The dQUOB system satis es client need for speci c information from high-volume data streams. The data streams we speak of are the ow of data existing during large-scale visualizat...
Data streams emerged as a critical model for multiple applications that handle vast amounts of data. One of the most influential and celebrated papers in streaming is the “AMS...
Current developments in processing data streams are based on the best-effort principle and therefore not adequate for many application areas. When sensor data is gathered by inte...
In data stream applications, data arrive continuously and can only be scanned once as the query processor has very limited memory (relative to the size of the stream) to work with...
Nick Koudas, Beng Chin Ooi, Kian-Lee Tan, Rui Zhan...
Detecting changes in a data stream is an important area of research with many applications. In this paper, we present a novel method for the detection and estimation of change. In...
We propose a space-efficient scheme for summarizing multidimensional data streams. Our sketch can be used to solve spatial versions of several classical data stream queries effici...
John Hershberger, Nisheeth Shrivastava, Subhash Su...
Clustering is a difficult problem especially when we consider the task in the context of a data stream of categorical attributes. In this paper, we propose SCLOPE, a novel algorith...
Mining massive temporal data streams for significant trends, emerging buzz, and unusually high or low activity is an important problem with several commercial applications. In th...
Processing data streams with Quality-ofService (QoS) guarantees is an emerging area in existing streaming applications. Although it is possible to negotiate the result quality and...
Abstract. Todays data stream management systems (DSMSs) lack security functionality. Based on adversary scenarios we show how a DSMS architecture can be protected. We sketch a gene...