Revealing Daily Practices through the Study of

We discuss micro-mobility, which is the study of the interrelated movements of people, devices, and other actors at a local scale. We also describe an ongoing study that makes use of highly accurate sensing technology to record the paths of residents and their wireless laptop computers. The data from the sensors, along with household interviews, allows us to explore the patterns of everyday behavior involving people and their technology as revealed by their daily movements about their homes, with the goal of motivating new designs for domestic technology that are grounded in actual practice

Probabilistic data management for pervasive computing: the data furnace project

Summarization: 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.Παρουσιάστηκε στο: IEEE Data Engineering Bulleti