Titlebook: Knowledge Discovery from Sensor Data; Second International Mohamed Medhat Gaber,Ranga Raju Vatsavai,Auroop R. Conference proceedings 2010 S

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Situation-Aware Adaptive Visualization for Sensory Data Stream Mining, user-interactions, real-time decision making and comprehension of the results of mining algorithms. In this paper we propose a novel architecture for situation-aware adaptive visualization that applies intelligent visualization techniques to data stream mining of sensory data. The proposed architec

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WiFi Miner: An Online Apriori-Infrequent Based Wireless Intrusion System,orks (WLAN). Currently, almost all devices are Wi-Fi (Wireless Fidelity) capable and can access WLAN. This paper proposes an Intrusion Detection System, WiFi Miner, which applies an infrequent pattern association rule mining Apriori technique to wireless network packets captured through hardware sen

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Probabilistic Analysis of a Large-Scale Urban Traffic Sensor Data Set, as the basis for experiments in many research papers. In this paper we report on a large case-study involving statistical data mining of over 100 million measurements from 1700 freeway traffic sensors over a period of seven months in Southern California. We discuss the challenges posed by the wide

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Spatio-temporal Outlier Detection in Precipitation Data,o understand and interpret it. Due to the limitations of current data mining techniques, new techniques to handle this data need to be developed. We propose a spatio-temporal outlier detection algorithm called Outstretch, which discovers the outlier movement patterns of the top-. spatial outliers ov

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Large-Scale Inference of Network-Service Disruption upon Natural Disasters,and the impact of natural disasters to networks, it is important to localize and analyze network-service disruption after natural disasters occur..This work studies an inference of network-service disruption caused by the real natural disaster, Hurricane Katrina. We perform inference using large-sca

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An Adaptive Sensor Mining Framework for Pervasive Computing Applications,ing data source is dynamic and the patterns change.  We introduce a new adaptive mining framework that detects patterns in sensor data, and more importantly, adapts to the changes in the underlying model.  In our framework, the frequent and periodic patterns of data are first discovered by the Frequ

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A Simple Dense Pixel Visualization for Mobile Sensor Data Mining,al representations, which most of the time require screen resolutions that are not available in small transient mobile devices. Moreover, when data presents cyclic behaviors, such as in the electricity domain, predictive models may tend to give higher errors in certain recurrent points of time, but

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