CyberIntel ⬡ News
★ Saved ◆ Cyber Reads
← Back ◬ AI & Machine Learning Apr 24, 2026

Active Data

arXiv AI Archived Apr 24, 2026 ✓ Full text saved

arXiv:2604.21044v1 Announce Type: new Abstract: 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 impr

Full text archived locally
✦ AI Summary · Claude Sonnet


    Computer Science > Artificial Intelligence [Submitted on 22 Apr 2026] Active Data Richard Arthur, Virginia DiDomizio, Louis Hoebel 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. Comments: 9 pages, 7 figures, 2 tables Subjects: Artificial Intelligence (cs.AI) ACM classes: E.2; H.3.1; H.3.3; I.2.11; I.2.4 Cite as: arXiv:2604.21044 [cs.AI]   (or arXiv:2604.21044v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.21044 Focus to learn more Journal reference: In: Meersman, R., Tari, Z., Schmidt, D.C. (eds) OTM 2003. Lecture Notes in Computer Science, vol 2888. Springer, Berlin, Heidelberg Related DOI: https://doi.org/10.1007/978-3-540-39964-3_91 Focus to learn more Submission history From: Richard Arthur [view email] [v1] Wed, 22 Apr 2026 19:42:09 UTC (463 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
    💬 Team Notes
    Article Info
    Source
    arXiv AI
    Category
    ◬ AI & Machine Learning
    Published
    Apr 24, 2026
    Archived
    Apr 24, 2026
    Full Text
    ✓ Saved locally
    Open Original ↗