A Systematic Approach for Managing the Risk Related to Semantic Interoperability between Geospatial Datacubes
Bibliographic record
Abstract
Geospatial datacubes are the database backend of novel types of spatiotemporal decision-support systems employed in large organizations. These datacubes extend the datacube concept underlying the field of Business Intelligence (BI) into the realm of geospatial decision-support and geographic knowledge discovery. The interoperability between geospatial datacubes facilitates the reuse of their content. Such interoperability, however, faces risks of data misinterpretation related to the heterogeneity of geospatial datacubes. Although the interoperability of transactional databases has been the subject of several research works, no research dealing with the interoperability of geospatial datacubes exists. In this paper, the authors support the semantic interoperability between geospatial datacubes and propose a categorization of semantic heterogeneity problems that may occur in geospatial datacubes. Additionally, the authors propose an approach to deal with the related risks of data misinterpretation, which consists of evaluating the fitness-for-use of datacubes models, and a general framework that facilitates making appropriate decisions about such risks. The framework is based on a hierarchical top-down structure going from the most general level to the most detailed level, showing the usefulness of the proposed approach in environmental applications.
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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".