Index and Materialized View Selection in Data Warehouses
Bibliographic record
Abstract
Database management systems (DBMSs) require an administrator whose principal tasks are data management, both at the logical and physical levels, as well as performance optimization. With the wide development of databases and data warehouses, minimizing the administration function is crucial. This function includes the selection of suitable physical structures to improve system performance. View materialization and indexing are presumably some of the most effective optimization techniques adopted in relational implementations of data warehouses. Materialized views are physical structures that improve data access time by precomputing intermediary results. Therefore, end-user queries can be efficiently processed through data stored in views and do not need to access the original data. Indexes are also physical structures that allow direct data access. They avoid sequential scans and thereby reduce query response time. Nevertheless, these solutions require additional storage space and entail maintenance overhead. The issue is then to select an appropriate configuration of materialized views and indexes that minimizes both query response time and maintenance cost given a limited storage space. This problem is NP hard (Gupta & Mumick, 2005).
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".