysis ◽  
Chronic Obstructive ◽  
Total Prevalence

Abstract Introduction Data about the epidemiology of valvular heart disease (VHD) is scarce. The increasing aging of the population may cause an augmented prevalence of VHD, with a great number of comorbidities that conveys a higher surgical risk. The aim of this study was to describe the prevalence of VHD in the patients attended at our institution from 2007 until 2017 and to describe the main characteristics of this population. Methods We used a new tool based on EHRead Technology to extract clinical relevant information from Electronic Health Records, designed for descriptive and predictive big data analysis. All medical reports generated at the outpatient clinic, ER or hospitalization ward were examined. Patients with a diagnosis of moderate or severe VHD were selected. The prevalence of VHD was also estimated in 2 quintiles, from 2008 until Feb 2013 and from March 2013 until Dec 2017. Results The total prevalence of VHD in our population was 1.04% (n=3431). Mitral regurgitation was the most frequent valvular lesion (0.4%, n=1318), followed by aortic stenosis (0.3%, n=967) and aortic regurgitation (0.28%, n=938). There was a clear female predominance (63%), and the median age was 76.4. In the 1st quintile the prevalence of VHD was 0.25%, and increased to 0.79% in the 2nd. This trend was consistent in all type of valvular lesions. The prevalence of comorbidities was higher than in other epidemiological studies (Table). Prevalence of comorbidities Severe MR Severe AS Severe AR Euro Heart Valve Survey Hypertension 54,5% 69,1% 47,9% 49% Dyslipidemia 32,2% 40,6% 27,4% 35% Diabetes Mellitus 28,0% 31,5% 16,4% 15% Smoking (current) 5,6% 5,4% 13,7% 39% Coronary heart disease 12,0% 17,0% 12,3% 13% Stroke 7,0% 8,9% 5,5% 7% Chronic kidney disease 18,9% 16,9% 20,5% 15% Chronic obstructive pulmonary disease 11,2% 9,9% 11,0% 15% MR: Mitral regurgitation, AS: aortic stenosis, AR: aortic regurgitation, MS: mitral stenosis. Sex Distribution Conclusions The older age and greater number of comorbidities seen in our series over the past ten years, compared to the Euroheart Valve Survey reinforce the idea that the percutaneous valvular therapies should play a major role in the treatment of patients with VHD. Although, the prevalence of VHD may be underestimated in our population, due to the methodology, it reflects an ever-growing pathology in an older and sicker population.

scholarly journals Industry 4.0 Contribution to Asset Management in the Electrical Industry

2021 ◽  
Vol 13 (18) ◽  
pp. 10369
Author(s):  
Gabrielle Biard ◽  
Georges Abdul Nour
Keyword(s):  
Big Data ◽  
Asset Management ◽  
Smart Grids ◽  
Industry 4.0 ◽  
Current Model ◽  
Relevant Information ◽  
Point Of View ◽  
Electrical Networks ◽  
Use Of Resources ◽  
Technological Developments

Industry 4.0 has revolutionized paradigms by leading to major technological developments in several sectors, including the energy sector. Aging equipment fleets and changing demand are challenges facing electricity companies. Forced to limit resources, these organizations must question their method and the current model of asset management (AM). The objective of this article is to detail how industry 4.0 can improve the AM of electrical networks from a global point of view. To do so, the industry 4.0 tools will be presented, as well as a review of the literature on their application and benefits in this area. From the literature review conducted, we observe that once properly structured and managed, big data forms the basis for the implementation of advanced tools and technologies in electrical networks. The data generated by smart grids and data compiled for several years in electrical networks have the characteristics of big data. Therefore, it leaves room for a multitude of possibilities for comprehensive analysis and highly relevant information. Several tools and technologies, such as modeling, simulation as well as the use of algorithms and IoT, combined with big data analysis, leads to innovations that serve a common goal. They facilitate the control of reliability-related risks, maximize the performance of assets, and optimize the intervention frequency. Consequently, they minimize the use of resources by helping decision-making processes.

Statistical Features for Extractive Automatic Text Summarization

2016 ◽  
pp. 126-144
Author(s):  
Yogesh Kumar Meena ◽  
Dinesh Gopalani
Keyword(s):  
Big Data ◽  
Performance Measures ◽  
Relevant Information ◽  
Text Summarization ◽  
Statistical Features ◽  
Information Need ◽  
Evaluation Measures ◽  
Large Size ◽  
Automatic Text Summarization ◽  
Automatic Text

Automatic Text Summarization (ATS) enables users to save their precious time to retrieve their relevant information need while searching voluminous big data. Text summaries are sensitive to scoring methods, as most of the methods requires to weight features for sentence scoring. In this chapter, various statistical features proposed by researchers for extractive automatic text summarization are explored. Features that perform well are termed as best features using ROUGE evaluation measures and used for creating feature combinations. After that, best performing feature combinations are identified. Performance evaluation of best performing feature combinations on short, medium and large size documents is also conducted using same ROUGE performance measures.

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