Data Analytics: Challenges and the Internet of Moving Things
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How do we address the key challenges of IoMT? Where does computing take place? Where do we place the sensors? This presentation explores those issues. Presented at the 2017 D-STOP Symposium.
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Data Analytics: Challenges and the Internet of Moving Things
- 1. Data Analytics: Challenges and the Internet of Moving Things Constantine Caramanis The University of Texas at Austin Electrical and Computer Engineering Department 1
- 2. Infrastructure-based sensing Sensing includes radar, LIDAR, cameras, and weather Coordinate traffic through intersections, support automated driving Collect data about collisions and near-misses for planning Effective with non-connected cars, bicycles, and pedestrians Sensing includes radar, LIDAR, cameras, and weather Coordinate traffic through intersections, support automated driving Collect data about collisions and near-misses for planning Effective with non-connected cars, bicycles, and pedestrians
- 3. Radar-aided millimeter wave communication mmWave BS supporting V2X+radar antennas Radar beam Millimeter wave is used for both radar sensing and high bandwidth communication communication beams Radar can be used to configure communication link more efficiently
- 4. Key Challenges: Noise + Noise + Noise • Different Noise Characteristics in Data. • Raw data – measurements from other cars, from infrastructure, and from local sensors. • Partial occlusions: objects may disappear/reappear • Maps can be wrong: sparse but arbitrary corruption in the data. • Inconsistent measurements from different sensing modalities
- 5. Hidden Structure and Missing Data: a visual illustration
- 6. Key Challenges: Mixed Populations • Higher level data abstractions • Inferring behavior: • Ex: deviations from trajectory
- 7. Mixtures and Non-Linearities in Large Scale Data Analysis Linear, Logistic and Non-linear regression are fundamental for prediction and planning Examples: transit time vs. daily flows, flow vs. speed, responses to network stressors or diversions or to future demand and flow patterns Mixtures: Populations are mixed, and may require simultaneous clustering and regression/classification, when clustering-as-data-preprocessing is impossible Nonlinearities: Discover structure without expensive/intractable non-parametric models New algorithms for: 1. Solving the simultaneous clustering-regression problem (tensor methods) 2. Structure recovery through unknown non-linear transforms (second-moment methods) Northfield Windsor Park RidgetopHyde Park/ Northfield Delwood II Hancock North University Cherrywood/WilshireWood / Delwood I Mueller Barbara Jordan Blvd 38 ½th St Manor Rd Other Ramps used by neighborhood traffic, Source: Dr. J. Duthie
- 8. Dynamics, Transportation and Data Science • The two themes above tightly interrelated • Inference-of-dynamics becomes a sensing modality • And different sensing capabilities require/impose different inference needs
- 9. Upshot • Basic Statistical and Algorithmic Research, Models and Computation still a fundamental bottleneck • Computing Infrastructure: • Where does computing take place? • Where are the sensors? • Cost – Speed – Communication challenges and tradeoffs
Notas do Editor
- Notes from Constantine Caramanis: Mixed regression is the problem where you see (y,x) and they are related via: y = z*x*beta_1 + (1-z)*x*beta_2 + noise Instead of having all data (y,x) being related by one (noisy) linear relationship, y = x*beta + noise, each data point has one of 2 (or many) possible linear relationships. The interesting setting is when if you look at all the x's by themselves, they are not clusterable, and hence you cannot pre-process. Note that if they were clusterable, you would just cluster the data, and then solve individual regressions on each cluster, as usual. ------------ For the nonlinear part, think of logistic regression. It says: P(y = 1 | x) = f(x*beta), where f is the logit function. Suppose that you do not know f. Must you learn it, if what you care about is learning beta? Our results say that you do not. if all you want is to learn beta (say, its support if you have a sparse problem) then no need to learn f.