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...
This paper investigates the problem of writing data to passive RFID tag memory and proposes a reprocessing model for assuring the atomicity and durability of writing transactions i...
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...
Complex queries over high speed data streams often need to rely on approximations to keep up with their input. The research community has developed a rich literature on approximat...
Theodore Johnson, S. Muthukrishnan, Irina Rozenbau...
Random sampling is an appealing approach to build synopses of large data streams because random samples can be used for a broad spectrum of analytical tasks. Users are often inter...