Section: New Results

Data Stream Mining

Probabilistic data management has shown growing interest to deal with uncertain data. In [29] , we focus on probabilistic time series with high volumes of data, which requires efficient compression techniques. To date, most of the work on probabilistic data reduction uses synopses that minimize the error of representation wrt. the original data. However, in most cases, the compressed data will be meaningless for usual queries involving aggregation operators such as SUM or AVG. We propose PHA (Probabilistic Histogram Aggregation), a compression technique whose objective is to minimize the error of such queries over compressed probabilistic data. We incorporate the aggregation operator given by the end-user directly in the compression technique, and obtain much lower error in the long term. We also adopt a global error aware strategy in order to manage large sets of probabilistic time series, where the available memory is carefully balanced between the series, according to their individual variability.