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Enregistrement W1524090478

Process-Oriented Systems Integration

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

Notice bibliographique

RevueProceedings 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
Langueen
DomaineBusiness, Management and Accounting
ThématiqueBusiness Process Modeling and Analysis
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésComputer scienceSystem integrationInformation integrationInformation systemBusiness processProcess (computing)Process managementEnterprise information integrationKnowledge managementWork in processEngineeringArchitectureDatabase
DOInon disponible

Résumé

récupéré en direct d'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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,639
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,011
Tête enseignante GPT0,224
Écart entre enseignants0,213 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2006
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueProceedings 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 JuneMême sujetBusiness Process Modeling and AnalysisTravaux en français237 207