scholarly journals Extracting predictive information from heterogeneous data streams using Gaussian Processes

2016 ◽  
Vol 5 (1-2) ◽  
pp. 21-30 ◽  
Author(s):  
S. Ghoshal ◽  
S. Roberts
Keyword(s):  
Gaussian Processes ◽  
Data Streams ◽  
Heterogeneous Data ◽  
Predictive Information

scholarly journals Unlocking the Predictive Power of Heterogeneous Data to Build an Operational Dengue Forecasting System

2020 ◽  
Author(s):  
Carrie Manore ◽  
Geoffrey Fairchild ◽  
Amanda Ziemann ◽  
Nidhi Parikh ◽  
Katherine Kempfert ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Data Streams ◽  
Cumulative Risk ◽  
State Level ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Global Dynamics ◽  
Flexible System ◽  
Clinical Surveillance ◽  
Spatio Temporal

ABSTRACTPredicting an infectious disease can help reduce its impact by advising public health interventions and personal preventive measures. While availability of heterogeneous data streams and sensors such as satellite imagery and the Internet have increased the opportunity to indirectly measure, understand, and predict global dynamics, the data may be prohibitively large and/or require intensive data management while also requiring subject matter experts to properly exploit the data sources (e.g., deriving features from fundamentally different data sets). Few efforts have quantitatively assessed the predictive benefit of novel data streams in comparison to more traditional data sources, especially at fine spatio-temporal resolutions. We have combined multiple traditional and non-traditional data streams (satellite imagery, Internet, weather, census, and clinical surveillance data) and assessed their combined ability to predict dengue in Brazil’s 27 states on a weekly and yearly basis over seven years. For each state, we nowcast dengue based on several time series models, which vary in complexity and inclusion of exogenous data. We also predict yearly cumulative risk by municipality and state. The top-performing model and utility of predictive data varies by state, implying that forecasting and nowcasting efforts in the future may be made more robust by and benefit from the use of multiple data streams and models. One size does not fit all, particularly when considering state-level predictions as opposed to the whole country. Our first-of-its-kind high resolution flexible system for predicting dengue incidence with heterogeneous (and still sometimes sparse) data can be extended to multiple applications and regions.

Automated Kernel Search for Gaussian Processes on Data Streams

2021 ◽  
Author(s):  
Jan David Huwel ◽  
Fabian Berns ◽  
Christian Beecks
Keyword(s):  
Gaussian Processes ◽  
Data Streams

scholarly journals Real-Time Discovery and Geospatial Visualization of Mobility and Industry Events from Large-Scale, Heterogeneous Data Streams

2016 ◽  
Author(s):  
Leonhard Hennig ◽  
Philippe Thomas ◽  
Renlong Ai ◽  
Johannes Kirschnick ◽  
He Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Large Scale ◽  
Heterogeneous Data

Monitoring Distributed, Heterogeneous Data Streams: The Emergence of Safe Zones

2014 ◽  
pp. 17-28 ◽  
Author(s):  
Daniel Keren ◽  
Guy Sagy ◽  
Amir Abboud ◽  
David Ben-David ◽  
Assaf Schuster ◽  
...  
Keyword(s):  
Data Streams ◽  
Heterogeneous Data ◽  
Safe Zones
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