Timothy Hunter
University of California, Berkeley
15 Papers
77 Citations
Timothy Hunter is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Computer science & Scalability. The author has an hindex of 12, co-authored 15 publications. Previous affiliations of Timothy Hunter include Stanford University.
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Papers
Discretized streams: fault-tolerant streaming computation at scale
Matei Zaharia,Tathagata Das,Haoyuan Li,Timothy Hunter,Scott Shenker,Ion Stoica +5 more
- 03 Nov 2013
TL;DR: D-Streams enable a parallel recovery mechanism that improves efficiency over traditional replication and backup schemes, and tolerates stragglers, and can easily be composed with batch and interactive query models like MapReduce, enabling rich applications that combine these modes.
Understanding road usage patterns in urban areas
Pu Wang,Timothy Hunter,Alexandre M. Bayen,Katja Schechtner,Katja Schechtner,Marta C. González +5 more
TL;DR: This paper combines the most complete record of daily mobility, based on large-scale mobile phone data, with detailed GIS data, uncovering previously hidden patterns in urban road usage, and proposes a network of road usage by defining a bipartite network framework.
Path and travel time inference from GPS probe vehicle data
Timothy Hunter,Ryan Herring,Pieter Abbeel,Alexandre M. Bayen +3 more
- 01 Jan 2009
TL;DR: This work presents an expectation maximization algorithm that simultaneously learns the likely paths taken by probe vehicles as well as the travel time distributions through the network, and assumes that the data available is a small set of sparsely traced vehicle trajectories.
The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data.
Timothy Hunter,Pieter Abbeel,Alexandre M. Bayen +2 more
- 01 Jan 2012
TL;DR: A new class of algorithms, which are altogether called the path inference filter (PIF), that maps GPS data in real time, for a variety of tradeoffs and scenarios and with a high throughput, is introduced.
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The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data
TL;DR: A new class of algorithms, which are altogether called the path inference filter (PIF), that maps GPS data in real time, for a variety of tradeoffs and scenarios and with a high throughput, is introduced.
118