Probabilistic Data Management for Pervasive Computing: The Data Furnace Project

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.

• Type: Journal
• Year: 2006
• Where: DEBU
• Authors: Minos N. Garofalakis, Kurt P. Brown, Michael J. Franklin, Joseph M. Hellerstein, Daisy Zhe Wang, Eirinaios Michelakis, Liviu Tancau, Eugene Wu 0002, Shawn R. Jeffery, Ryan Aipperspach
• Keywords: Data Management, DEBU 2006, Pervasive Computing, Pervasive Computing Environments