Data Stream Management Systems are useful when large volumes of data need to be processed in real time. Examples include monitoring network traffic, monitoring financial transacti...
Theodore Johnson, S. Muthukrishnan, Vladislav Shka...
Reservoir sampling is a well-known technique for random sampling over data streams. In many streaming applications, however, an input stream may be naturally heterogeneous, i.e., c...
The massive data streams observed in network monitoring, data processing and scientific studies are typically too large to store. For many applications over such data, we must ob...
A fundamental problem in data management is to draw and maintain a sample of a large data set, for approximate query answering, selectivity estimation, and query planning. With la...
Graham Cormode, S. Muthukrishnan, Ke Yi, Qin Zhang