On-line analytical processing while immersed in a CAVE
Bibliographic record
Abstract
The paper presents a new approach for interactive visualization of data warehouses and data mining results in an immersed virtual environment. DIVE-ON is a data mining system prototype that is capable of constructing a multidimensional data model on a remote system, transporting pertinent views to a CAVE, creating an immersed virtual environment and providing an interactive data mining toolset. The main objective of this research is to examine the possibility of effective mining, visualizing and manipulating large amounts of distributed multidimensional data with little or no instructional help. To achieve this, DIVE-ON immerses the user in a virtual environment and provides a set of intuitive and effective interaction techniques within the CAVE environment. Intuitiveness was tackled by exploiting the user's considerable natural experience in interacting and navigating through a 3-dimensional world and by understanding the characteristics of a virtual environment that is well suited for the visual analysis of data. The ability to perform OLAP operations intuitively in such an environment provides the user with an effective means to conceptualize and gain an insight into large volumes of data from several distributed sources.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".