Spatial OLAP modeling: an overview base on spatial objects changing over time
Bibliographic record
Abstract
Enabling the decision making process to support spatial queries is not a trivial task. This task becomes even harder when using geographic information systems (GIS) with data warehouse (DW) because these two technologies are in general used separately. In general, a GIS solution handles spatial data without considering time constrains or without requiring the analyses of geometric shapes evolving over time. Temporal maps further raise difficulties on indexing issues and on the associated query mechanisms. On the other hand, a typical DW operates with non-spatial data for different time periods. It also does not support spatial data types, such as point, lines, and polygons. In this paper, we propose a multidimensional spatiotemporal data model to enable spatial analysis, in a context of evolving specifications. The proposed data model addresses the problem of spatial and temporal data integration by providing information to facilitate semantic interoperability and data analysis in a spatial DW that uniformly handles all types of data. Using a practical example in the field of land parcels, we evaluate the implementation of the model.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".