Apologies for multiple postings. (Note: Due to multiple requests for the extension of deadline, we are extending Sensor-KDD paper submission by a week to June 6th.)
2nd International Workshop on Knowledge Discovery from Sensor Data (Sensor-KDD, 2008) 24th August 2008, Las Vegas, NV In conjunction with ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'08) 24-27 August 2008, Las Vegas, NV. URL: http://www.ornl.gov/sci/knowledgediscovery/SensorKDD-2008/index.htm FINAL CALL FOR PAPERS Important dates * May 28, 2008: June 6, 2008. Extended submission date for full papers (Final) * June 23, 2008: Author notification * June 30, 2008: Submission of Camera-ready papers * August 24, 2008: Full-day Workshop at ACM SIGKDD Conference, Las Vegas, USA ***NEW*** * Best Student and Research Paper Awards. * Post workshop proceedings to be published by Springer as LNCS volume. * In addition to paper and poster sessions this workshop also features invited talks and a panel consisting of leading experts from Academia, Government and Industry. * Please check web-page periodically for more details and updates. Brief Description Wide-area sensor infrastructures, remote sensors, and wireless sensor networks, RFIDs, 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 disaster preparedness and management, adaptability to climate change, national or homeland security, and the management of critical infrastructures. 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, dynamic data streams or events require real-time analysis methodologies and systems, while on the other hand centralized processing through high end computing is also required for generating offline predictive insights, which in turn can facilitate real-time analysis. Problems ranging from mitigating hurricane impacts, preparing for abrupt climate change, preventing terror attacks and monitoring improvised explosive devices require knowledge discovery solutions designed to detect and analyze anomalies, change, extremes and nonlinear processes, and departures from normal behavior. In order to be relevant to society, solutions must eventually reach end-to-end, covering the entire path from raw sensor data to real-world decisions. Suggested Topics This workshop seeks to bring together researchers from academia, government and the private sector to facilitate cross-disciplinary exchange of ideas in the area of knowledge discovery from sensor and stream data. Interested authors should submit high quality research papers (up to 9 pages) using the standard ACM format found http://www.acm.org/sigs/publications/proceedings-templates in MS Word or PDF format as an email attachment to [EMAIL PROTECTED] Papers, accepted for presentation at the workshop will be distributed along with KDD proceedings. Post workshop proceedings (extended papers) will be published by Springer as an LNCS volume. All submitted papers will be refereed for quality and originality by the Program Committee. The major topics of the workshop include but are not limited to: A. Offline Knowledge Discovery 1. Predictive analysis from geographically distributed and temporally spread heterogeneous data 2. Computationally efficient approaches for mining unusual patterns, including but not limited to anomalies, outliers, extremes, nonlinear processes, and changes from massive and disparate space-time data C. 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 5. Monitoring and surveillance based on a single or multiple sensor feeds D. 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 and strategic decisions E. Theory 1. Distributed data stream models 2. Theoretical frameworks for distributed stream mining F. Case Studies 1. Success stories, especially about end-to-end solutions, for national or global priorities 2. Real-world problem design and knowledge discovery requirements Workshop Organizers " Ranga Raju Vatsavai, Oak Ridge National Laboratory, Oak Ridge, TN " Olufemi A. Omitaomu, Oak Ridge National Laboratory, Oak Ridge, TN " Joao Gama, University of Porto, Portugal " Nitesh V. Chawla, University of Notre Dame, IN. " Mohamed Medhat Gaber, CSIT, Monash University, Australia " Auroop R. Ganguly, Oak Ridge National Laboratory, Oak Ridge, TN
