Titlebook: Data Stream Management; Processing High-Spee Minos Garofalakis,Johannes Gehrke,Rajeev Rastogi Textbook 2016 Springer-Verlag Berlin Heidelbe

Projection

謙卑

Mining Decision Trees from Streams data from the data stream to make each decision required by the learning process. The method is applicable to essentially any induction algorithm based on discrete search. In this chapter, we illustrate the use of our method by applying it to what is perhaps the most widely used form of data mining: decision tree induction.

使殘廢

玷污

Temporal Dynamics of On-Line Information Streamsill be manageable, but the data stream perspective takes what has generally been a static view of a problem and adds a strong temporal dimension to it. Our focus here is on some of the challenges that this latter issue raises in the settings of text mining, on-line information, and information retrieval.

Obstruction

https://doi.org/10.1007/978-3-642-58360-5antile summaries of data streams using small space. We highlight connections among these ideas, and how techniques developed for one setting sometimes naturally lend themselves to a seemingly different setting.

原告

Antagonism

沉積物

Sketch-Based Multi-Query Processing over Data Streamsto improve the quality of the approximation provided by our algorithms. The key idea is to intelligently partition the domain of the underlying attribute(s) and, thus, decompose the sketching problem in a way that provably tightens our guarantees.

梯田

Textbook 2016lgorithms, systems, and applications. The collection of chapters, contributed by authorities in the field, offers a comprehensive introduction to both the algorithmic/theoretical foundations of data streams, as well as the streaming systems and applications built in different domains..A short introd

抒情短詩(shī)