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Record W1722472107

BIM-Tool: Modeling and Reasoning Support for Strategic Business Models

2013· article· en· W1722472107 on OpenAlexaff
Fabiano Dalpiaz, Daniele Barone, Jennifer Horkoff, Lei Jiang, John Mylopoulos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Process Modeling and Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceEclipseBuilding information modelingSoftware engineeringPlan (archaeology)Process managementKnowledge managementEngineeringOperations management
DOInot available

Abstract

fetched live from OpenAlex

Abstract. The BIM-Tool provides graphical modeling and analysis support for the Business Intelligence Model (BIM). BIM-Tool is a standalone application built on top of Eclipse. The tool supports two kinds of automated reasoning: (1) bottom-up “what-if ” analysis: given input labels about some elements of a BIM model (for example, success/failure for leaf goals), do these propagate to other elements in the model?; and (2) top-down “is it possible? ” analysis: is there a plan that leads to the satisfaction of goals, occurence/non-occurence of situations? BIM-Tool answers these questions through an encoding of BIM models in disjuntive datalog programs. 1 The Business Intelligence Model The Business Intelligence Model (BIM) [1,2] is a goal-oriented language for modeling organizational strategies. BIM relies on a set of modeling primitives that decision makers are familiar with, such as goal, task/process, indicator, situation, and influence relations among them. BIM is intended to support the notions from SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis by modeling the internal and externals factors (situations) that are (un)favorable for fulfilling strategic business goals. Some of the primitives of BIM (goals, tasks, refinement) are adopted from i*. Unlike i*, BIM does not support strategic dependencies, for BIM focuses on the high-level strategic goals of the organization, and does not ascribe goals to specific actors. BIM does not distinguish between hard- and soft-goals; rather, it includes measurable indicators that determine the degree of satisfaction of goals. BIM is endowed with a formal semantics [3] that enables a variety of automatic reasoning techniques [2]: (i) goal analysis (both top-down and bottom-up), (ii) probabilistic evaluation of strategies, and (iii) reasoning with composite and qualitative indicators. 2 BIM-Tool The BIM-Tool aims to provide a comprehensive graphical modeling and analysis support for BIM. BIM-Tool is a standalone Rich Client Platform application built on top of Eclipse that exploits the Eclipse Graphical Modeling Project (GMP) framework 1

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 categoriesScholarly communication
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 score0.497
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 communication0.0010.003
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 head0.045
GPT teacher head0.230
Teacher spread0.185 · 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 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
Published2013
Admission routes1
Has abstractyes

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