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Record W2171789777 · doi:10.5120/4612-6605

Component Replacement Strategies for Information Systems Reengineering

2012· article· en· W2171789777 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Computer Applications · 2012
Typearticle
Languageen
FieldComputer Science
TopicService-Oriented Architecture and Web Services
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceBusiness process reengineeringComponent (thermodynamics)Software engineeringProcess managementManufacturing engineeringBusiness

Abstract

fetched live from OpenAlex

Recent trend in systems architecture and design is componentbased. A system is designed as a set of mutually supporting components that provide the intended services. The requirements models such as business type models and use case models are often used for deriving the targeted component-based architecture. The component interfaces are derived via sequence diagrams, collaboration diagrams and context diagrams. As the business model evolves, it becomes vital that the system also needs to match the business evolution whether it involves changing business rule set or growth in volume of business transactions. Timely reengineering of systems is profitable to any organization. The systems reengineering can be conducted in a pragmatic manner via component by component or a selected set of components; it becomes manageable and cost-effective to maintain the system and to train only a smaller sample of affected users. This paper offers a methodology for system reengineering via component replacement and model-viewcontrol framework for component refinement and evolution in order to achieve a reengineered system that reflects upon the latest requirements in business domain.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.899
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.253
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it