Dissemination of Models over Time-Varying Data
Dissemination of time-varying data is essential in many applications, such as sensor networks, patient monitoring, stock tickers, etc. Often, the raw data have to go through some form of pre-processing, such as cleaning, smoothing, etc, be-fore being disseminated. Such pre-processing often applies mathematical or statistical models to transform the large volumes of raw, point-based data into a much smaller number of piece-wise continuous functions. In such cases, the necessity to distribute data models instead of raw data may arise. Nevertheless, model dissemination has received very little attention so far.