This paper studies Data Stream Management Systems that combine real-time data streams with historical data, and hence access incoming streams and archived data simultaneously. A s...
In contrast to traditional machine learning algorithms, where all data are available in batch mode, the new paradigm of streaming data poses additional difficulties, since data sam...
The composite signal flow model of computation targets systems with significant control and data processing parts. It builds on the data flow and synchronous data flow models ...
We present a new technique for using samples to estimate join cardinalities. This technique, which we term "end-biased samples," is inspired by recent work in network tr...
Data stream applications have made use of statistical summaries to reason about the data using nonparametric tools such as histograms, heavy hitters, and join sizes. However, rela...