Bibliographic record
Abstract
A successful information system is the one that meets its design goals. Expressing these goals and subsequently translating them into a working solution is a major challenge for information systems engineering. This thesis adopts the concepts and techniques from goal-oriented (software) requirements engineering research for conceptual database design, with a focus on data quality issues. Based on a real-world case study, a goal-oriented process is proposed for database requirements analysis and modeling. It spans from analysis of high-level stakeholder goals to detailed design of a conceptual databases schema. This process is then extended specifically for dealing with data quality issues: data of low quality may be detected and corrected by performing various quality assurance activities; to support these activities, the schema needs to be revised by accommodating additional data requirements. The extended process therefore focuses on analyzing and modeling quality assurance data requirements. A quality assurance activity supported by a revised schema may involve manual work, and/or rely on some automatic techniques, which often depend on the specification and enforcement of data quality rules. To address the constraint aspect in conceptual database design, data quality rules are classified according to a number of domain and application independent properties. This classification can be used to guide rule designers and to facilitate building of a rule repository. A quantitative framework is then proposed for measuring and comparing DQ rules according to one of these properties: effectiveness; this framework relies on derivation of formulas that represent the effectiveness of DQ rules under different probabilistic assumptions. A semi-automatic approach is also presented to derive these effectiveness formulas.
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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 | 0.032 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".