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Record W1524090478

Process-Oriented Systems Integration

2006· article· en· W1524090478 on OpenAlexaboutno aff
Yimin Zhu, R. Altis Raja, Raja R. A. Issa, Iván Mutis

Bibliographic record

VenueProceedings of the Joint CIB W78, W102, ICCCBE, ICCC, and DMUCE International Conference on Computing and Decision Making in Civil and Building Engineering, Montreal, Canada, 14-16 June · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Process Modeling and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceSystem integrationInformation integrationInformation systemBusiness processProcess (computing)Process managementEnterprise information integrationKnowledge managementWork in processEngineeringArchitectureDatabase
DOInot available

Abstract

fetched live from OpenAlex

Existing mainstream integration solutions, which are based on sharing common semantic models, are mostly static. The semantic models are designed based on a priori knowledge to the heterogeneity of collaborating systems. However, the problems that those integration solutions intend to resolve are not static, which requires that an integration solution being adaptive to constantly changing information processes. The adaptiveness of collaborating systems also puts significant constraints on the integration strategy because it is the integration strategy that coordinates and controls the information flow among collaborating systems and the accessibility on which the shared information is based. First, the changes of the composition of supporting information systems throughout the lifecycle of a project such as a construction project will cause the technical infrastructure that supports computer-mediated communication and collaboration to change, which further requires the integration strategy to be able to adjust to different integration situations dynamically. The integration strategy needs to be able to deal with the newly added tools as well as to maintain access to the data in the old format. Secondly, formal business processes such as change of order processes and procurement processes often have significant variations from case to case. Let alone to say many informal business or information processes that team with a typical construction project. The variations also have great impact on the integration solutions because for different cases the information needs are different. Therefore, the changes in technical infrastructure as well as business processes require that the information system of a project be adaptive. The writing of this paper is motivated by recent studies in computer-mediated information processes and integration strategies. Recently research in areas such as process mining and dynamic enactment of workflow processes makes computer-mediated information processes more adaptive to human-oriented business processes. Meanwhile, the hybrid strategy for systems integration in AEC has been introduced and discussed. The hybrid strategy relies on a community-specific representation that serves as a de facto standard for collaborating systems. The community-specific representation is an ontological representation that addresses the access to local data definitions, semantic mappings between local definitions, and other related issues. To support integration solutions that address the dynamics of a project and evolve as project environment changes, this paper explores theories and methods to establish a link 1 Professor, Dept. of Construction Management, Florida International University, Miami, FL 33174, U.S.A, Phone (305) 348-3517, zhuy@fiu.edu 2 Rinker Professor, Rinker School of Building Construction, University of Florida, Gainesville, FL, 32611; Phone (352) 273 1152; raymond-issa@ufl.edu 3 Ph.D. student, Rinker School of Building Construction, University of Florida, Gainesville, FL 32611; Phone (352) 273 1178; imutis@ufl.edu June 14-16, 2006 Montreal, Canada Joint International Conference on Computing and Decision Making in Civil and Building Engineering

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Joint CIB W78, W102, ICCCBE, ICCC, and DMUCE International Conference on Computing and Decision Making in Civil and Building Engineering, Montreal, Canada, 14-16 JuneSame topicBusiness Process Modeling and AnalysisFrench-language works237,207