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Towards Anticipatory Databases Through Shared Data and Workload Semantics — arXiv:2609.14255

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.14255
License (as archived): CC0-1.0
Archived: 2026-10-16T00:15:31.122Z
SHA-256 of the archived text: fa0e0ab0ab324a9678966924de97b6604cd650a313b2d7a9c4e1c06e2047f8de
Stable URL of this page: https://metraipolis.com/documento/dc_636fefa67fd1

How to cite

Towards Anticipatory Databases Through Shared Data and Workload Semantics — arXiv:2609.14255 — arXiv (en), https://arxiv.org/abs/2609.14255 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_636fefa67fd1 (Metraipolis archive dc_636fefa67fd1, sha256 fa0e0ab0ab324a9678966924de97b6604cd650a313b2d7a9c4e1c06e2047f8de)

BibTeX

@misc{dc636fefa67fd1,
  title = {Towards Anticipatory Databases Through Shared Data and Workload Semantics — arXiv:2609.14255},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.14255},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_636fefa67fd1; sha256 fa0e0ab0ab324a9678966924de97b6604cd650a313b2d7a9c4e1c06e2047f8de},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.14255. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Database management systems increasingly serve dynamic and exploratory workloads, yet many of their decisions still rely on low-level signals such as recency, frequency, and address locality. These signals capture how data was accessed, but not what is being examined or how an analytical focus evolves. We argue for treating workload semantics as a first-class control signal for anticipatory decision making. Central to this view, we introduce semantic locality and semantic trajectories, which capture relationships among nearby queries and how those relationships evolve across a session. We propose a framework that represents semantic context at the data, query, and session levels, models its evolution over time, and translates it into task-specific utility estimates. We instantiate this framework in semantic prefetching and semantic cache eviction, which share a semantic layer to make two separate decisions. Prefetching uses semantic trajectories to anticipate future accesses beyond what address-based locality can capture, while eviction uses semantic relevance to inform block replacement. These systems provide initial evidence that shared semantic context can support multiple DBMS components. We further outline how this principle can extend to other decisions and data systems, and discuss key challenges in representation, cost, adaptation, and evaluation.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-15 in cs.LG).
Categorie arXiv: Databases (cs.DB) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.14255 — 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 2026-10-15T23:54:14.898Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-15?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).