A Case Study of Execution of Untrusted Business Process on Permissioned Blockchain
Bibliographic record
Abstract
Many studies have been done to improve the performance of centrally controlled business processes and enhance the integration between different parties of these collaborations. However, the most serious issues of collaborative business processes remained unsolved in these studies - lack of trust and divided data on various confidential ledgers. Blockchain technology has enormous potential to become a new substantial integration method for untrusted collaborative businesses. Using the governing consensus mechanism, blockchain eliminates the necessity of the trusted third party. It provides a distributed shared ledger which facilitates the job of the process monitoring for the parties. The smart contract, as a crucial tool, is used to define the guaranteed autonomous programs. In addition, the privacy of the data can be ensured by using a permissioned blockchain that handles the access control because, in this way, only verifiable participants can have access to the state of the business process and its related information. In this study, the applicability of execution of a real-world untrusted business process on the permissioned blockchain is investigated. Moreover, we determine the advantages of using the permissioned access-controller blockchain as the infrastructure for the collaborative business processes, through implementing the process of Order Processing on the Hyperledger Fabric blockchain platform.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".