Building data warehouses with incremental maintenance for decision support
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data warehousing is an emerging technology that facilitates gathering and integrating heterogeneous data from distributed sources and extracting information that can be utilized as a knowledge base for decision support. Once a data warehouse is built, we need to maintain it consistent with the underlying data sources, which always subject to dynamic updates. Much work has been done on manipulating and mining data warehouses. However, most of the published works pay no attention to the issue of building a complete data warehouse from scratch, and employing it as a crucial technique to support the decision making process. In this paper, we exhibit a comprehensive case study, based on utilizing a ready-made commercial database for designing and implementing a data warehouse (DW) with incremental maintenance capabilities. Furthermore, we demonstrate the process of employing the constructed data warehouse as a decision support tool to provide the management with accurate, precise, and quick information, upon which decisions can be made
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.
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 | 0.000 | 0.001 |
| 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 it