International Workshop on
Knowledge Discovery from Sensor Data
(SensorKDD Workshop)


















The SensorKDD-Workshop

Wide-area sensor infrastructures, remote sensors, RFIDs, phasor measurements, and wireless sensor networks yield massive volumes of disparate, dynamic, and geographically distributed data. 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, climate change, disaster preparedness and management, and critical infrastructures monitoring. 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.

The SensorKDD workshop brings together researchers from academia, government, and the industry working in the following areas and applications:

  • Offline Knowledge Discovery
    1. Predictive analysis from geographically distributed and heterogeneous data
    2. Computationally efficient approaches for mining unusual patterns, specifically, anomalies, extremes, nonlinear processes and change, from massive and disparate space-time data
  • Online Knowledge Discovery
    1. Real-time analysis of dynamic and distributed data, including streaming and event-based data
    2. Mining from continuous streams of time-changing data and mining from ubiquitous data
    3. Efficient algorithms to detect deviations from the normal in real-time
    4. Resource-aware algorithms for distributed mining
  • Decision and Policy Aids
    1. Coordinated offline discovery and online analysis with feedback loops
    2. Combination of knowledge discovery and decision scientific processes
    3. Facilitation of faster and reliable tactical decisions as well as prudent and insightful longer term policies
  • Theory
    1. Distributed data stream models
    2. Theoretical frameworks for distributed stream mining
  • Case Studies
    1. Success stories in national or global priority applications
    2. Real-world problem design and knowledge discovery requirements




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