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Comprehensive Analysis of GPS Logs: System Architecture, Challenges, and Automatic Reasoning

This document provides an in-depth analysis of GPS log data, focusing on system architecture, characteristics, and backend processes. We explore the challenges in accurate GPS tracking and the importance of automatic reasoning in data analysis. Our study includes a 10-week observational survey with 100 respondents, highlighting the necessity of user involvement in data correction. Additionally, we discuss the GPS characteristics such as accuracy, battery life, and trip segmentation, alongside the implementation of logic rules for improved data integrity and modality derivation.

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Comprehensive Analysis of GPS Logs: System Architecture, Challenges, and Automatic Reasoning

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  1. Analysis of GPS logs Dagstuhl Wilko Quak

  2. Overview • Introduction • GPS characteristics • System architecture • DBMS backend • Automatic rules • Challenges GPS Fun

  3. Introduction • GPS-Monitored itinerary trackingof persons • 10 weeks * 100 respondents, each 7 days • Communication via Website GPS Fun

  4. This is what we want: GPS Fun

  5. This is what we have GPS Fun

  6. This is how plan to get it GPS LOG Analyzed trip data Validated analysis Postgis Web server GPS Fun

  7. GPS Characteristics • Accuracy 10 meters (approx.) • Battery life 18 hours • 10 000 trackpoints • Works only outdoors • Needs clear view of the sky • Returns innacurate logs • Has a start-up time GPS Fun

  8. GPS tracklog characteristics • A tracklog is a list of points: • timestamp • location • elevation • connected GPS Fun

  9. In Google Earth GPS Fun

  10. GPS Characteristics (this is my life) GPS Fun

  11. Flow Control + Architecture Web Server Internet User: Upload tracklog PostGIS DBMS Backend + Mapserver Server: Automatic Analysis User: hand-edit data Server: store data permanently GPS Fun

  12. Automatic Analysis Automatic reasoning is needed! GPS Fun

  13. Automatic reasoning is not enough. Keep the user in the loop! Hand Edits GPS Fun

  14. DBMS Model GPS Fun

  15. Automatic analysis • Process trackpoints to fill trip table: • Cleanup data • Split into trips • Derive modality • Good guess better than noting (user is in the loop) GPS Fun

  16. Analysis Rules • Simple logic rules: • No Teleport: The endpoint of one trip is the startpoint of the next trip • Rules with external data: • If a track follows a railway line the user is travelling by train • Probabilistic rules: • The speed distribution on a track gives probabilities for: foot, bike, car. GPS Fun

  17. Rule implementation I have different preffered languages for every rule: • PL/SQL • Java • XSLT • Probabilistic Language (???) • GIS system Challenge: How do I make an integrated system? GPS Fun

  18. Discussion GPS Fun

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