Selection of Commercially Viable Areas for Taxi Drivers Using Big Data

2018 ◽  
pp. 517-525
Author(s):  
Kavya Devabhakthuni ◽  
Bhavya Munukurthi ◽  
Sireesha Rodda
Keyword(s):  
Big Data ◽  
Taxi Drivers ◽  
Selection Of

Big Data in the Field of Civil Security Research

2016 ◽  
Vol 7 (1) ◽  
pp. 54-64 ◽  
Author(s):  
Julia Gonschorek ◽  
Anja Langer ◽  
Benjamin Bernhardt ◽  
Caroline Räbiger
Keyword(s):  
Big Data ◽  
Temporal Distribution ◽  
Tactical Planning ◽  
Fire Departments ◽  
Prevention Measures ◽  
Scientific Contribution ◽  
Security Research ◽  
Geovisual Analytics ◽  
The University ◽  
Selection Of

This article gives insight in a running dissertation at the University in Potsdam. Point of discussion is the spatial and temporal distribution of emergencies of German fire brigades that have not sufficiently been scientifically examined. The challenge is seen in Big Data: enormous amounts of data that exist now (or can be collected in the future) and whose variables are linked to one another. These analyses and visualizations can form a basis for strategic, operational and tactical planning, as well as prevention measures. The user-centered (geo-) visualization of fire brigade data accessible to the general public is a scientific contribution to the research topic 'geovisual analytics and geographical profiling'. It may supplement antiquated methods such as the so-called pinmaps as well as the areas of engagement that are freehand constructions in GIS. Considering police work, there are already numerous scientific projects, publications, and software solutions designed to meet the specific requirements of Crime Analysis and Crime Mapping. By adapting and extending these methods and techniques, civil security research can be tailored to the needs of fire departments. In this paper, a selection of appropriate visualization methods will be presented and discussed.

Big Data in Cultural Tourism – Building Sustainability and Enhancing Competitiveness

2021 ◽  
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Value Chain ◽  
New Technologies ◽  
Cultural Tourism ◽  
End Users ◽  
Ethical Implications ◽  
Tourism Sector ◽  
Cultural Experiences ◽  
Selection Of

The use of big data is becoming increasingly important across the tourism sector and the value chain. With this publication, UNWTO intends to provide a baseline research on using big data by tourism and culture stakeholders, in order to improve the competitiveness of cultural tourism and reinforce its sustainability. The study sets the basis to connect tourism, culture and new technologies for mutual benefits, while calling for a reflection on the ethical implications for policymakers, businesses and end-users. The selection of case studies illustrates the most frequent case-scenarios of the use of big data in cultural tourism within destinations, compiled during the research. As the new technologies are facing ever-evolving scenarios, their use will be harnessed by the tourism sector in its endeavour to innovate and provide new cultural experiences.

Model of predicting customer behavior based on big data analysis technologies

2021 ◽  
Vol 1 (2 (6)) ◽  
Author(s):  
Anastasiia Ivanitska ◽  
Dmytro Ivanov ◽  
Ludmila Zubik
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Customer Behavior ◽  
Learning Technologies ◽  
User Preferences ◽  
Data Sets ◽  
Learning Technology ◽  
Behavior Prediction ◽  
Network Information ◽  
Selection Of

The analysis of the available methods and models of formation of recommendations for the potential buyer in network information systems for the purpose of development of effective modules of selection of advertising is executed. The effectiveness of the use of machine learning technologies for the analysis of user preferences based on the processing of data on purchases made by users with a similar profile is substantiated. A model of recommendation formation based on machine learning technology is proposed, its work on test data sets is tested and the adequacy of the RMSE model is assessed. Keywords: behavior prediction; advertising based on similarity; collaborative filtering; matrix factorization; big data; machine learning

scholarly journals Rapid Evolution of SARS-CoV-2 Challenges Human Defenses

2021 ◽  
Author(s):  
Carlos M. Duarte ◽  
David Ketcheson ◽  
Víctor M. Eguíluz ◽  
Susana Agustí ◽  
Juan Fernández-Gracia ◽  
...  
Keyword(s):  
Big Data ◽  
Receptor Binding ◽  
Evolutionary Ecology ◽  
Rapid Evolution ◽  
Binding Domain ◽  
Spike Protein ◽  
Receptor Binding Domain ◽  
Future Behavior ◽  
Human Infections ◽  
Selection Of

AbstractEvolutionary ecology theory provides an avenue to anticipate the future behavior of SARS-CoV-2. Here we quantify the accelerating evolution of SARS-CoV-2 by tracking the SARS-CoV-2 mutation globally, with a focus on the Receptor Binding Domain (RBD) of the spike protein believed to determine infectivity. We estimate that 384 million people were infected by March 1st, 2021, producing up to 1021 copies of the virus, with one new RBD variant appearing for every 600,000 human infections, resulting in approximately three new effective RBD variants produced daily. Doubling the number of RBD variants every 71.67 days followed by selection of the most infective variants challenges our defenses and calls for a shift to anticipatory, rather than reactive tactics.One-Sentence SummaryAccelerating evolution of SARS-CoV-2 demands formulating universal vaccines and treatments based on big-data simulations of possible new variants.

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