scholarly journals Augmented Virtual Reality: Combining Crowd Sensing and Social Data Mining with Large-Scale Simulation Using Mobile Agents for Future Smart Cities

Proceedings ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 49 ◽  
Author(s):  
Stefan Bosse ◽  
Uwe Engel
Keyword(s):  
Data Mining ◽  
Real World ◽  
Mobile Agents ◽  
Smart Cities ◽  
Sensory Information ◽  
Social Data ◽  
Virtual Sensors ◽  
Crowd Sensing ◽  
Social Data Mining ◽  
Simulator Program

Augmented reality is well known for extending the real world by adding computer-generated perceptual information and overlaid sensory information. In contrast, simulation worlds are commonly closed and rely on artificial sensory information generated by the simulator program or using data collected off-line. In this work, a new simulation paradigm is introduced, providing augmented virtuality by integrating crowd sensing and social data mining in simulation worlds by using mobile agents. The simulation world interacts with real world environments, humans, machines, and other virtual worlds in real-time. Mobile agents are closely related to bots that can interact with humans via chat blogs. Among the mining of physical sensors (temperature, motion, position, light, …), mobile agents can perform Crowd Sensing by participating in question–answer dialogs via a chat blog provided by a WEB App that can be used by the masses. Additionally, mobile agents can act as virtual sensors (offering data exchanged with other agents). Virtual sensors are sensor aggregators performing sensor fusion in a spatially region.

ParSoDA: high-level parallel programming for social data mining

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Loris Belcastro ◽  
Fabrizio Marozzo ◽  
Domenico Talia ◽  
Paolo Trunfio
Keyword(s):  
Data Mining ◽  
Parallel Programming ◽  
Social Data ◽  
Social Data Mining ◽  
High Level

scholarly journals Small Area Model-Based Estimators Using Big Data Sources

2015 ◽  
Vol 31 (2) ◽  
pp. 263-281 ◽  
Author(s):  
Stefano Marchetti ◽  
Caterina Giusti ◽  
Monica Pratesi ◽  
Nicola Salvati ◽  
Fosca Giannotti ◽  
...  
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Small Area ◽  
Small Area Estimation ◽  
The Body ◽  
Statistical Research ◽  
Social Data ◽  
Area Estimation ◽  
Model Based ◽  
Social Data Mining

Abstract The timely, accurate monitoring of social indicators, such as poverty or inequality, on a finegrained spatial and temporal scale is a crucial tool for understanding social phenomena and policymaking, but poses a great challenge to official statistics. This article argues that an interdisciplinary approach, combining the body of statistical research in small area estimation with the body of research in social data mining based on Big Data, can provide novel means to tackle this problem successfully. Big Data derived from the digital crumbs that humans leave behind in their daily activities are in fact providing ever more accurate proxies of social life. Social data mining from these data, coupled with advanced model-based techniques for fine-grained estimates, have the potential to provide a novel microscope through which to view and understand social complexity. This article suggests three ways to use Big Data together with small area estimation techniques, and shows how Big Data has the potential to mirror aspects of well-being and other socioeconomic phenomena.

Predicting retail business success using urban social data mining

2020 ◽  
Vol 12 (3) ◽  
pp. 263-277 ◽  
Author(s):  
Georgios Papadimitriou ◽  
Andreas Komninos ◽  
John Garofalakis
Keyword(s):  
Data Mining ◽  
Business Success ◽  
Social Data ◽  
Retail Business ◽  
Social Data Mining
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