Cardinality estimation has a wide range of applications and is of particular importance in
database systems. Various algorithms have been proposed in the past, and the …

A hands-on approach to tasks and techniques in data stream mining and real-time analytics,
with examples in MOA, a popular freely available open-source software framework. Today …

Abstract Methods for Approximate Query Processing (AQP) are essential for dealing with
massive data. They are often the only means of providing interactive response times when …

The frequent items problem is to process a stream of items and find all items occurring more
than a given fraction of the time. It is one of the most heavily studied problems in data stream …

Data preparation and data profiling comprise many both basic and complex tasks to analyze
a dataset at hand and extract metadata, such as data distributions, key candidates, and …

The frequent items problem is to process a stream of items and find all items occurring more
than a given fraction of the time. It is one of the most heavily studied problems in data stream …

Sketch techniques have undergone extensive development within the past few years. They
are especially appropriate for the data streaming scenario, in which large quantities of data …

Cardinality estimation plays an important role in processing big data. We consider the
challenging problem of computing millions or more distinct count aggregations in a single …

System operators are often interested in extracting different feature streams from multi-
dimensional data streams; and reporting their distributions at regular intervals, including the …

Data analysts often need to characterize a data stream as a first step to its further
processing. Some of the initial insights to be gained include, eg, the cardinality of the data …