Existing density-based data stream clustering algorithms use a two-phase scheme approach consisting of an online phase, in which raw data is processed to gather summary statistics...
Agostino Forestiero, Clara Pizzuti, Giandomenico S...
Data stream management systems usually have to process many long-running queries that are active at the same time. Multiple queries can be evaluated more efficiently together tha...
Mingsheng Hong, Mirek Riedewald, Christoph Koch, J...
In the past years, the theory and practice of machine learning and data mining have been focused on static and finite data sets from where learning algorithms generate a static m...
Abstract— It is widely realized that the integration of information retrieval (IR) and database (DB) techniques provides users with a broad range of high quality services. A new ...
—Sampling is used as a universal method to reduce the running time of computations – the computation is performed on a much smaller sample and then the result is scaled to comp...
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...
A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is more likely to be c...
Efficient one-pass computation of F0, the number of distinct elements in a data stream, is a fundamental problem arising in various contexts in databases and networking. We consid...
The overwhelming flow of information in many data stream applications forces many companies to outsource to a third-party the deployment of a Data Stream Management System (DSMS) f...
Ke Yi, Feifei Li, Marios Hadjieleftheriou, George ...
Abstract-- Many applications are driven by evolving data -patterns in web traffic, program execution traces, network event logs, etc., are often non-stationary. Building prediction...
Shixi Chen, Haixun Wang, Shuigeng Zhou, Philip S. ...