Bibliographic record
Abstract
A data warehousing system is a single data repository, which integrates already existing information from different data sources belonging to an enterprise over a long time period. One of the main tasks in building a data warehouse is to ensure that data drawn from several data sources contain no structural and semantic conflicts before being loaded into the data warehouse. Representing the same real world object in numerous ways is just one form of data disparity (dirt) to be resolved in a data warehouse. Data cleaning is a complex process, which uses multidisciplinary techniques to remove all the conflicts inherent in warehouse data. This thesis proposes two data cleaning algorithms. The first algorithm, designed for initial data warehouse cleaning, uses the token keys composed from record fields for comparison of records. The second algorithm is designed to subsequently clean an existing data warehouse in a timely fashion. The algorithms achieve optimal cleaning correctness in a good time. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2002 .O33. Source: Masters Abstracts International, Volume: 41-04, page: 1116. Thesis (M.Sc.)--University of Windsor (Canada), 2002.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".