Active Data

Fuente: arXiv
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Autores principales: Arthur, Richard, DiDomizio, Virginia, Hoebel, Louis
Formato: Preprint
Publicado: 2026
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author Arthur, Richard
DiDomizio, Virginia
Hoebel, Louis
author_facet Arthur, Richard
DiDomizio, Virginia
Hoebel, Louis
contents In some complex domains, certain problem-specific decompositions can provide advantages over monolithic designs by enabling comprehension and specification of the design. In this paper we present an intuitive and tractable approach to reasoning over large and complex data sets. Our approach is based on Active Data, i.e., data as atomic objects that actively interact with environments. We describe our intuition about how this bottom-up approach improves designs confronting computational and conceptual complexity. We describe an implementation of the base Active Data concepts within the air traffic flow management domain and discuss performance for this implementation.
format Preprint
id arxiv_https___arxiv_org_abs_2604_21044
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Active Data
Arthur, Richard
DiDomizio, Virginia
Hoebel, Louis
Artificial Intelligence
E.2; H.3.1; H.3.3; I.2.11; I.2.4
In some complex domains, certain problem-specific decompositions can provide advantages over monolithic designs by enabling comprehension and specification of the design. In this paper we present an intuitive and tractable approach to reasoning over large and complex data sets. Our approach is based on Active Data, i.e., data as atomic objects that actively interact with environments. We describe our intuition about how this bottom-up approach improves designs confronting computational and conceptual complexity. We describe an implementation of the base Active Data concepts within the air traffic flow management domain and discuss performance for this implementation.
title Active Data
topic Artificial Intelligence
E.2; H.3.1; H.3.3; I.2.11; I.2.4
url https://arxiv.org/abs/2604.21044