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Record W2028506533 · doi:10.1145/1923947.1923993

Achieving business agility with WebSphere ILOG JRules and WebSphere BPM

2010· article· en· W2028506533 on OpenAlexaff
Maria Koshkina, Kien Huynh, Ying Zhao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Process Modeling and Analysis
Canadian institutionsIBM (Canada)
Fundersnot available
KeywordsBusiness process managementComputer scienceFlexibility (engineering)Business processProcess managementBusiness ruleBusiness process modelingProcess (computing)Business Process Model and NotationBusiness process discoveryArtifact-centric business process modelBusiness logicSoftware engineeringKnowledge managementOperations managementDatabaseEngineeringOperating systemWork in process

Abstract

fetched live from OpenAlex

Business Process Management (BPM) and Business Rule Management (BRM) are two technologies that are used to improve the agility, flexibility and efficiency of operational processes. Many people have questioned the differences between the two, or use the terms interchangeably---there are, however, clear differences in terms of the functionality and the value of each. BPM is focused on defining, orchestrating and monitoring long running processes that are comprised of both people- and system-based activities. BRM on the other hand, is focused on defining, maintaining and executing decision logic that is used at specific points within a process or as part of automated decisions within business systems. Overall, the complement, or synergy, between BPM and BRM is about reaching further---expanding the breadth of problems that can be solved with a single solution. Both technologies share goals such as improving the efficiency and visibility of business processes, but they do so at different levels in a solution.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.176
Teacher spread0.170 · 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.

Study designObservational
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
Published2010
Admission routes1
Has abstractyes

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