scholarly journals Activity Pattern Mining from Social Media for Healthcare Monitoring on Big data

2019 ◽  
Vol 8 (9S3) ◽  
pp. 527-530
Keyword(s):  
Social Media ◽  
Big Data ◽  
Activity Pattern ◽  
Pattern Mining ◽  
Medical Practitioner ◽  
Time Data ◽  
Community Healthcare ◽  
Big Data Applications ◽  
Technological Developments ◽  
Analytical Tools

Big data applications introduce novel openings for establishinginnovative information and produce differentadvanced methods to improve the worth of healthcare.In this paper, a novel activity pattern mining from social media for healthcare to examine big data applications in different biomedical multi-disciplines such as bioinformatics, medical imaging and community healthcare applications.Big data analytical tools perform the key part in their task for extracting hidden behavioural and expressive patterns frompersonal messages and their tweets. The behavioural patterns of the users can realizetheir additional informations about their concealed feelings and sentiments[1],[ 3],[5]. Further, the neural network is modelled to predict the psychological informations, such as nervousness, depression, behavioural disorder and mental stress.This is also shows that integrating variety of sources of data enables medical practitioner to show a novel investigation of patient care processes, improvements in new mobile healthcare technological developments aid real-time data collection, archiving and analysis of data in distributed environments

scholarly journals ANALYSIS ON COGNITIVE SOCIAL DATA FUSION

2020 ◽  
pp. 27-30
Author(s):  
Anandakumar H ◽  
Tamilselvan T ◽  
Nandni S ◽  
Subashree R ◽  
Vinodhini E
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Fusion ◽  
Data Processing ◽  
Pattern Mining ◽  
Data Representation ◽  
Map Reduce ◽  
User Interest ◽  
Social Data ◽  
Algorithm Mapping

Big data stands for effective handling of large amount of data, research, mining, intelligence. In social media large amount of data uploaded every.Social media handle large amount of data like photo, video, songs and so many using big data. When it comes for big data, a large amount of data should be effectively handled. Big data face various challenges like clustering of data, visualizing, data representation, data processing, pattern mining, tracking of data and analysing behaviour of users. In this paper the Emoji in messages are decoded and Unicode will be set. Based on the Emoji the user interest can be understood in a better way. Then another part involves the replacement of repeated data by using the map Reduce algorithm. Mapping of data with key values used to reduce the size of storage.

Resource Provisioning and Scheduling of Big Data Processing Jobs

2018 ◽  
pp. 382-401
Author(s):  
Rajni Aron ◽  
Deepak Kumar Aggarwal
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Research Area ◽  
Resource Provisioning ◽  
Time Data ◽  
Big Data Processing ◽  
Cloud Resource Management ◽  
Big Data Applications ◽  
Cloud Resource

Cloud Computing has become a buzzword in the IT industry. Cloud Computing which provides inexpensive computing resources on the pay-as-you-go basis is promptly gaining momentum as a substitute for traditional Information Technology (IT) based organizations. Therefore, the increased utilization of Clouds makes an execution of Big Data processing jobs a vital research area. As more and more users have started to store/process their real-time data in Cloud environments, Resource Provisioning and Scheduling of Big Data processing jobs becomes a key element of consideration for efficient execution of Big Data applications. This chapter discusses the fundamental concepts supporting Cloud Computing & Big Data terms and the relationship between them. This chapter will help researchers find the important characteristics of Cloud Resource Management Systems to handle Big Data processing jobs and will also help to select the most suitable technique for processing Big Data jobs in Cloud Computing environment.

Exploring the Usage of Big Data Analytical Tools in Telecommunication Industry in Oman

Web Services ◽  
2019 ◽  
pp. 459-472
Author(s):  
Himyar Ali Al Jabri ◽  
Ali H. Al-Badi ◽  
Oualid Ali
Keyword(s):  
Information Technology ◽  
Social Media ◽  
Big Data ◽  
Data Management ◽  
Competitive Advantage ◽  
Case Studies ◽  
Telecommunication Industry ◽  
A Value ◽  
Telecom Industry ◽  
Analytical Tools

Big Data has recently become a very hot topic in the field of Information Technology and Data Management. Data generated by the company's daily operations through different resources such as social media, etc. is very important because it can bring a value that will lead to a competitive advantage. The objectives of this research are to: 1) Explore the analytical tools used to manipulate Big Data in Omani telecom industry, 2) Present the benefits of using these tools, the extent of use, and the features specifically promoted these tools, and 3) Highlight the challenges/obstacles that the telecom industry in Oman facing in adopting/using Big Data analytical tools. To achieve the research objectives two case studies were conducted among the main telecom operators in Oman. This research concluded that both studied telecom operators in Oman are not ready for the DBAs. Both operators need to invest in developing the capabilities that enable them to use these tools. Once that is satisfied, then other components like the infrastructure, tools, and data can be managed very well.

Big Data Applications and Architectures for Emerging Countries

2021 ◽  
pp. 120-142
Author(s):  
Anil K. Maheshwari
Keyword(s):  
Social Media ◽  
Big Data ◽  
Special Needs ◽  
Emerging Countries ◽  
Healthcare Sector ◽  
Healthcare Applications ◽  
Big Data Applications ◽  
Big Data Technologies

Exponential increases in generation of data, especially through social media, has found an increased influence in society over the last decade. This chapter provides an overview of big data technologies and architectures and how this data could be applied to meet the special needs of the emerging societies. Healthcare applications are most important, especially for the rural and the marginal sections of society. This chapter lays out architecture designs of 10 big data applications with half of them relating to the healthcare sector. These designs can be seeds for the implementation of other imaginative beneficial big data applications.

scholarly journals Heritage-based tribalism in Big Data ecologies: Deploying origin myths for antagonistic othering

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110033
Author(s):  
Chiara Bonacchi ◽  
Marta Krzyzanska
Keyword(s):  
Social Media ◽  
Big Data ◽  
Qualitative Methods ◽  
Methodological Framework ◽  
Human Origin ◽  
Data Intensive ◽  
The Past ◽  
Origin Myths ◽  
People’S Attitudes ◽  
Analytical Tools

This article presents a conceptual and methodological framework to study heritage-based tribalism in Big Data ecologies by combining approaches from the humanities, social and computing sciences. We use such a framework to examine how ideas of human origin and ancestry are deployed on Twitter for purposes of antagonistic ‘othering’. Our goal is to equip researchers with theory and analytical tools for investigating divisive online uses of the past in today’s networked societies. In particular, we apply notions of heritage, othering and neo-tribalism, and both data-intensive and qualitative methods to the case of people’s engagements with the news of Cheddar Man’s DNA on Twitter. We show that heritage-based tribalism in Big Data ecologies is uniquely shaped as an assemblage by the coalescing of different forms of antagonistic othering. Those that co-occur most frequently are the ones that draw on ‘Views on Race’, ‘Trust in Experts’ and ‘Political Leaning’. The framings of the news that were most influential in triggering heritage-based tribalism were introduced by both right- and left-leaning newspaper outlets and by activist websites. We conclude that heritage-themed communications that rely on provocative narratives on social media tend to be labelled as political and not to be conducive to positive change in people’s attitudes towards issues such as racism.

scholarly journals TOWARDS EFFECTIVE CUSTOMER RELATIONSHIP MANAGEMENT IN OMAN: ROLE OF BIG DATA

2017 ◽  
Vol 5 (10) ◽  
pp. 92-100
Author(s):  
Tarek Khalil ◽  
Al-Refai Mohammad ◽  
Amer Nizar Fayez ◽  
SharafQudah Mohammed
Keyword(s):  
Big Data ◽  
Customer Relationship Management ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Relationship Management ◽  
Qualitative Evaluation ◽  
Customer Relationship ◽  
Customer Engagement ◽  
Time Data ◽  
Analytical Tools

We established a framework to explore the feasibility of enabling big data within the customer relationship management (CRM) strategies in Oman for creating sustainable business profit nationwide. A qualitative evaluation was made based on predictive analytics convergence and big data facilitated CRM. It was found that the big data analytics can meticulously alter the competitive industrial setting, and thereby proffered notable benefits to the business organization in terms of operation, strategies, and competitiveness. Results revealed that companies must introduce analytical tools, real-time data, and hire talented as well as skilled employees to improve the productivity in consistent with the new business model. Furthermore, depending on the customer engagement, an assemblage and analysis of enormous data volume together with analytical tools was discerned to assist companies towards efficient resource allocation and capital spending. The implications of using big data for CRM in Oman and way forward were emphasized.

scholarly journals VARTTA: A Visual Analytics System for Making Sense of Real-Time Twitter Data

Data ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 20
Author(s):  
Amir Haghighati ◽  
Kamran Sedig
Keyword(s):  
Social Media ◽  
Real Time ◽  
Visual Analytics ◽  
Time Data ◽  
Making Sense ◽  
Twitter Data ◽  
Social Media Platforms ◽  
Media Platform ◽  
Analytical Tools ◽  
Data Visualizations

Through social media platforms, massive amounts of data are being produced. As a microblogging social media platform, Twitter enables its users to post short updates as “tweets” on an unprecedented scale. Once analyzed using machine learning (ML) techniques and in aggregate, Twitter data can be an invaluable resource for gaining insight into different domains of discussion and public opinion. However, when applied to real-time data streams, due to covariate shifts in the data (i.e., changes in the distributions of the inputs of ML algorithms), existing ML approaches result in different types of biases and provide uncertain outputs. In this paper, we describe VARTTA (Visual Analytics for Real-Time Twitter datA), a visual analytics system that combines data visualizations, human-data interaction, and ML algorithms to help users monitor, analyze, and make sense of the streams of tweets in a real-time manner. As a case study, we demonstrate the use of VARTTA in political discussions. VARTTA not only provides users with powerful analytical tools, but also enables them to diagnose and to heuristically suggest fixes for the errors in the outcome, resulting in a more detailed understanding of the tweets. Finally, we outline several issues to be considered while designing other similar visual analytics systems.

scholarly journals Advances in Big Data Applications for transportation: airline, highway, and railway

2021 ◽  
Vol 5 (2) ◽  
pp. 121-134
Author(s):  
Babek Erdebilli ◽  
Emine Nur NACAR
Keyword(s):  
Big Data ◽  
Design Research ◽  
Real Life ◽  
Structural Data ◽  
Added Value ◽  
Data Types ◽  
Time Data ◽  
Life Problems ◽  
Big Data Applications ◽  
Transportation Sector

Aim: The purpose of this article is to present the latest advances in big data applications in the industries of the transportation sector such as airline, highway, and railway. It is difficult to analyze data in transportation because there is continuous real-time data flow. Since the improvements made are fast with the same logic, it is necessary to catch up with the new developments. Data should be analyzed with the big data concept because data stacks highly contain non-structural data types in transportation data. Although the mentioned industries are complementary to each other, the applications differ depending on the needs of the industry. Thus, solutions to specific problems in different industries using big data applications should be addressed. Design / Research methods: In accordance with the purpose of the study, big data studies that provide added value to the transportation sector were examined. Studies have been filtered through some criteria which are whether the application is adaptable to the industry, the study is available online in full-text, and its references are from respectable sources.   Conclusions / findings: All the big data application studies in the academy are not adaptable in real-life problems or suitable for all situations. For this reason, trying all of the applications will lead to moral and material losses for firms. This study is a guideline for companies to follow the developments in the big data concept and to choose the one that suits their problems. Thus, the gap between academia and industry was tried to close. Originality / value of the article: Although studies are referring to big data applications in the transportation sector, this study differs from others in terms of specifically analyzing big data applications in different industries such as airline, highway, and railway in the transportation sector

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