Longitudinal study of a building-scale RFID ecosystem
Proceedings of the 7th international conference on Mobile systems, applications, and services
Lahar demonstration: warehousing Markovian streams
Proceedings of the VLDB Endowment
Specification and verification of complex location events with panoramic
Pervasive'10 Proceedings of the 8th international conference on Pervasive Computing
| Hi-index | 0.00 |
Building applications on top of sensor data streams is challenging because sensor data is noisy. A model-based view can reduce noise by transforming raw sensor streams into streams of probabilistic state estimates, which smooth out errors and gaps. The authors propose a novel model-based view, the Markovian stream, to represent correlated probabilistic sequences. Applications interested in evaluating event queries — extracting sophisticated state sequences — can improve robustness by querying a Markovian stream view instead of querying raw data directly. The primary challenge is to properly handle the Markovian stream's correlations.