Probabilistic Data Management for Pervasive Computing: The Data Furnace Project.
Minos Garofalakis, Kurt P. Brown, Michael J. M. Franklin, Joseph M. Hellerstein, Daisy Zhe Wang, Eirinaios Michelakis, Liviu Tancau, Eugene W. Wu, Shawn R. Jeffery, Ryan Aipperspach · 2006
The wide deployment of wireless sensor and RFID (Radio Frequency IDentification) devices is one of the key enablers for next-generation pervasive computing applications, including large-scale environmental monitoring and control, context-aware computing, and “smart digital homes”. Sensory readings are inherently unreliable and typically exhibit strong temporal and spatial correlations (within and across different sensing devices); effective reasoning over such unreliable streams introduces a host of new data management challenges. The Data Furnace project at Intel Research and UC-Berkeley aims to build a probabilistic data management infrastructure for pervasive computing environments that handles the uncertain nature of such data as a first-class citizen through a principled framework grounded in probabilistic models and inference techniques. 1