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For the upcoming IoT (Internet of things) era, plethora of data from a variety of sensors needs to be processed on a real time basis for improving system responsiveness. Due to the increasing modality of sensors, data streaming analytics to deal with high dimensional data becomes a critical ability. In addition, concept drift also needs to be addressed since an IoT-enabled environment is dynamic in...
In a monopolistic cable television (CATV) service market, it is generally believed that tiered charging is beneficial to consumer surplus as compared to flat rate charging while it is likely to reduce supplier income. Can the addition of a carefully designed Price-Channel-Downscaled (PCD) package to a flat rate (FR) package stimulate enough new subscribers and achieve Pareto improvement between cable...
Process mining is one key technology in PASI, it can extract the relevant knowledge according to the event log information recorded in the information system, then restructure a process instance model and makes all the information track in the event log can meet the process model. In this paper, based on ¦Á algorithm of process mining, we propose the process mining ¦Ã algorithm which can discover...
Reliable recognition of activities from cluttered sensory data is challenging and important for a smart home to enable various activity-aware applications. In addition, understanding a user's preferences and then providing corresponding services is substantial in a smart home environment. Traditionally, activity recognition and preference learning were dealt with separately. In this work, we aim to...
It is desirable to know a resident's on-going activities before a robot or a smart system can provide attentive services to meet real human needs. This work addresses the problem of learning and recognizing human daily activities in a dynamic environment. Most currently available approaches learn offline activity models and recognize activities of interest on a real time basis. However, the activity...
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