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SAIVE: Selecting AI Valuable Entities — arXiv:2609.36512

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.36512
License (as archived): CC0-1.0
Archived: 2026-10-07T19:16:18.470Z
SHA-256 of the archived text: 46c5bcb3e561b15997e9662adcc859c736d4bc4214ae64d55b75ee744b8c148f
Stable URL of this page: https://metraipolis.com/documento/dc_2f577416098a

How to cite

SAIVE: Selecting AI Valuable Entities — arXiv:2609.36512 — arXiv (en), https://arxiv.org/abs/2609.36512 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2f577416098a (Metraipolis archive dc_2f577416098a, sha256 46c5bcb3e561b15997e9662adcc859c736d4bc4214ae64d55b75ee744b8c148f)

BibTeX

@misc{dc2f577416098a,
  title = {SAIVE: Selecting AI Valuable Entities — arXiv:2609.36512},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.36512},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2f577416098a; sha256 46c5bcb3e561b15997e9662adcc859c736d4bc4214ae64d55b75ee744b8c148f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.36512. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Data lakes store large amounts of telemetry, with logs from network sensors, hosts, and applications containing possibly hundreds of fields for every event. Large enterprises are then left with data lakes that cannot be analyzed efficiently with AI. Aggregate analysis looks at persistent shifts in behavior over time. Many of the fields and columns in data lakes are not useful as they do not contain information that is sufficiently diverse or concentrated to support AI analysis. SAIVE is a simple method for examining a few rows in a large table and applies a histogram of histograms filtering criterion to select the fields that for AI analysis is more likely to yield useful results. This paper provides a principled foundation for the SAIVE heuristics by assuming of a Zipf-Mandelbrot power-law distribution of the underlying data. Constraining the Zipf-Mandelbrot exponent alpha to a reasonable range provides a a practical, cheap, expert-free filter for selecting AI valuable entities in large data sets.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-30 in cs.DB).
Categorie arXiv: Databases (cs.DB).
Pagina del lavoro: https://arxiv.org/abs/2609.36512 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T18:42:16.909Z dalla pagina https://arxiv.org/catchup/cs.DB/2026-09-30?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).