TY - BOOK
T1 - Proceedings of the sixth international workshop on knowledge discovery from sensor data (SensorKDD '12)
A2 - Das, D.
A2 - Ganguly, A.
A2 - Chandola, V.
A2 - Omitaomu, O.
A2 - Steinhaeuser, K.
A2 - Gama, J.
A2 - Vatsavai, R.
A2 - Gaber, M.
A2 - Chawla, N.
PY - 2012/8
Y1 - 2012/8
N2 - Wide-area sensor infrastructures, remote sensors, RFIDs, phasor measurements, and wireless sensor networks yield massive volumes of disparate, dynamic, and geographically distributed data. With the recent proliferation of smart-phones and similar GPS enabled mobile devices with several onboard sensors, collection of sensor data is no longer limited to scientific communities, but has reached general public. As such sensors are becoming ubiquitous, a set of broad requirements is beginning to emerge across high-priority applications including adaptability to national or homeland security, critical infrastructures monitoring, smart grids, disaster preparedness and management, greenhouse emissions and climate change, and transportation. The raw data from sensors need to be efficiently managed and transformed to usable information through data fusion, which in turn must be converted to predictive insights via knowledge discovery, ultimately facilitating automated or human-induced tactical decisions or strategic policy based on decision sciences and decision support systems.
The challenges for the knowledge discovery community are expected to be immense. On the one hand are dynamic data streams or events that require real-time analysis methodologies and systems, while on the other hand are static data that require high end computing for generating offline predictive insights, which in turn can facilitate real-time analysis. The online and real-time knowledge discovery imply immediate opportunities as well as intriguing short- and long-term challenges for practitioners and researchers in knowledge discovery. The opportunities would be to develop new data mining approaches and adapt traditional and emerging knowledge discovery methodologies to the requirements of the emerging problems. In addition, emerging societal problems require knowledge discovery solutions that are designed to investigate anomalies, rare events, hotspots, changes, extremes and nonlinear processes, and departures from the normal.
AB - Wide-area sensor infrastructures, remote sensors, RFIDs, phasor measurements, and wireless sensor networks yield massive volumes of disparate, dynamic, and geographically distributed data. With the recent proliferation of smart-phones and similar GPS enabled mobile devices with several onboard sensors, collection of sensor data is no longer limited to scientific communities, but has reached general public. As such sensors are becoming ubiquitous, a set of broad requirements is beginning to emerge across high-priority applications including adaptability to national or homeland security, critical infrastructures monitoring, smart grids, disaster preparedness and management, greenhouse emissions and climate change, and transportation. The raw data from sensors need to be efficiently managed and transformed to usable information through data fusion, which in turn must be converted to predictive insights via knowledge discovery, ultimately facilitating automated or human-induced tactical decisions or strategic policy based on decision sciences and decision support systems.
The challenges for the knowledge discovery community are expected to be immense. On the one hand are dynamic data streams or events that require real-time analysis methodologies and systems, while on the other hand are static data that require high end computing for generating offline predictive insights, which in turn can facilitate real-time analysis. The online and real-time knowledge discovery imply immediate opportunities as well as intriguing short- and long-term challenges for practitioners and researchers in knowledge discovery. The opportunities would be to develop new data mining approaches and adapt traditional and emerging knowledge discovery methodologies to the requirements of the emerging problems. In addition, emerging societal problems require knowledge discovery solutions that are designed to investigate anomalies, rare events, hotspots, changes, extremes and nonlinear processes, and departures from the normal.
M3 - Book
BT - Proceedings of the sixth international workshop on knowledge discovery from sensor data (SensorKDD '12)
PB - ACM
ER -
