Data Mining for Diagnostic Debugging in Sensor Networks: Preliminary Evidence and Lessons Learned.- Monitoring Incremental Histogram Distribution for Change Detection in Data Streams.- Situation-Aware Adaptive Visualization for Sensory Data Stream Mining.- Unsupervised Plan Detection with Factor Graphs.- WiFi Miner: An Online Apriori-Infrequent Based Wireless Intrusion System.- Probabilistic Analysis of a Large-Scale Urban Traffic Sensor Data Set.- Spatio-temporal Outlier Detection in Precipitation Data.- Large-Scale Inference of Network-Service Disruption upon Natural Disasters.- An Adaptive Sensor Mining Framework for Pervasive Computing Applications.- A Simple Dense Pixel Visualization for Mobile Sensor Data Mining.- Incremental Anomaly Detection Approach for Characterizing Unusual Profiles.- Spatiotemporal Neighborhood Discovery for Sensor Data.
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This volume contains extended papers from Sensor-KDD 2008, the Second - ternational Workshop on Knowledge Discovery from Sensor Data. The second Sensor-KDDworkshopwasheldinLasVegasonAugust24,2008,inconjunction with the 14th ACM SIGKDD InternationalConference on KnowledgeDiscovery and Data Mining. Wide-area sensor infrastructures, remote sensors, and wireless sensor n- works, RFIDs, yield massive volumes of disparate, dynamic, and geographically distributeddata.Assuchsensorsarebecomingubiquitous,asetofbroadrequi- ments is beginning to emerge across high-priority applications including dis- ter preparedness and management, adaptability to climate change, national or homelandsecurity,andthe managementofcriticalinfrastructures.Therawdata from sensors need to be e ciently managed and transformed to usable infor- tion 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 s- port systems. The expected ubiquity of sensors in the near future, combined with the cr- ical roles they are expected to play in high-priority application solutions, points to an era of unprecedented growth and opportunities. The main motivation for the Sensor-KDD series of workshops stems from the increasing need for a forum to exchange ideas and recent research results, and to facilitate coll- oration and dialog between academia, government, and industrial stakeho- ers. This is clearly re ected in the successful organization of the rst workshop alongwiththe ACMKDD-2007conference,whichwasattendedbymorethanseventyregistered participants, and resulted in an edited book (CRC Press, ISBN-9781420082326, 2008), and a special issue in the Intelligent Data Analysis journal (Volume 13, Number 3, 2009). 240 pp. Englisch. Bestandsnummer des Verkäufers 9783642125188
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Kartoniert / Broschiert. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Fast-track conference proceedings State-of-the-art researchUnique visibilityData Mining for Diagnostic Debugging in Sensor Networks: Preliminary Evidence and Lessons Learned.- Monitoring Incremental Histogram Distribution for Change Detection in Data St. Bestandsnummer des Verkäufers 5049890
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Data Mining for Diagnostic Debugging in Sensor Networks: Preliminary Evidence and Lessons Learned.- Monitoring Incremental Histogram Distribution for Change Detection in Data Streams.- Situation-Aware Adaptive Visualization for Sensory Data Stream Mining.- Unsupervised Plan Detection with Factor Graphs.- WiFi Miner: An Online Apriori-Infrequent Based Wireless Intrusion System.- Probabilistic Analysis of a Large-Scale Urban Traffic Sensor Data Set.- Spatio-temporal Outlier Detection in Precipitation Data.- Large-Scale Inference of Network-Service Disruption upon Natural Disasters.- An Adaptive Sensor Mining Framework for Pervasive Computing Applications.- A Simple Dense Pixel Visualization for Mobile Sensor Data Mining.- Incremental Anomaly Detection Approach for Characterizing Unusual Profiles.- Spatiotemporal Neighborhood Discovery for Sensor Data.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 240 pp. Englisch. Bestandsnummer des Verkäufers 9783642125188
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