K-CEP: a knowledge-based complex event processing framework to manage qualitative spatiotemporal patterns
Bibliographic record
Abstract
In this paper, we present a framework for managing qualitative spatiotemporal patterns. Our framework is designed for large scale monitoring systems. Such systems generate a huge amount of real-time data in various formats. End-users are interested in finding significant data configurations based on their expertise and attempt to leverage the large amounts of data generated by acquisition systems. Several software tools have been proposed to help users achieve such goals. However, available solutions are mostly based on relational databases and use SQL queries to support such functionalities. These systems do not allow for real-time detection of situations of interest (also called 'patterns' in this domain) due to the weak expressiveness of SQL queries. We present a novel approach based on complex event processing for the real-time detection of situations of interest based on events, states and spatial objects. We leverage the rich semantics of a qualitative pattern representation model to present a complete solution to qualitatively represent patterns and to detect their instances from the event cloud. Thanks to our approach, a user will be able to react to detected patterns instead of trying to identify ('to mine') patterns in databases as it is proposed in current approaches.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| 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".