Business Process Preservation: How to capture, document & evaluate?: Poster - iPRES 2012 - Digital Curation Institute, iSchool, Toronto
Bibliographic record
Abstract
Preserved digital data is often of limited use and value, due to the unavailability of the environment where the data has been generated, executed, analysed, and presented.The preservation of complete processes, including the supporting infrastructure, raises a number of new challenges.In highly computerized firms, processes are often based on service oriented architectures with services provided by third parties.Digital preservation of business processes requires keeping the data, software services and infrastructure available for long time spans.The strong intra-and interdependencies of components make modification and adoption for preservation highly complex.Besides the technical challenges, the organisation and legal aspects of a process need to be considered as well.Contracts for external services, licences for software, access rights to business data and national data regulations for sensible data have to be taken into account when preserving complex information systems.The TIMBUS project targets the research and development of methods and tools for preserving business processes over time.This poster presents a phased phases approach and the processes to capture and identify the relevant context, plan preservation actions and execute and store business process for the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.013 |
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".