A foundation for representing and querying moving objects
ACM Transactions on Database Systems (TODS)
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Space-Time Summarization of Multisensor Time Series. Case of Missing Data
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Proximity queries in large traffic networks
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
A conceptual view on trajectories
Data & Knowledge Engineering
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
A network-based indexing method for trajectories of moving objects
ADVIS'06 Proceedings of the 4th international conference on Advances in Information Systems
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Sensor data on traffic events have prompted a wide range of research issues, related with the so-called ITS (Intelligent Transportation Systems). Data are delivered for both static (fixed) and mobile (embarked) sensors, generating large and complex spatio-temporal series. Research efforts in handling these data range from pattern matching and data mining techniques (for forecasting and trend analysis) to work on database queries (e.g., to construct scenarios). Work on embarked sensors also considers issues on trajectories and moving objects.This paper presents a new kind of framework to manage static sensor data. Our work is based on combining research on analytical methods to process sensor data, and database procedures to query these data. The first component is geared towards supporting pattern matching, whereas the second deals with spatio-temporal database issues. This allows distinct granularities and modalities of analysis of sensor data in space and time. This work was conducted within a project that uses real data, with test conducted on 1000 sensors, during 3 years, in a large French city.