Hadoop-Based Integrated Monitoring Platform for Risk Prediction Using Big Data

2016 ◽  
Vol 826 ◽  
pp. 113-117 ◽  
Author(s):  
Yoon Deuk Seo ◽  
Jin Ho Ahn
Keyword(s):  
Social Media ◽  
Big Data ◽  
Real Time ◽  
Risk Prediction ◽  
Risk Information ◽  
Integrated Monitoring ◽  
Control Situation ◽  
Monitoring Platform

In this paper, we present an integrated monitoring platform framework to enable risk prediction using big data. By using social media and access records of their users, the proposed system determines uncertain risks they could create. By using the risk information that has been obtained from the platform, it can determine whether they should be granted the requested rights to access to restricted areas. And it can set some places that should be monitored as restricted areas. If the intrusion is detected in an area that has been set, then it promptly notifies the administrator with agile viewers showing him/her the intrusion for preparing the next step. So, it will provide a control situation for the administrator in real time and can detect the risky moments that may occur in advance.

Television by the numbers: The challenges of audience measurement in the age of Big Data

2017 ◽  
Vol 25 (1) ◽  
pp. 113-132 ◽  
Author(s):  
JP Kelly
Keyword(s):  
Social Media ◽  
Big Data ◽  
Real Time ◽  
Creative Industries ◽  
Data Sets ◽  
Audience Research ◽  
Television Industry ◽  
Real Time Analysis ◽  
Production Culture ◽  
Social Media Platforms

This article examines recent innovations in how television audiences are measured, paying particular attention to the industry's growing efforts to utilize the large bodies of data generated through social media platforms – a paradigm of research known as Big Data. Although Big Data is considered by many in the television industry as a more veracious model of audience research, this essay uses Boyd and Crawford's (2011) `Six Provocations of Big Data' to problematize and interrogate this prevailing industrial consensus. In doing so, this article explores both the affordances and the limitations of this emerging research paradigm – the latter having largely been ignored by those in the industry – and considers the consequences of these developments for the production culture of television more broadly. Although the full impact of the television industry's adoption of Big Data remains unclear, this article traces some preliminary connections between the introduction of these new measurement practices and the production culture of contemporary television. First, I demonstrate how the design of Big Data privileges real-time analysis, which, in turn, encourages increased investment in ‘live’ and/or ‘event’ television. Second, I argue that despite its potential to produce real-time insights, the scale of Big Data actually limits its utility in the context of the creative industries. Third, building on this discussion of the debatable value and applicability of Big Data, I describe how the introduction of social media metrics is further contributing to a ‘data divide’ in which access to these new information data sets is highly uneven, generally favouring institutions over individuals. Taken together, these three different but overlapping developments provide evidence that the introduction of Big Data is already having a notable effect on the television industry in a number of interesting and unexpected ways.

scholarly journals Sentiment Trend Analysis of Big Data

2019 ◽  
Vol 8 (10) ◽  
pp. 59-64
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Big Data ◽  
Sentiment Analysis ◽  
Real Time ◽  
Language Processing ◽  
Opinion Mining ◽  
Data Sources ◽  
Product Reviews

Various fields like Text Mining, Linguistics, Decision Making and Natural Language Processing together form the basis for Opinion Mining or Sentiment Analysis. People share their feelings, observations and thoughts on social media, which has emerged as a powerful tool for rapidly growing enormous repository of real time discussions and thoughts shared by people. In this paper, we aim to decipher the current popular opinions or emotions from various sources, hence, contributing to sentiment analysis domain. Text from social media, blogs and product reviews are classified according to the sentiment they project. We re-examine the traditional processes of sentiment extraction, to incorporate the increase in complexity and number of the data sources and relevant topics, while re-populating the meaning of sentiment. Working across and within numerous streams of social media, expression of sentiment and classification of polarity is re-examined, thereby redefining and enhancing the realm of sentiment. Numerous social media streams are analyzed to build datasets that are topical for each stream and are later polarized according to their sentiment expression. In conclusion, defining a sentiment and developing tools for its analysis in real time of human idea exchange is the motive.

scholarly journals THE EMERGENCE OF SOCIAL MEDIA FOR NATURAL DISASTERS MANAGEMENT: A BIG DATA PERSPECTIVE

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 75-82 ◽  
Author(s):  
N. Athanasis ◽  
M. Themistocleous ◽  
K. Kalabokidis ◽  
A. Papakonstantinou ◽  
N. Soulakellis ◽  
...  
Keyword(s):  
Social Media ◽  
Big Data ◽  
Natural Disasters ◽  
Real Time ◽  
Web Gis ◽  
Transfer Of Knowledge ◽  
Gis Modeling ◽  
Potential Resource ◽  
Real Time Information ◽  
Natural Disaster Management

<p><strong>Abstract.</strong> Social media is rapidly emerging as a potential resource of information capable to support natural disasters management. Despite the growing research interest focused on using social media during natural disasters, many challenges may arise on how to handle the ‘big data’ problem: huge amounts of geo-social data are available, in different formats and varying quality that must be processed quickly. This article presents a state-of-the-art approach towards the enhancement of decision support tools for natural disaster management with information from the Twitter social network. The novelty of the approach lies in the integration of Geographic Information Systems (GIS) modeling outputs with real-time information from Twitter. A first prototype has been implemented that integrates geo-referenced Twitter messages into a Web GIS for wildfire risk management and real-time earthquake monitoring. Following a highly scalable architecture that relies on big data components, the proposed methodology can be applied in different geographical areas, different types of social media and a variety of natural disasters. The article aims at highlighting the role of social big data, towards a more sophisticated transfer of knowledge among civil protection agencies, emergency response crews and affected population.</p>

Big Data Analytics of Geo-Social Media on Planning and Real-Time Decisions

2019 ◽  
Vol 16 (8) ◽  
pp. 3332-3337 ◽  
Author(s):  
S. Dhamodaran ◽  
G. Mahalakshmi ◽  
P. Harika ◽  
J. Refonaa ◽  
K. AshokKumar
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Big Data ◽  
Social Network ◽  
Real Time ◽  
Spatial Data ◽  
System Throughput ◽  
Future Planning ◽  
Efficient System ◽  
International Conference

The authorities are to be considered to make a real-time decision and future planning by various analyzing geo-social media posts in Geo-social Network. However, there are millions of Geo-social Network users who are producing overwhelming of data, called “Big Data” that is challenging to be analyzed and make the required real-time decisions. In our proposed system proposal of the efficient system for inquiring Geo-social Networks and harvesting the data as well as user’s location information (Dhamodaran, S. and Lakshmi, M., 2017. Design and Analysis of Spatial-Temporal Model Using Hydrological Techniques. IEEE International Conference on Computing of Power, Energy and Communication. pp.1–4). System architecture is proposed that processes an abundant amount of various social networks’ of data to monitor. Earth events, incidents, medical diseases, user trends, and views to make future real-time decisions and facilitate future planning (Dhamodarn, S., et al., Identification of User Poi in Spatial Data Using Android Application. International Conference on Computation of Power, Energy Information and Communication (ICCPEIC), IEEE. ISBN: 978-1-5090-0901-5). Twitter and Flickr have been analyzed using the proposed architecture in order to identify current events or disasters, such as earthquakes, snow, Ebola virus, and fires. The system is evaluated with respect to an efficiency of data while considering the system throughput.

scholarly journals Real Time Traffic Prediction Based On Social Media Text Data Using Deep Learning

2021 ◽  
Author(s):  
B. Mounica ◽  
K. Lavanya
Keyword(s):  
Social Media ◽  
Big Data ◽  
Real Time ◽  
Traffic Management ◽  
Traffic Analysis ◽  
Traffic Prediction ◽  
Text Data ◽  
Real Time Traffic ◽  
Data Architecture ◽  
Social Media Text

Due to urbanization Traffic management is one of the major issues in contemporary civic management, considering this circumstance traffic analysis is turning into the need of the present world. Text data generated by Twitter, Facebook and other social media platforms can be used for traffic management. Big data helps in traffic prediction and traffic analysis of advancing metropolitan zones. Constant traffic investigation requires preparing of information streams that are produced persistently to increase fast experiences. To measures stream information at a fast rate advancements on high figuring limit is required. Social media text data can be processed by using batch processing and stream processing with big data architecture through Spark and Hadoop framework. In this paper big data architecture is proposed for real time traffic text data analysis. In architecture Spark and Kafka are used in combination. Kafka helps in pipelines text data used in conjunction with spark stream processing engine. Big data architecture using Spark, Kafka with ability for processing and preparing huge measure of information, have settled the serious issue of handling and putting away constantly streaming data. The traffic information from Twitter API is streamed. In The proposed model pointed toward ensemble neural network model to reduce the variance in results for better prediction foreseeing traffic stream text data by incorporating Spark and Kafka that will be of an extraordinary incentive to the public authority for traffic management and analysis.

Techniques of Viral Marketing

2019 ◽  
Vol 118 (6) ◽  
pp. 97-99
Author(s):  
Arockia Jeyasheela A ◽  
Dr.S. Chandramohan
Keyword(s):  
Social Media ◽  
Real Time ◽  
Word Of Mouth ◽  
Viral Marketing ◽  
Consumer Perception ◽  
Key Word ◽  
The Right ◽  
A Company

This study is discussed about the viral marketing. It is a one of the key success of marketing. This paper gave the techniques of viral marketing. It can be delivered word of mouth. It can be created by both the representatives of a company and consumer (individuals or communities). The right viral message with go to right consumer to the right time. Viral marketing is easy to attract the consumer. It is most important advertising to consumer. It involves consumer perception, organization contribution, blogs, SMO (Social Media Optimize), SEO (Social Engine Optimize). Principles of viral marketing are social profile gathering, Proximity Market, Real time Key word density.

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