標(biāo)題: Titlebook: Knowledge Discovery from Sensor Data; Second International Mohamed Medhat Gaber,Ranga Raju Vatsavai,Auroop R. Conference proceedings 2010 S [打印本頁] 作者: 不要提吃飯 時間: 2025-3-21 19:14
書目名稱Knowledge Discovery from Sensor Data影響因子(影響力)
書目名稱Knowledge Discovery from Sensor Data影響因子(影響力)學(xué)科排名
書目名稱Knowledge Discovery from Sensor Data網(wǎng)絡(luò)公開度
書目名稱Knowledge Discovery from Sensor Data網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Knowledge Discovery from Sensor Data被引頻次
書目名稱Knowledge Discovery from Sensor Data被引頻次學(xué)科排名
書目名稱Knowledge Discovery from Sensor Data年度引用
書目名稱Knowledge Discovery from Sensor Data年度引用學(xué)科排名
書目名稱Knowledge Discovery from Sensor Data讀者反饋
書目名稱Knowledge Discovery from Sensor Data讀者反饋學(xué)科排名
作者: Maximize 時間: 2025-3-21 23:39
Spatiotemporal Neighborhood Discovery for Sensor Data, discretize temporal intervals. These methods were tested on real life datasets including (a) sea surface temperature data from the Tropical Atmospheric Ocean Project (TAO) array in the Equatorial Pacific Ocean and (b)highway sensor network data archive. We have found encouraging results which are validated by real life phenomenon.作者: 發(fā)牢騷 時間: 2025-3-22 00:34
Unsupervised Plan Detection with Factor Graphs,levant locations. Instead, we introduce 2 unsupervised methods to simultaneously estimate model parameters and hidden values within a Factor graph representing agent transitions over time. We evaluate our approach by applying it to goal prediction in a GPS dataset tracking 1074 ships over 5 days in the English channel.作者: interpose 時間: 2025-3-22 04:53
Probabilistic Analysis of a Large-Scale Urban Traffic Sensor Data Set,n or simple thresholding techniques to identify these anomalies. We describe the application of probabilistic modeling and unsupervised learning techniques to this data set and illustrate how these approaches can successfully detect underlying systematic patterns even in the presence of substantial noise and missing data.作者: 欲望小妹 時間: 2025-3-22 09:12 作者: 耕種 時間: 2025-3-22 13:05
Data Mining for Diagnostic Debugging in Sensor Networks: Preliminary Evidence and Lessons Learned,osis in the face of non-reproducible behavior, high interactive complexity, and resource constraints. Several examples are given to finding bugs in real sensor network code using the tools developed, demonstrating the efficacy of the approach.作者: 解決 時間: 2025-3-22 17:27
An Adaptive Sensor Mining Framework for Pervasive Computing Applications,nt in pervasive computing applications, such as the startup triggers and temporal information. In this paper, we present a description of our mining framework and validate the approach using data collected in the CASAS smart home testbed.作者: 野蠻 時間: 2025-3-23 00:21 作者: 柏樹 時間: 2025-3-23 02:16 作者: STELL 時間: 2025-3-23 08:06
Pari Delir Haghighi,Brett Gillick,Shonali Krishnaswamy,Mohamed Medhat Gaber,Arkady Zaslavsky作者: 工作 時間: 2025-3-23 12:26 作者: Modicum 時間: 2025-3-23 17:05 作者: 設(shè)施 時間: 2025-3-23 19:36
Large-Scale Inference of Network-Service Disruption upon Natural Disasters,bilities occurred after the landfall. The majority (73%) of unreachable subnets lasted longer than four weeks showing that Katrina caused extreme damage on networks and a slow recovery..Network-service disruption is inevitable after large-scale natural disasters occur. Thus, it is crucial to have ef作者: 懦夫 時間: 2025-3-24 01:30 作者: QUAIL 時間: 2025-3-24 04:36 作者: Diaphragm 時間: 2025-3-24 10:01
Jon Hutchins,Alexander Ihler,Padhraic Smyth, and deduce linearization theorems. The results are illustrated using temporal and full discretizations of evolutionary differential equations.978-3-642-14257-4978-3-642-14258-1Series ISSN 0075-8434 Series E-ISSN 1617-9692 作者: 關(guān)心 時間: 2025-3-24 12:06
Elizabeth Wu,Wei Liu,Sanjay Chawla, and deduce linearization theorems. The results are illustrated using temporal and full discretizations of evolutionary differential equations.978-3-642-14257-4978-3-642-14258-1Series ISSN 0075-8434 Series E-ISSN 1617-9692 作者: Conspiracy 時間: 2025-3-24 18:27
Supaporn Erjongmanee,Chuanyi Ji,Jere Stokely,Neale Hightower, and deduce linearization theorems. The results are illustrated using temporal and full discretizations of evolutionary differential equations.978-3-642-14257-4978-3-642-14258-1Series ISSN 0075-8434 Series E-ISSN 1617-9692 作者: 保存 時間: 2025-3-24 19:25
Parisa Rashidi,Diane J. Cook, and deduce linearization theorems. The results are illustrated using temporal and full discretizations of evolutionary differential equations.978-3-642-14257-4978-3-642-14258-1Series ISSN 0075-8434 Series E-ISSN 1617-9692 作者: 觀點 時間: 2025-3-24 23:20
Pedro Pereira Rodrigues,Jo?o Gama, and deduce linearization theorems. The results are illustrated using temporal and full discretizations of evolutionary differential equations.978-3-642-14257-4978-3-642-14258-1Series ISSN 0075-8434 Series E-ISSN 1617-9692 作者: 山間窄路 時間: 2025-3-25 05:57
Yi Fang,Olufemi A. Omitaomu,Auroop R. Ganguly, and deduce linearization theorems. The results are illustrated using temporal and full discretizations of evolutionary differential equations.978-3-642-14257-4978-3-642-14258-1Series ISSN 0075-8434 Series E-ISSN 1617-9692 作者: Statins 時間: 2025-3-25 11:06
Michael P. McGuire,Vandana P. Janeja,Aryya Gangopadhyay, and deduce linearization theorems. The results are illustrated using temporal and full discretizations of evolutionary differential equations.978-3-642-14257-4978-3-642-14258-1Series ISSN 0075-8434 Series E-ISSN 1617-9692 作者: 現(xiàn)暈光 時間: 2025-3-25 14:47 作者: NAV 時間: 2025-3-25 17:09
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/k/image/543866.jpg作者: 極小量 時間: 2025-3-25 21:56
https://doi.org/10.1007/978-3-642-12519-5data mining; disaster management; knowledge discovery; online; remote sensors; sensor mining; sensor netwo作者: ABYSS 時間: 2025-3-26 02:26 作者: antedate 時間: 2025-3-26 06:58 作者: output 時間: 2025-3-26 09:00 作者: oxidant 時間: 2025-3-26 15:15 作者: Infirm 時間: 2025-3-26 19:18 作者: 好忠告人 時間: 2025-3-27 00:40
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作者: Itinerant 時間: 2025-3-27 02:25 作者: harrow 時間: 2025-3-27 06:18
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作者: 發(fā)現(xiàn) 時間: 2025-3-27 09:31
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 作者: ZEST 時間: 2025-3-27 17:18
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作者: indubitable 時間: 2025-3-27 21:35
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作者: Confess 時間: 2025-3-27 23:18
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作者: Collected 時間: 2025-3-28 02:30
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 作者: 充足 時間: 2025-3-28 08:40 作者: 蚊子 時間: 2025-3-28 12:39 作者: Perennial長期的 時間: 2025-3-28 16:42 作者: 思想流動 時間: 2025-3-28 18:53 作者: Judicious 時間: 2025-3-29 02:40 作者: fetter 時間: 2025-3-29 04:34
Parisa Rashidi,Diane J. Cooksupplementary material: Nonautonomous dynamical systems provide a mathematical framework for temporally changing phenomena, where the law of evolution varies in time due to seasonal, modulation, controlling or even random effects. Our goal is to provide an approach to the corresponding geometric the作者: 維持 時間: 2025-3-29 09:18 作者: 一回合 時間: 2025-3-29 12:04
Yi Fang,Olufemi A. Omitaomu,Auroop R. Gangulysupplementary material: Nonautonomous dynamical systems provide a mathematical framework for temporally changing phenomena, where the law of evolution varies in time due to seasonal, modulation, controlling or even random effects. Our goal is to provide an approach to the corresponding geometric the
