On querying spreadsheets
Bibliographic record
Abstract
Considers the problem of querying the data in applications such as spreadsheets and word processors. This problem has several motivations from the perspective of data integration, interoperability and OLAP. We provide an architecture for realizing interoperability among such diverse applications and address the challenges that arise specifically in the context of querying data stored in spreadsheet applications. A fundamental challenge is the lack of a well-defined schema. We propose a framework in which the user can specify the layout of data in a spreadsheet, based on his perception of the important concepts underlying that data. Layout specifications can be viewed as the "physical schema" of a spreadsheet. We motivate the concept of an abstract database machine (ADM) that uses the layout specifications to provide a relational view of the data in spreadsheet applications and, similar to a DBMS, supports efficient querying of the spreadsheet data. We develop a methodology for building ADMs for spreadsheets and describe our implementation of an ADM for Microsoft Excel applications, based on the above methodology. Our implementation platform is IBM PCs running Windows NT, Microsoft Office and OLE 2.0. We demonstrate the generality and practicality of our approach by developing a formal characterization of the class of spreadsheets that can be handled in our framework. Our results show that the approach is capable of handling a broad class of naturally occurring spreadsheet applications. This work is part of an office tool integration project.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.036 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".