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Probabilistic data management for pervasive computing: The data furnace project (2006)
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| Venue: | IEEE Data Eng. Bull |
| Citations: | 21 - 0 self |
BibTeX
@ARTICLE{Garofalakis06probabilisticdata,
author = {Minos Garofalakis and Kurt P. Brown and Michael J. Franklin and Joseph M. Hellerstein and Daisy Zhe Wang},
title = {Probabilistic data management for pervasive computing: The data furnace project},
journal = {IEEE Data Eng. Bull},
year = {2006},
volume = {29},
pages = {2006}
}
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Abstract
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
Keyphrases
data furnace project pervasive computing probabilistic data management first-class citizen wireless sensor smart digital home uncertain nature context-aware computing inference technique intel research wide deployment probabilistic data management infrastructure principled framework radio frequency identification unreliable stream key enablers sensory reading pervasive computing environment next-generation pervasive computing application new data management challenge probabilistic model uc-berkeley aim large-scale environmental monitoring effective reasoning spatial correlation
