Proceedings of the 5th ACM international workshop on Data Warehousing and OLAP
Bibliographic record
Abstract
Research in data warehousing and OLAP has produced important technologies for the design, management and use of information systems for decision support. Much of the interest and success in this area can be attributed to the need for software and tools to improve data management and analysis given the large amounts of information that are being accumulated in corporate as well as scientific databases.However, even though the high maturity of these technologies, new data needs or applications currently run at companies not only demand more capacity, but also new methods, models, techniques or architectures to satisfy these new needs. Some of the hot topics in data warehouses (DWs) include distributed DWs, advanced OLAP for business intelligence, web warehouses, DWs for new applications such as XML documents, stream data, spatial or GIS data or biomedical data. Moreover, there are other aspects very developed in other software areas such as security or quality, which still remain uncovered by current design methods or technologies for DWs.Like the previous successful DOLAP workshops held in conjunction with CIKMs, the eighth edition of the Workshop on Data Warehousing and OLAP (DOLAP'05) aims to synergistically connect the research community and industry practitioners. It provides an international forum where both researchers and practitioners can share their findings in theoretical foundations, current methodologies, and practical experiences. This year, DOLAP'05 will be specially focused on new research directions and emerging application domains in the areas of data warehousing and OLAP.This year, we received papers from 18 different countries distributed over all continents such as The Netherlands, France, Spain, Israel, Korea, USA, Canada and Argentine. We received 31 submissions and, after a careful review, only 12 papers were selected by the Program Committee, making an acceptance rate of 38.7%.These proceedings contain the papers selected for presentation at the workshop. The accepted papers were presented in 5 sessions: (i) querying OLAP databases, (ii) data warehouse models, (iii, iv) data warehouse design, and (v) query processing and view maintenance. A keynote address was given by Jens Lechtenburger on Schema transformations. We hope that these proceedings will serve as a valuable reference for data warehousing and OLAP researchers and practitioners.
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 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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.045 | 0.023 |
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".