Streaming data are increasingly present in real-world applications such as sensor
measurements, satellite data feed, stock market, and financial data. The main characteristics …

Due to the increasing privacy concerns and data regulations, training data have been
increasingly fragmented, forming distributed databases of multiple “data silos”(eg, within …

Compressive sampling offers a new paradigm for acquiring signals that are compressible
with respect to an orthonormal basis. The major algorithmic challenge in compressive …

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 …

(MATH) A locality sensitive hashing scheme is a distribution on a family \F of hash functions
operating on a collection of objects, such that for two objects x, y, Pr h εF h (x)= h (y)= sim (x …

We introduce a new sublinear space data structure—the count-min sketch—for summarizing
data streams. Our sketch allows fundamental queries in data stream summarization such as …

In this overview paper we motivate the need for and research issues arising from a new
model of data processing. In this model, data does not take the form of persistent relations …

We present a 1-pass algorithm for estimating the most frequent items in a data stream using
very limited storage space. Our method relies on a novel data structure called a count …

In the data stream scenario, input arrives very rapidly and there is limited memory to store
the input. Algorithms have to work with one or few passes over the data, space less than …

Compressed sensing is a new concept in signal processing where one seeks to minimize
the number of measurements to be taken from signals while still retaining the information …