scholarly journals Major Big Data Challenges in Most Industries and Innovative Solutions

2019 ◽  
Vol 9 (2S2) ◽  
pp. 424-428
Keyword(s):  
Big Data ◽  
Mobile Networks ◽  
Spatial Data ◽  
High Volume ◽  
High Definition ◽  
Management Tools ◽  
Social Media Networks ◽  
Massive Growth ◽  
Multiple Challenges ◽  
High Definition Tv

The term “Big data” refers to “the high volume of data sets that are relatively complex in nature and having challenges in processing and analyzing the data using conventional database management tools”. In the digital universe, the data volume and variety that, we deal today have grown-up massively from different sources such as Business Informatics, Social-Media Networks, Images from High Definition TV, data from Mobile Networks, Banking data from ATM Machines, Genomics and GPS Trails, Telemetry from automobiles, Meteorology, Financial market data etc. Data Scientists confirm that 80% of the data that we have gathered today are in unstructured format, i.e. in the form of images, pixel data, Videos, geo-spatial data, PDF files etc. Because of the massive growth of data and its different formats, organizations are having multiple challenges in capturing, storing, mining, analyzing, and visualizing the Big data. This paper aims to exemplify the key challenges faced by most organizations and the significance of implementing the emerging Big data techniques for effective extraction of business intelligence to make better and faster decisions

Scaling spatial big data in a location-based social network

2014 ◽  
Vol 5 (2) ◽  
pp. 141-155
Author(s):  
Maxwell Guimarães de Oliveira ◽  
Ana Gabrielle Ramos Falcão ◽  
Cláudio De Souza Baptista ◽  
Hugo Feitosa de Figueiredo ◽  
Daniel Farias Batista Leite
Keyword(s):  
Big Data ◽  
Spatial Data ◽  
Response Times ◽  
Smart Cities ◽  
High Volume ◽  
Geographic Region ◽  
Technological Advances ◽  
Shared Information ◽  
Spatial Big Data ◽  
Location Based Social Networks

The widespread of the World Wide Web has resulted in a high volume of volunteered generated information using different formats including text, photography and video. The technological advances of recent years enabled the emergence and the popularization of various mobile devices equipped with GPS and connectivity to the Internet. This scenario contributed to the advent of several location-based applications and aroused the interest of many users in the geographical context of the information. An example of such applications are the Location-Based Social Networks (LBSN), in which the users interact with information classified by geographic region, as in the context of Smart Cities, in which citizens can interact pinning their criticisms, opinions and comments on various topics related to their city or neighborhood. The LBSNs have increasingly attracted the interest of the population and have consequently registered an increase in both the number of users interacting and the volume of shared information. This popularity brings up concerns about scalability, since it is essential to provide an environment that maintains the users active and motivated for contributing. Thus, the LBSNs must ensure acceptable response times, especially in spatial queries performed by their users, otherwise such applications may collapse due to the abandonment of their faithful users. Among several proposals of LBSNs in the community, it is still difficult to find out approaches concerned in scalability. In this context, this paper proposes an approach based on Big Data technologies to provide scalability in LBSNs and thus handle large volumes of spatial data. Our approach exploits NoSQL databases, the Map/Reduce technique and the development of extensions for indexing and querying Spatial Big Data.

Driver Technology for 8K Ultra High Definition TV

2019 ◽  
pp. 1506
Author(s):  
Hyun-Wook Lim ◽  
Yong-Hoon Yu ◽  
Jinho Kim ◽  
Byoung-Yoon Jang ◽  
Jung-Pil Lim ◽  
...  
Keyword(s):  
High Definition ◽  
High Definition Tv

An Optimal Framework for Spatial Query Optimization Using Hadoop in Big Data Analytics

2019 ◽  
Vol 12 ◽  
Author(s):  
Pankaj Dadheech ◽  
Dinesh Goyal ◽  
Sumit Srivastava ◽  
Ankit Kumar
Keyword(s):  
Big Data ◽  
Query Optimization ◽  
Spatial Data ◽  
Spatial Information ◽  
Big Data Analytics ◽  
Spatial Query ◽  
Data Process ◽  
Boolean Queries ◽  
Spatial Query Optimization ◽  
Hadoop System

Spatial queries frequently used in Hadoop for significant data process. However, vast and massive size of spatial information makes it difficult to process the spatial inquiries proficiently, so they utilized the Hadoop system for process Big Data. We have used Boolean Queries & Geometry Boolean Spatial Data for Query Optimization using Hadoop System. In this paper, we show a lightweight and adaptable spatial data index for big data which will process in Hadoop frameworks. Results demonstrate the proficiency and adequacy of our spatial ordering system for various spatial inquiries.

scholarly journals Development and Evaluation of a Big Data Framework for Performance Management in Mobile Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 226380-226396
Author(s):  
Diana Martinez-Mosquera ◽  
Rosa Navarrete ◽  
Sergio Lujan-Mora
Keyword(s):  
Big Data ◽  
Performance Management ◽  
Mobile Networks ◽  
Data Framework

scholarly journals Accuracy improvements for cold-start recommendation problem using indirect relations in social networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Fu Jie Tey ◽  
Tin-Yu Wu ◽  
Chiao-Ling Lin ◽  
Jiann-Liang Chen
Keyword(s):  
Social Media ◽  
Recommender System ◽  
Mobile Networks ◽  
Reference Value ◽  
User Preferences ◽  
Digital Information ◽  
Internet Applications ◽  
Social Media Networks ◽  
Recommendation Accuracy ◽  
Gain Access

AbstractRecent advances in Internet applications have facilitated information spreading and, thanks to a wide variety of mobile devices and the burgeoning 5G networks, users easily and quickly gain access to information. Great amounts of digital information moreover have contributed to the emergence of recommender systems that help to filter information. When the rise of mobile networks has pushed forward the growth of social media networks and users get used to posting whatever they do and wherever they visit on the Web, such quick social media updates already make it difficult for users to find historical data. For this reason, this paper presents a social network-based recommender system. Our purpose is to build a user-centered recommender system to exclude the products that users are disinterested in according to user preferences and their friends' shopping experiences so as to make recommendations effective. Since there might be no corresponding reference value for new products or services, we use indirect relations between friends and “friends’ friends” as well as sentinel friends to improve the recommendation accuracy. The simulation result has proven that our proposed mechanism is efficient in enhancing recommendation accuracy.

scholarly journals Putting the world back to work: An expert system using big data and artificial intelligence in combating the spread of COVID-19 and similar contagious diseases

Work ◽  
2020 ◽  
Vol 67 (3) ◽  
pp. 557-572
Author(s):  
Said Tkatek ◽  
Amine Belmzoukia ◽  
Said Nafai ◽  
Jaafar Abouchabaka ◽  
Youssef Ibnou-ratib
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Expert System ◽  
Population Monitoring ◽  
High Definition ◽  
Contagious Diseases ◽  
The World ◽  
General Guide ◽  
The Impact ◽  
Back To Work

BACKGROUND: To combat COVID-19, curb the pandemic, and manage containment, governments around the world are turning to data collection and population monitoring for analysis and prediction. The massive data generated through the use of big data and artificial intelligence can play an important role in addressing this unprecedented global health and economic crisis. OBJECTIVES: The objective of this work is to develop an expert system that combines several solutions to combat COVID-19. The main solution is based on a new developed software called General Guide (GG) application. This expert system allows us to explore, monitor, forecast, and optimize the data collected in order to take an efficient decision to ensure the safety of citizens, forecast, and slow down the spread’s rate of COVID-19. It will also facilitate countries’ interventions and optimize resources. Moreover, other solutions can be integrated into this expert system, such as the automatic vehicle and passenger sanitizing system equipped with a thermal and smart High Definition (HD) cameras and multi-purpose drones which offer many services. All of these solutions will facilitate lifting COVID-19 restrictions and minimize the impact of this pandemic. METHODS: The methods used in this expert system will assist in designing and analyzing the model based on big data and artificial intelligence (machine learning). This can enhance countries’ abilities and tools in monitoring, combating, and predicting the spread of COVID-19. RESULTS: The results obtained by this prediction process and the use of the above mentioned solutions will help monitor, predict, generate indicators, and make operational decisions to stop the spread of COVID-19. CONCLUSIONS: This developed expert system can assist in stopping the spread of COVID-19 globally and putting the world back to work.

Analysis of Big Data Analytics in Healthcare Sector: Applications and Tools

2020 ◽  
Vol 17 (12) ◽  
pp. 5605-5612
Author(s):  
A. Kaliappan ◽  
D. Chitra
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Health Sector ◽  
Big Data Analytics ◽  
High Volume ◽  
Healthcare Sector ◽  
Genome Database ◽  
Tremendous Amount ◽  
The Impact ◽  
Different Sources

In today’s world, an immense measure of information in the form of unstructured, semi-structured and unstructured is generated by different sources all over the world in a tremendous amount. Big data is the termed coined to address these enormous amounts of data. One of the major challenges in the health sector is handling a high-volume variety of data generated from diverse sources and utilizing it for the wellbeing of human. Big data analytics is one of technique designed to operate with monstrous measures of information. The impact of big data in healthcare field and utilization of Hadoop system tools for supervising the big data are deliberated in this paper. The big data analytics role and its theoretical and conceptual architecture include the gathering of diverse information’s such as electronic health records, genome database and clinical decisions support systems, text representation in health care industry is investigated in this paper.

A Topic Modeling Approach to Study Design Requirements

2021 ◽  
Author(s):  
Cheng Chen ◽  
Jesse Mullis ◽  
Beshoy Morkos
Keyword(s):  
Domain Knowledge ◽  
Latent Dirichlet Allocation ◽  
High Volume ◽  
Management Tools ◽  
Human Errors ◽  
Domain Experts ◽  
Product Evolution ◽  
Engineering Changes ◽  
Collapsed Gibbs Sampling ◽  
Requirements Documents

Abstract Risk management is vital to a product’s lifecycle. The current practice of reducing risks relies on domain experts or management tools to identify unexpected engineering changes, where such approaches are prone to human errors and laborious operations. However, this study presents a framework to contribute to requirements management by implementing a generative probabilistic model, the supervised latent Dirichlet allocation (LDA) with collapsed Gibbs sampling (CGS), to study the topic composition within three unlabeled and unstructured industrial requirements documents. As finding the preferred number of topics remains an open-ended question, a case study estimates an appropriate number of topics to represent each requirements document based on both perplexity and coherence values. Using human evaluations and interpretable visualizations, the result demonstrates the different level of design details by varying the number of topics. Further, a relevance measurement provides the flexibility to improve the quality of topics. Designers can increase design efficiency by understanding, organizing, and analyzing high-volume requirements documents in confirmation management based on topics across different domains. With domain knowledge and purposeful interpretation of topics, designers can make informed decisions on product evolution and mitigate the risks of unexpected engineering changes.

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