Proceedings of the ACM tenth international workshop on Data warehousing and OLAP
Bibliographic record
Abstract
It is our great pleasure to welcome you at the 10th ACM International Workshop on Data Warehousing and OLAP -- DOLAP'07. Continuing the tradition of previous successful DOLAP workshops, the DOLAP 2007 workshop provides an international forum where both researchers and practitioners in the field of Data Warehousing and OLAP can share their findings in theoretical foundations, current methodologies, and practical experiences. The mission of DOLAP is to explore novel research directions and emerging application domains in the areas of data warehousing and OLAP. Although, research in data warehousing and OLAP has produced important technologies for the design, management and use of information systems for decision support, there are still problems and research opportunities in the area. 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. Nevertheless, the high maturity of these technologies as well as new data needs or applications not only demand more capacity or storing necessities, but also new methods, models, techniques or architectures to satisfy these new needs. Some of the hot topics in data warehouse research include distributed data warehouses, web warehouses, data streams, realtime DWs, GIS/location-based services, and biomedical data. Moreover, there are other aspects developed in other software areas such as security/privacy or quality, which still remain unexplored by current design methods or technologies for data warehouses. The call for papers attracted 28 submissions from Asia, Canada, Europe, and the United States. The program committee accepted 12 papers that can be thematically grouped into data warehouse design, physical data organization, data warehouse processing, and spatio-temporal data warehouses and data mining. The papers in the area of data warehouse design presents novel methods for (semi-)automatic design of DWs and for estimating the size of materialized views. The papers on physical data organization provide new insights into MOLAP storage, probabilistic data management, and bitmap indexing. The papers on data warehouse processing address ETL workflows, real-time DW environments, and the efficient computation of materialized view subsets. Finally, the papers on spatio-temporal data warehouses and data mining expand into new territory by presenting a novel multidimensional model for dynamic spatio-temporal data, a GIS-OLAP system implementation, and a method for discovering unexpected multidimensional sequential rules. Finally, since DOLAP is the premier venue for data warehouse and OLAP research, the program includes a research challenges vision, in the form of a panel with research challenge proposals by four 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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.051 | 0.027 |
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".