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Springer.Learning.from.Data.Streams.Processing.Techniques.in.Sensor.Networks.Nov.2007.pdf
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上传时间: 2008-12-12
详细说明： Data streams are everywhere these days. Look around and you will find sources of information that are continuously generating data. Our everyday life is now getting stuffed with devices that are emanating many data streams. Cell-phones, cars, security sensors, and televisions are just some examples. More are on their way. Smart houses of the future are likely to have many different types of sensors for sensing and adapting the domestic services to the changing needs of the inhabitants. For example, artificial motion, audio, and visual sensory systems are already being used to revolutionize the face of assisted at-home living for senior citizens. Cars are also increasingly becoming rich sources of data. The vehicle data bus and onboard GPS devices are tapped for extracting vehicle-health, fuel consumption, driver behavior, and location-dependent data. Data streams are common in many commercial and law-enforcement-related surveillance applications. Many of the public places that we often visit (e.g. airports, metros) and places where we work are monitored round the clock using various types of sensors. Perimeters of secured establishments and borders of countries are getting monitored using sensors. Wearable computers embedded in clothes and accessories for measuring our body parameters are increasingly getting popular among sport enthusiasts, patients, military, and researchers studying human computer interaction. In short, data streams are widely prevalent and in fact these days it is somewhat hard to find any source of information that does not generate continuous streams of data. ...展开收缩
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