Behavioral Insights for Development from Mobile Network Big Data: Enlightening Policy Makers on the State of the Art

2014 ◽  
Author(s):  
Sriganesh Lokanathan ◽  
Roshanthi Lucas Gunaratne
Keyword(s):  
Big Data ◽  
State Of The Art ◽  
Mobile Network ◽  
The State ◽  
Policy Makers ◽  
Network Big Data

Using Mobile Network Big Data for Informing Transportation and Urban Planning in Colombo

2014 ◽  
Author(s):  
Sriganesh Lokanathan ◽  
Gabriel Kreindler ◽  
Nisansa Dilushan de Silva ◽  
Yuhei Miyauchi ◽  
Dedunu Dhananjaya
Keyword(s):  
Big Data ◽  
Urban Planning ◽  
Mobile Network ◽  
Network Big Data

An Approach of Semantic Similarity Measure between Documents Based on Big Data

2016 ◽  
Vol 6 (5) ◽  
pp. 2454 ◽  
Author(s):  
Mohammed Erritali ◽  
Abderrahim Beni-Hssane ◽  
Marouane Birjali ◽  
Youness Madani
Keyword(s):  
Big Data ◽  
Semantic Similarity ◽  
Retrieval System ◽  
Programming Model ◽  
State Of The Art ◽  
Distributed Processing ◽  
Similarity Measures ◽  
The State ◽  
Semantic Similarity Measure ◽  
Document Similarity

<p>Semantic indexing and document similarity is an important information retrieval system problem in Big Data with broad applications. In this paper, we investigate MapReduce programming model as a specific framework for managing distributed processing in a large of amount documents. Then we study the state of the art of different approaches for computing the similarity of documents. Finally, we propose our approach of semantic similarity measures using WordNet as an external network semantic resource. For evaluation, we compare the proposed approach with other approaches previously presented by using our new MapReduce algorithm. Experimental results review that our proposed approach outperforms the state of the art ones on running time performance and increases the measurement of semantic similarity.</p>

scholarly journals Prospects for energy economy modelling with big data: Hype, eliminating blind spots, or revolutionising the state of the art?

2019 ◽  
Vol 239 ◽  
pp. 991-1002 ◽  
Author(s):  
Francis G.N. Li ◽  
Chris Bataille ◽  
Steve Pye ◽  
Aidan O'Sullivan
Keyword(s):  
Big Data ◽  
State Of The Art ◽  
The State ◽  
Energy Economy ◽  
Blind Spots

scholarly journals Review of the Complexity of Managing Big Data of the Internet of Things

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
David Gil ◽  
Magnus Johnsson ◽  
Higinio Mora ◽  
Julian Szymański
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Internet Of Things ◽  
State Of The Art ◽  
The State ◽  
The Internet ◽  
Novel Technologies ◽  
Extensible Markup ◽  
Description Framework ◽  
The Internet Of Things

There is a growing awareness that the complexity of managing Big Data is one of the main challenges in the developing field of the Internet of Things (IoT). Complexity arises from several aspects of the Big Data life cycle, such as gathering data, storing them onto cloud servers, cleaning and integrating the data, a process involving the last advances in ontologies, such as Extensible Markup Language (XML) and Resource Description Framework (RDF), and the application of machine learning methods to carry out classifications, predictions, and visualizations. In this review, the state of the art of all the aforementioned aspects of Big Data in the context of the Internet of Things is exposed. The most novel technologies in machine learning, deep learning, and data mining on Big Data are discussed as well. Finally, we also point the reader to the state-of-the-art literature for further in-depth studies, and we present the major trends for the future.

scholarly journals Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts

2020 ◽  
Author(s):  
Quoc-Viet Pham ◽  
Dinh C. Nguyen ◽  
Thien Huynh-The ◽  
Won-Joo Hwang ◽  
Pubudu N. Pathirana
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
State Of The Art ◽  
The State ◽  
Daily Lives ◽  
Recent Advances ◽  
The Arts ◽  
The World ◽  
Novel Coronavirus

The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 215 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 14 April 2020, a cumulative total of 1,853,265 (118,854) infected (dead) COVID-19 cases were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify their applications in fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.

scholarly journals An Approach of Semantic Similarity Measure between Documents Based on Big Data

2016 ◽  
Vol 6 (5) ◽  
pp. 2454 ◽  
Author(s):  
Mohammed Erritali ◽  
Abderrahim Beni-Hssane ◽  
Marouane Birjali ◽  
Youness Madani
Keyword(s):  
Big Data ◽  
Semantic Similarity ◽  
Retrieval System ◽  
Programming Model ◽  
State Of The Art ◽  
Distributed Processing ◽  
Similarity Measures ◽  
The State ◽  
Semantic Similarity Measure ◽  
Document Similarity

<p>Semantic indexing and document similarity is an important information retrieval system problem in Big Data with broad applications. In this paper, we investigate MapReduce programming model as a specific framework for managing distributed processing in a large of amount documents. Then we study the state of the art of different approaches for computing the similarity of documents. Finally, we propose our approach of semantic similarity measures using WordNet as an external network semantic resource. For evaluation, we compare the proposed approach with other approaches previously presented by using our new MapReduce algorithm. Experimental results review that our proposed approach outperforms the state of the art ones on running time performance and increases the measurement of semantic similarity.</p>

The Big Data Era

2021 ◽  
pp. 87-103
Author(s):  
Maria K. Krommyda ◽  
Verena Kantere
Keyword(s):  
Big Data ◽  
State Of The Art ◽  
Large Datasets ◽  
The State ◽  
Data Exploration ◽  
Everyday Activities ◽  
Research Challenges ◽  
Open Research ◽  
Big Data Applications ◽  
Scientific Fields

Large datasets pertaining to many scientific fields and everyday activities are becoming available at an increasing rate. Processing, analyzing, and understanding the information that they offer poses significant technical challenges. There are many efforts dedicated to the development of big data exploration, analysis, and visualization applications that will improve the value of the information extracted from these datasets. An analysis of the state-of-the-art in these applications is presented here along with open research challenges that have not yet been tackled sufficiently. Also, specific domains where big data applications are needed are presented, and unique challenges are identified.

scholarly journals Fast Matrix Multiplication with Big Sparse Data

2017 ◽  
Vol 17 (1) ◽  
pp. 16-30
Author(s):  
G. Somasekhar ◽  
K. Karthikeyan
Keyword(s):  
Big Data ◽  
State Of The Art ◽  
Matrix Multiplication ◽  
Sparse Data ◽  
The State ◽  
Fast Matrix Multiplication

Abstract Big Data becameabuzz word nowadays due to the evolution of huge volumes of data beyond peta bytes. This article focuses on matrix multiplication with big sparse data. The proposed FASTsparse MULalgorithm outperforms the state-of-the-art big matrix multiplication approaches in sparse data scenario.

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