Proceedings of the Fourteenth International Database Engineering & Applications Symposium
Bibliographic record
Abstract
The International Database Engineering & Applications Symposium (IDEAS) was established in 1997. IDEAS'10 is the fourteenth annual meeting in the IDEAS series and is being held in Montreal, Quebec, Canada, during August 16-18, 2010. The conference aims to address the needs of the academic community in database and engineering, its application found in an ever increasing number of domains. It brings together academics, government and industry professionals to discuss recent progress and challenges in a broad range of areas in this domain such as data mining, databases, information retrieval, machine learning, as well as software engineering. IDEAS'10 also serves as a platform for theoreticians and practitioners to exchange their original research ideas on academic or application aspects of computing and engineering of database systems. They can present their new findings and share their experiences on integrating new technologies into products and applications. The symposium facilitates the discussion of their work as applied to real-life situations, and their development and operations of challenging database related systems, while identifying unsolved challenges. In terms of submissions, we have attracted many high-quality papers submitted by authors globally including: Australia, Austria, Brazil, Canada, China, France, Germany, India, Iran, Ireland, Italy, Japan, Jordan, Macedonia, Norway, Portugal and United States. For IDEAS'10, we continue to keep our tradition of inviting international experts in various aspects of database engineering to join our program committee. As a result, our Program Committee consists of professionals who have done an excellent job in finishing the single-blind review and on-line double-blind debate processes built into ConfSys. On average, about 97% of reviews were submitted by the due date and about 3.85 reviews were received for each paper. Thus the paper selection process was thorough and competitive. On average, each paper was refereed by at least 3.85 reviewers, and about 17% of submissions were accepted as full research papers. This year, we have an intensive program that spans over three days. In addition to full research papers we also include selected short papers and posters, which allow authors to present new applications and explore untried research directions.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.089 | 0.058 |
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".