Integration and Visualization Issues in Large-Scale Location-Based Facilities Management Systems
Bibliographic record
Abstract
Large-scale Facilities Management Information Systems (FMIS) require integrating a great amount of informa-tion about each building, and the ability to easily locate these buildings and their components, especially when considering the potential of using the FMIS in a mobile Location-Based Computing (LBC) setting. The interop-erability of these systems is of paramount importance because of the need to develop and use them by a large number of groups in a distributed fashion. Available interoperability product models, such as Industrial Founda-tion Classes (IFC), have several limitations with respect to the requirements of these systems. In this paper, we describe innovative methods for integrating and visualizing information of large scale FMIS and discuss the computational issues needed for creating and deploying the 3D models used in these systems. CAD models, maps and images are integrated to create the 3D model of a facility, and then the resulting model is integrated with cost and scheduling information and used to collect inspection data using mobile computers equipped with tracking devices and wireless communications. In order to realize the proposed FMIS, several standards are in-troduced for the purpose of complementing IFC in fulfilling the additional requirements of integration, visualization, and tracking. The proposed approach is demonstrated through a case study about a FMIS for a university campus.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".