WarpFlow: Exploring Petabytes of Space-Time Data

02/09/2019
by   Catalin Popescu, et al.
0

WarpFlow is a fast, interactive data querying and processing system with a focus on petabyte-scale spatiotemporal datasets and Tesseract queries. With the rapid growth in smartphones and mobile navigation services, we now have an opportunity to radically improve urban mobility and reduce friction in how people and packages move globally every minute-mile, with data. WarpFlow speeds up three key metrics for data engineers working on such datasets -- time-to-first-result, time-to-full-scale-result, and time-to-trained-model for machine learning.

READ FULL TEXT
research
01/22/2021

Efficient Data Management for Intelligent Urban Mobility Systems

Modern intelligent urban mobility applications are underpinned by large-...
research
03/15/2023

PTMTorrent: A Dataset for Mining Open-source Pre-trained Model Packages

Due to the cost of developing and training deep learning models from scr...
research
06/05/2020

Extracting Spatiotemporal Demand for Public Transit from Mobility Data

With people constantly migrating to different urban areas, our mobility ...
research
09/27/2021

Sustainable Urban Mobility in the Post-Pandemic Era (position paper)

COVID-19 is the first pandemic of the modern world causing significant c...
research
07/11/2007

The Trade-offs with Space Time Cube Representation of Spatiotemporal Patterns

Space time cube representation is an information visualization technique...
research
01/10/2023

Adapting to Skew: Imputing Spatiotemporal Urban Data with 3D Partial Convolutions and Biased Masking

We adapt image inpainting techniques to impute large, irregular missing ...

Please sign up or login with your details

Forgot password? Click here to reset