Large-Scale Data-Driven Financial Risk Assessment

2019 ◽  
pp. 387-408 ◽  
Author(s):  
Wolfgang Breymann ◽  
Nils Bundi ◽  
Jonas Heitz ◽  
Johannes Micheler ◽  
Kurt Stockinger
Keyword(s):  
Risk Assessment ◽  
Financial Risk ◽  
Large Scale ◽  
Data Driven ◽  
Large Scale Data ◽  
Scale Data

A data-driven mechanism for large-scale data distribution

2016 ◽  
Author(s):  
Peichang Shi ◽  
Yiying Li ◽  
Bo Ding ◽  
Longquan Jiang ◽  
Hui Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Data Distribution ◽  
Data Driven ◽  
Large Scale Data ◽  
Scale Data

Real Men Don’t Say “Cute”

2016 ◽  
Vol 8 (3) ◽  
pp. 310-322 ◽  
Author(s):  
Jordan Carpenter ◽  
Daniel Preotiuc-Pietro ◽  
Lucie Flekova ◽  
Salvatore Giorgi ◽  
Courtney Hagan ◽  
...  
Keyword(s):  
Large Scale ◽  
Social Groups ◽  
Education Level ◽  
Political Orientation ◽  
Data Driven ◽  
Social Categories ◽  
Verbal Expression ◽  
Large Scale Data ◽  
The Social ◽  
Scale Data

People associate certain behaviors with certain social groups. These stereotypical beliefs consist of both accurate and inaccurate associations. Using large-scale, data-driven methods with social media as a context, we isolate stereotypes by using verbal expression. Across four social categories—gender, age, education level, and political orientation—we identify words and phrases that lead people to incorrectly guess the social category of the writer. Although raters often correctly categorize authors, they overestimate the importance of some stereotype-congruent signal. Findings suggest that data-driven approaches might be a valuable and ecologically valid tool for identifying even subtle aspects of stereotypes and highlighting the facets that are exaggerated or misapplied.

Large-Scale Data-Driven Traffic Sensor Health Monitoring

2021 ◽  
Author(s):  
Tongge Huang ◽  
Pranamesh Chakraborty ◽  
Anuj Sharma ◽  
Chinmay Hegde
Keyword(s):  
Health Monitoring ◽  
Large Scale ◽  
Data Driven ◽  
Large Scale Data ◽  
Scale Data ◽  
Sensor Health

Foundry approach for layout risk assessment through comprehensive pattern harvesting and large-scale data analysis

2020 ◽  
Author(s):  
Monisa Ramesh Babu ◽  
Shenghua Song ◽  
Qian Xie ◽  
Pouya Rezaeifakhr ◽  
Eric Chiu ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Data Analysis ◽  
Large Scale ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Quantifying the Spatio-Temporal Process of Township Urbanization: A Large-Scale Data-Driven Approach

2019 ◽  
Vol 8 (9) ◽  
pp. 389
Author(s):  
Xinliang Liu ◽  
Yi Wang ◽  
Yong Li ◽  
Jinshui Wu
Keyword(s):  
Large Scale ◽  
Driving Forces ◽  
Data Driven ◽  
Population Migration ◽  
Urbanization Process ◽  
Large Scale Data ◽  
Temporal Process ◽  
Data Driven Approach ◽  
Spatio Temporal ◽  
Scale Data

The integrated recognition of spatio-temporal characteristics (e.g., speed, interaction with surrounding areas, and driving forces) of urbanization facilitates regional comprehensive development. In this study, a large-scale data-driven approach was formed for exploring the township urbanization process. The approach integrated logistic models to quantify urbanization speed, partial triadic analysis to reveal dynamic relationships between rural population migration and urbanization, and random forest analysis to identify the response of urbanization to spatial driving forces. A typical subtropical town was chosen to verify the approach by quantifying the spatio-temporal process of township urbanization from 1933 to 2012. The results showed that (i) urbanization speed was well reflected by the changes of time-course areas of urban cores fitted by a four-parameter logistic equation (R2 = 0.95–1.00, p < 0.001), and the relatively fast and steady developing periods were also successfully predicted, respectively; (ii) the spatio-temporal sprawl of urban cores and their interactions with the surrounding rural residential areas were well revealed and implied that the town experienced different historically aggregating and splitting trajectories; and (iii) the key drivers (township merger, elevation and distance to roads, as well as population migration) were identified in the spatial sprawl of urban cores. Our findings proved that a comprehensive approach is powerful for quantifying the spatio-temporal characteristics of the urbanization process at the township level and emphasized the importance of applying long-term historical data when researching the urbanization process.

Large-scale Data-driven Segmentation of Banking Customers

2020 ◽  
Author(s):  
Md Monir Hossain ◽  
Mark Sebestyen ◽  
Dhruv Mayank ◽  
Omid Ardakanian ◽  
Hamzeh Khazaei
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Large Scale Data ◽  
Scale Data
Sign in / Sign up

Export Citation Format

Share Document