Publication
Title
Estimating mutual information on data streams
Author
Abstract
Mutual information is a well-established and broadly used concept in information theory. It allows to quantify the mutual dependence between two variables an essential task in data analysis. For static data, a broad range of techniques addresses the problem of estimating mutual information. However, the assumption of static data is not applicable for today's dynamic data sources such as data streams: In contrast to static approaches, an online estimator must be able to deal with the evolving, changing, and infinite nature of the stream. Furthermore, some tasks require the estimation to be available online while processing the raw data stream. Our proposed solution MISE (Mutual Information Stream Estimation) allows a user to issue mutual information queries in arbitrary time windows. As a key feature, we introduce a novel sampling scheme, which ensures an equal treatment of queries over multiple time scales, e.g., ranging from milliseconds up to decades. We thoroughly analyze the requirements of such a multiscale sampling scheme, and evaluate the resulting quality of MISE in a broad range of experiments.
Language
English
Source (journal)
PROCEEDINGS OF THE 27TH INTERNATIONAL CONFERENCE ON SCIENTIFIC AND
STATISTICAL DATABASE MANAGEMENT
Source (book)
27th International Conference on Scientific and Statistical Database, Management (SSDBM), JUN 29-JUL 01, 2015, Univ Calif San Diego, Univ Calif San Diego, San Diego, CA
Publication
New york : Assoc computing machinery , 2015
ISBN
978-1-4503-3709-0
DOI
10.1145/2791347.2791348
Volume/pages
(2015) , 12 p.
ISI
000382164600001
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 06.10.2016
Last edited 09.10.2023
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