Calculating data warehouse aggregates using range-encoded bitmap index.
Bibliographic record
Abstract
A data warehouse is a database consisting of huge amounts of data collected from different source databases of an organization over a long period of time. Warehouse data are used for analytical purposes to make accurate and timely decisions based on previously integrated facts. Data warehouse is accessed using different kinds of analytical queries. One of the most critical issues is that those queries be responded to quickly and accurately. The size and logical schema of data warehouse systems make it difficult to apply existing query optimizing techniques originally developed for traditional database systems. Indexes are data structures, which help to locate the specific records in the database with minimum number of disk accesses. Bitmap indexing is a promising technique for data warehousing systems, but space for bitmap indexes is a major problem. This thesis proposes the use of range-encoded bitmap index to calculate aggregates. By using space optimal range-encoded bitmap index for range predicates and aggregates, the need of separate indexes for these operations can be eliminated. The range-encoded index is efficiently used for evaluating range predicates. We are proposing algorithm to evaluate aggregates with the same index that gives equal performance, which was previously achieved by storing a separate index for these operations. This will reduce the space requirements and maintenance overheads considerably without losing performance for aggregates. The proposed indexing scheme is easy to maintain and use the population ratio of 1's in a bitmap to decide if the bitmap has to be scanned from the disk. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2002 .B58. Source: Masters Abstracts International, Volume: 41-04, page: 1100. Adviser: Christie Ezeife. Thesis (M.Sc.)--University of Windsor (Canada), 2002.
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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 | 0.001 | 0.000 |
| 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 | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".