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Sensing, Collection, Mining of Vehicular Data

Sensing, Collection, Mining of Vehicular Data. Group – 1 Breakout Session DriveSense NSF Workshop Oct 31, 2014. Breakout Group I – Sensing, Collection, & Mining. Infrastructure Sensing vs. Crowd Sensing Incentives What How to evaluate Unified Tools Data Processing/Analysis Validation

Mercy
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Sensing, Collection, Mining of Vehicular Data

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  1. Sensing, Collection, Mining of Vehicular Data Group – 1 Breakout Session DriveSense NSF Workshop Oct 31, 2014

  2. Breakout Group I – Sensing, Collection, & Mining • Infrastructure Sensing vs. Crowd Sensing • Incentives • What • How to evaluate • Unified Tools • Data Processing/Analysis • Validation • Large-Scale Sensing Data! • How Large should be large? • How to execute? • Privacy/Security • Do we need to redefine privacy? • Privacy vs. Safety? • Faulty Sources vs. Malicious Behavior? • New Mining tools vs. existing one? • Is there any unique characteristics that defines driving data? • Heterogeneity • Data type sources vs. Data types • Role of giant companies (e.g., Google, Navtaq)

  3. What does the community lack? • Libraries of Data, from various sources • Infrastructure-based – Crowd sourced – In-vehicle – Satellite, social networks data, etc. • For: Mobility, Energy, Pollution, Health, Livability, Safety, Activity, Context (weather, infra condition, etc.), etc. • Common Tools • Middleware for apps, for crowd sensing, APIs, etc. • Mining, data fusion, scalable data processing, visualization • Algorithms for data quality, outlier detection, faulty/malicious info, data sanitation, etc.

  4. What does the community lack? (2) • Incentives for data/algorithm sharing, • Existing data, projects (knowing what we don’t know) • Portal for Data, tools, projects, use-cases, studies, related research • Library of models, test suites, test beds, simulation tools • Benchmarks for validation (worst/best cases)

  5. Major Issues • Privacy, security • Understanding the impact of anon. (etc.) algorithms on data/study quality • Anon algorithms • Data fusion • Existing data sources, RITIS, STEWARD, mobile data, in-vehicle, etc. • Heterogeneity, time/space/context dependency, normalization, homogeneous formats, etc. • Scale & other data issues • How much is enough, added value, granularity, application-dependent, transparency, private data providers/hiding

  6. Opportunities/Directions • Risk assessment, revisiting safety practices • Interaction/collaboration with companies • Breakthrough research • Long term • Non incremental (aiming for the sky) • Feedback integration • Actuation – decision making – recommendations • Going global

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