Process-Oriented Systems Integration
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".