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From Research Questions to Columns: Operationalization-Aware Data Discovery — arXiv:2608.04536

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.04536
License (as archived): CC0-1.0
Archived: 2026-10-31T11:58:00.420Z
SHA-256 of the archived text: cde3f59deec5fd3cba0c0ef394e883d8e3243184e6cdee9f1b2048905ef6c026
Stable URL of this page: https://metraipolis.com/documento/dc_fa115162cdee

How to cite

From Research Questions to Columns: Operationalization-Aware Data Discovery — arXiv:2608.04536 — arXiv (en), https://arxiv.org/abs/2608.04536 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fa115162cdee (Metraipolis archive dc_fa115162cdee, sha256 cde3f59deec5fd3cba0c0ef394e883d8e3243184e6cdee9f1b2048905ef6c026)

BibTeX

@misc{dcfa115162cdee,
  title = {From Research Questions to Columns: Operationalization-Aware Data Discovery — arXiv:2608.04536},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.04536},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fa115162cdee; sha256 cde3f59deec5fd3cba0c0ef394e883d8e3243184e6cdee9f1b2048905ef6c026},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.04536. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Researchers often approach a data repository with an abstract concept and ask which columns can measure it. Useful columns may not resemble the query; they may matter only as complementary indicators in a defensible measure. This need differs from schema linking and column retrieval, which begin from more explicit needs and reward direct relevance. We define operationalization-aware data discovery (OADD): given a broad question and a database, optionally under a scope constraint, OADD jointly determines how focal concepts can be measured with available data and identifies supporting columns. Developing OADD methods requires examples for design and evaluation, but asking researchers to supply conceptual questions and their columns is impractical. We construct OADD-Bench by treating empirical papers as records of schema in use. A question miner extracts and reframes a paper-supported question; a paper-conditioned column miner reconstructs its measurements and grounds them to database identifiers. We admit only mappings supported by the publication and database documentation. OADD-Bench contains 160 questions from 111 papers and 4,682 question-column labels. Each target records a measurement used in published research; the paper supplies the precedent, while the miners extract and ground it. We evaluate lexical and neural retrieval, adapted schema-linking systems, and large language model (LLM) OADD agents. Each method receives only a question, permitted years, and dataset metadata; source papers are used only to construct and document benchmark labels. At the largest output limit, direct retrieval reaches at most 0.185 recall. The strongest schema-linking adaptation reaches 0.401 but remains optimized for a different objective; an OADD-directed agent performs best at 0.465. Even this agent covers less than half the target columns, showing that OADD remains an open problem.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-06 in cs.DB).
Categorie arXiv: Databases (cs.DB).
Pagina del lavoro: https://arxiv.org/abs/2608.04536 — 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 2026-10-31T11:46:06.592Z dalla pagina https://arxiv.org/catchup/cs.DB/2026-08-06?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).