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Record W2128420290 · doi:10.1109/icsm.2005.100

Using self-reconfigurable workplaces to automate the maintenance of evolving business applications

2005· article· en· W2128420290 on OpenAlexaff
Qi Zhang, Ying Zou

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Process Modeling and Analysis
Canadian institutionsQueen's University
Fundersnot available
KeywordsControl reconfigurationComputer scienceArtifact-centric business process modelBusiness processProcess managementBusiness domainBusiness process modelingBusiness ruleBusiness environmentWork (physics)Business analysisKnowledge managementBusiness transformationBusiness requirementsBusiness modelBusinessEngineeringWork in processMarketingEmbedded system

Abstract

fetched live from OpenAlex

In this ever changing business environment, business processes are constantly being customized to reflect the up-to-date organizational structure and business objectives. Technology updates and innovation also affect the way business is carried out. A workplace application provides an interactive electronic working environment that integrates software applications to assist users in performing their daily work more efficiently. Managing and maintaining workplace applications within an organization is a challenging job, since it often involves labor intensive manual reconfiguration to adapt the workplace to the changes in business processes. In this paper, we propose a dynamic reconfigurable workplace framework that supports the changing nature of the business domain. This framework updates the workplace at run time, minimizes the interruption to users' work, and simplifies the evolution of a business application. The effectiveness of the framework is studied by examining changes to several business processes and the ability of the framework to update the corresponding workplaces.

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 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.748
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.019
GPT teacher head0.244
Teacher spread0.224 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2005
Admission routes1
Has abstractyes

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