Flow-based Adaptive Information Integration
Bibliographic record
Abstract
Assembling a coherent view of distributed heterogeneous information and their processing is challenging but important for inter-organizational business collaboration and service provision. However, traditional integration approaches do not consider dynamic and adaptive issues such as human intervention and exception handling. Therefore, we propose a Workflow-based Information Integration (WII) approach, which is particularly suitable in a loosely coupled Web services environment. Our implementation framework comprises five layers: semantic, application, workflow, service, and message. We focus on the workflow layer for providing adaptiveness from the aspects of various types of flows such as controlflows, data-flows, security-flows, exception-flows and semantic-flows by using the Business Process Execution Language for Web Services (BPEL). We further extend this with our proposed data-integration, semantic-referencing, and exception-handling assertions in order to achieve dynamic and adaptive workflow-based information integration plans. We map information into SOAP messages and link the proposed exception-handling assertions in BPEL to SOAP-fault implementations. We also define semantic referencing in BPEL by using OWL Web Ontology Language. Lastly, we demonstrate the feasibility of our adaptive approach with an intelligence information integration case study at the application layer and examine some typical use cases of exception-handling with semantic support.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".