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Progress in Artificial Intelligence

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01.08.2014 | Regular Paper

Constructing fading histograms from data streams

verfasst von: Raquel Sebastião, João Gama, Teresa Mendonça

Erschienen in: Progress in Artificial Intelligence | Ausgabe 1/2014

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Abstract

The ability to collect data is changing drastically. Nowadays, data are gathered in the form of transient and finite data streams. Memory restrictions preclude keeping all received data in memory. When dealing with massive data streams, it is mandatory to create compact representations of data, also known as synopses structures or summaries. Reducing memory occupancy is of utmost importance when handling a huge amount of data. This paper addresses the problem of constructing histograms from data streams under error constraints. When constructing online histograms from data streams there are two main characteristics to embrace: the updating facility and the error of the histogram. Moreover, in dynamic environments, besides the need of compact summaries to capture the most important properties of data, it is also essential to forget old data. Therefore, this paper presents sliding histograms and fading histograms, an abrupt and a smooth strategies to forget outdated data.

Vorheriger Artikel Unbalanced linguistic approach for venture investment evaluation with risk attitudes

Nächster Artikel Undersampled -means approach for handling imbalanced distributed data

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Titel: Constructing fading histograms from data streams
verfasst von: Raquel Sebastião
João Gama
Teresa Mendonça
Publikationsdatum: 01.08.2014
Verlag: Springer Berlin Heidelberg
Erschienen in: Progress in Artificial Intelligence / Ausgabe 1/2014
Print ISSN: 2192-6352
Elektronische ISSN: 2192-6360
DOI: https://doi.org/10.1007/s13748-014-0050-9

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