Modelling and Design of Generic Semantic Trajectory Data Warehouse
Bibliographic record
Abstract
The trajectory patterns of a moving object in a spatio-temporal domain offers varied information in terms of the management of the data generated from the movement. A trajectory data warehouse is a data repository for the data and information of trajectory objects and their associated spatial objects for defined temporal periods. The query results of trajectory objects from the data warehouse are usually not enough to answer certain trend behaviours and meaningful inferences without the associated semantic information of the trajectory object or the geospatial environment within a specified purpose or context. This paper formulates and designs a generic ontology modelling framework that serves as the background model platform for the design of a semantic data warehouse for trajectories. This semantic trajectory data warehouse can be adaptable for trajectory data processing and analytics on any chosen spatio-temporal application domain. The methodology underpins on higher granularity of data as a result of pre-processed and transformed ETL data so as to offer efficient semantic inference to the underlying trajectory data. Moreover, the approach outlines the thematic dimensions that serve as necessary entities for extracting semantic information. Additionally, the modelling approach offers a design platform for effective predictive trend analysis and knowledge discovery in the trajectory dynamics and data processing for moving objects.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".