scholarly journals Applications of Big Data in the Digital India: Opportunities and Challenges

2016 ◽  
Vol 3 (3) ◽  
Author(s):  
Vinay Kumar ◽  
Arpana Chaturvedi
Keyword(s):  
Big Data ◽  
Social Networking ◽  
Exponential Growth ◽  
Social Networking Sites ◽  
Unstructured Data ◽  
Threat Perception ◽  
Data Repository ◽  
Huge Amount ◽  
Real Challenge ◽  
Area Of Application

<div><p><em>With the advent of Social Networking Sites (SNS), volumes of data are generated daily. Most of these data are multimedia type and unstructured with exponential growth. This exponential growth of variety, volume and complexity of structured and unstructured data leads to the concept of big data. Managing big data and harnessing its benefits is a real challenge. With increase in access to big data repository for various applications, security and access control is another aspect that needs to be considered while managing big data. We have discussed area of application of big data, opportunities it provides and challenges that we face in the managing such huge amount of data for various applications. Issues related to security against different threat perception of big data are also discussed. </em></p></div>

scholarly journals A Web-Based Framework for Classification and Integration of News Articles Big Data using Ontology

2019 ◽  
Vol 8 (3) ◽  
pp. 3257-3263
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Semantic Web ◽  
Social Networking ◽  
Social Networking Sites ◽  
Climate Information ◽  
Huge Amount ◽  
Web Based ◽  
Different Sources

Around 2.5 quintillion bytes of data have been created online: out of which most of the data has been generated in the last two years. To generate this huge amount of data from different sources, many devices are being utilized such as sensors to get the data about climate information, social networking sites, banking records, e-commerce records, etc. This data is known as Big Data. It mainly consists of three 3v’s volumes, velocity, and variety. Variety of data discusses about different formats of data originating from various data foundations. Hence, the big data variety’s issue is significant in explaining some genuine challenges. The semantic Web is utilized as an Integrator to join information from different sorts of data foundations like web services, social databases, and spreadsheets and so on and in various formats. The semantic Web is an all-encompassing type of the present web that gives simpler methods to look, reuse, join and offer the data. In this manner, it is along these lines seen as a combiner transversely over different things, information applications, and systems. This paper is an effort to uncover the nature of big data and a brief survey on the use of various semantic web-based methods and tools to add value to today’s big data. In addition, it discusses a case study on performing various machine learning functionalities on news articles and proposes a web-based framework for classification and integration of news articles big data using ontologies.

scholarly journals A Survey on Analysis of Twitter Opinion Mining Using Sentiment Analysis

2019 ◽  
pp. 537-542
Author(s):  
Vishnu VardanReddy ◽  
Mahesh Maila ◽  
Sai Sri Raghava ◽  
Yashwanth Avvaru ◽  
Sri. V. Koteswarao
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Opinion Mining ◽  
Online Communication ◽  
Valuable Insight ◽  
Huge Amount ◽  
Twitter Data ◽  
Product Service ◽  
Different Types

In recent years, there is a rapid growth in online communication. There are many social networking sites and related mobile applications, and some more are still emerging. Huge amount of data is generated by these sites everyday and this data can be used as a source for various analysis purposes. Twitter is one of the most popular networking sites with millions of users. There are users with different views and varieties of reviews in the form of tweets are generated by them. Nowadays Opinion Mining has become an emerging topic of research due to lot of opinionated data available on Blogs & social networking sites. Tracking different types of opinions & summarizing them can provide valuable insight to different types of opinions to users who use Social networking sites to get reviews about any product, service or any topic. Analysis of opinions & its classification on the basis of polarity (positive, negative, neutral) is a challenging task. Lot of work has been done on sentiment analysis of twitter data and lot needs to be done. In this paper we discuss the levels, approaches of sentiment analysis, sentiment analysis of twitter data, existing tools available for sentiment analysis and the steps involved for same. Two approaches are discussed with an example which works on machine learning and lexicon based respectively.

Need for Dynamicity in Social Networking Sites

2014 ◽  
pp. 64-87
Author(s):  
Gurdeep S Hura
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Social Networking ◽  
Real World ◽  
Social Networking Sites ◽  
Data Science ◽  
Emerging Technology ◽  
New Applications ◽  
Future Status

This chapter presents this new emerging technology of social media and networking with a detailed discussion on: basic definitions and applications, how this technology evolved in the last few years, the need for dynamicity under data mining environment. It also provides a comprehensive design and analysis of popular social networking media and sites available for the users. A brief discussion on the data mining methodologies for implementing the variety of new applications dealing with huge/big data in data science is presented. Further, an attempt is being made in this chapter to present a new emerging perspective of data mining methodologies with its dynamicity for social networking media and sites as a new trend and needed framework for dealing with huge amount of data for its collection, analysis and interpretation for a number of real world applications. A discussion will also be provided for the current and future status of data mining of social media and networking applications.

scholarly journals Big Data and MapReduce Challenges, Opportunities and Trends

2016 ◽  
Vol 6 (6) ◽  
pp. 2911
Author(s):  
Sachin Arun Thanekar ◽  
K. Subrahmanyam ◽  
A. B. Bagwan
Keyword(s):  
Big Data ◽  
High Velocity ◽  
Structured Data ◽  
Unstructured Data ◽  
Map Reduce ◽  
Huge Amount ◽  
Commodity Hardware ◽  
Survey Paper ◽  
Recent Trends ◽  
Large Clusters

<p>Nowadays we all are surrounded by Big data. The term ‘Big Data’ itself indicates huge volume, high velocity, variety and veracity i.e. uncertainty of data which gave rise to new difficulties and challenges. Big data generated may be structured data, Semi Structured data or unstructured data. For existing database and systems lot of difficulties are there to process, analyze, store and manage such a Big Data.  The Big Data challenges are Protection, Curation, Capture, Analysis, Searching, Visualization, Storage, Transfer and sharing. Map Reduce is a framework using which we can write applications to process huge amount of data, in parallel, on large clusters of commodity hardware in a reliable manner. Lot of efforts have been put by different researchers to make it simple, easy, effective and efficient. In our survey paper we emphasized on the working of Map Reduce, challenges, opportunities and recent trends so that researchers can think on further improvement.</p>

scholarly journals BIG DATA IN ADOLESCENT PSYCHIATRY: DO PATIENTS SHARE THEIR PSYCHIATRIC SYMPTOMS ON SOCIAL NETWORKING SITES?

2018 ◽  
Vol 30 (4) ◽  
pp. 395-403
Author(s):  
Halit Necmi Ucar ◽  
◽  
Safak Eray ◽  
Omer Kocael ◽  
Lutfi Ucar ◽  
...  
Keyword(s):  
Big Data ◽  
Social Networking ◽  
Adolescent Psychiatry ◽  
Social Networking Sites ◽  
Psychiatric Symptoms

Big Data Mining

2016 ◽  
pp. 53-59 ◽  
Author(s):  
N. G. Bhuvaneswari Amma
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Social Networking ◽  
Unstructured Data ◽  
Science And Engineering ◽  
Business World ◽  
Volume Velocity ◽  
Big Data Mining ◽  
Traditional Processing ◽  
Processing Techniques

Big data is a term used to describe very large amount of structured, semi-structured and unstructured data that is difficult to process using the traditional processing techniques. It is now expanding in all science and engineering domains. The key attributes of big data are volume, velocity, variety, validity, veracity, value, and visibility. In today's world, everyone is using social networking applications like Facebook, Twitter, YouTube, etc. These applications allow the users to create the contents for free of cost and it becomes huge volume of web data. These data are important in the competitive business world for making decisions. In this context, big data mining plays a major role which is different from the traditional data mining. The process of extracting useful information from large datasets or streams of data, due to its volume, velocity, variety, validity, veracity, value and visibility is termed as Big Data Mining.

Security Issues and Challenges Related to Big Data

2018 ◽  
pp. 86-101
Author(s):  
Jaimin N. Undavia ◽  
Atul Patel ◽  
Sheenal Patel
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Database Systems ◽  
Heterogeneous Data ◽  
Unstructured Data ◽  
Huge Amount ◽  
Current Time ◽  
Security Issues ◽  
Future Prediction ◽  
Data Term

Availability of huge amount of data has opened up a new area and challenge to analyze these data. Analysis of these data become essential for each organization and these analyses may yield some useful information for their future prospectus. To store, manage and analyze such huge amount of data traditional database systems are not adequate and not capable also, so new data term is introduced – “Big Data”. This term refers to huge amount of data which are used for analytical purpose and future prediction or forecasting. Big Data may consist of combination of structured, semi structured or unstructured data and managing such data is a big challenge in current time. Such heterogeneous data is required to maintained in very secured and specific way. In this chapter, we have tried to identify such challenges and issues and also tried to resolve it with specific tools.

Need for Dynamicity in Social Networking Sites

2013 ◽  
pp. 1-24 ◽  
Author(s):  
Gurdeep S Hura
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Social Networking ◽  
Real World ◽  
Social Networking Sites ◽  
Data Science ◽  
Emerging Technology ◽  
New Applications ◽  
Future Status

This chapter presents this new emerging technology of social media and networking with a detailed discussion on: basic definitions and applications, how this technology evolved in the last few years, the need for dynamicity under data mining environment. It also provides a comprehensive design and analysis of popular social networking media and sites available for the users. A brief discussion on the data mining methodologies for implementing the variety of new applications dealing with huge/big data in data science is presented. Further, an attempt is being made in this chapter to present a new emerging perspective of data mining methodologies with its dynamicity for social networking media and sites as a new trend and needed framework for dealing with huge amount of data for its collection, analysis and interpretation for a number of real world applications. A discussion will also be provided for the current and future status of data mining of social media and networking applications.

Stakeholder Perceptions and Word-of-Mouth on CSR Dynamics

2021 ◽  
pp. 1165-1179
Author(s):  
Andrée Marie López-Fernández ◽  
Zamira Burgos Silva
Keyword(s):  
Big Data ◽  
Corporate Social Responsibility ◽  
Social Responsibility ◽  
Social Networking ◽  
Social Networking Sites ◽  
Social Issues ◽  
Firm Dynamics ◽  
Big Data Analysis ◽  
Socially Responsible ◽  
Corporate Social

Corporate social responsibility is a strategy by which firms address social issues whilst tending to their profit enhancing objectives. However, is a socially responsible firm fulfilling its objectives if current and potential stakeholders perceive it to be unethical, engaging in poor and questionable practices? The article analyzes Big Data retrieved from Twitter related to five firms that have stated to be socially responsible but have yet to obtain stakeholders' legitimacy granted by the engagement in corporate social responsibility. The article contributes to the understanding and effects of firm dynamics in corporate social responsibility or lack thereof, on social networking sites by means of Big Data analysis.

Big Data

2015 ◽  
Vol 6 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Ashok Kumar Wahi ◽  
Yajulu Medury ◽  
Rajnish Kumar Misra
Keyword(s):  
Big Data ◽  
Unstructured Data ◽  
Data Driven ◽  
Enterprise 2.0 ◽  
Huge Amount ◽  
The World ◽  
Data Driven Decisions ◽  
The Impact

Big data has taken the world by storm. Everyone from every industry is not only talking about the impact of big data but is looking for ways to effectively leverage the power of big data. This challenge has heightened with the huge amount of unstructured data flowing from every direction, bringing along with it the increasing pressure to make data driven decisions rather than the gut-driven decisions. This article sheds light on how big data can be an enabler for smart enterprises if the organization is able to address the challenges posed by big data. Enterprises need to equip themselves with relevant technology, desired skills and a supporting managerial attitude to swim through the challenges of big data. It also highlights the need for all enterprises making the journey from 1.0 stage to Enterprise 2.0 to master the art of Big Data if they have to make the transition successful.

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