SOLAP: A NEW TYPE OF USER INTERFACE TO SUPPORT SPATIO-TEMPORAL MULTIDIMENSIONAL DATA EXPLORATION AND ANALYSIS
Bibliographic record
Abstract
It is well known that transactional and analysis systems each require a different database structure. In general, the database structure of transactional systems is optimized for consistency and efficient updates while the database structure of analysis systems is optimized for complex query performance. Non-spatial data are reorganized in data warehouses in order to support analysis and decision-making. In the same way, spatial data need to be stored in spatial data warehouses to support spatio-temporal decision-making. However, the actual client tools used to exploit the data warehouse are not well adapted to fully exploit the spatial data warehouse. New client tools are then required to take full advantage of the geometric component of the spatial data. GIS are potential candidates but despite interesting spatiotemporal analysis capabilities, it is recognized that actual GIS systems per se are not optimally designed to be used to support decision applications and that alternative solutions should be used (Bedard et al, 2001). Among them, the Spatial OLAP (SOLAP) tools offer promising possibilities. A SOLAP tool can be defined as “a visual platform built especially to support rapid and easy spatio-temporal analysis and exploration of data following a multidimensional approach comprised of aggregation levels available in cartographic displays as well as in tabular and diagram displays” (Bedard, 1997). SOLAP tools form a new family of user interfaces and are meant to be client applications sitting on top of a multi-scale spatial data warehouse. They are based on the multidimensional paradigm. This document presents the concepts of SOLAP, the characteristics of this new type of user interface, and examples related to a few of the many possible application domains. A live demonstration of a SOLAP tool will complete this document.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.018 |
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".