Standard data models for interoperability of municipal infrastructure asset management systems
Bibliographic record
Abstract
Efficient management of infrastructure assets depends largely on the ability to efficiently share, exchange, and manage asset life-cycle information. Although software tools are used to support almost every asset management process in municipalities, data exchange is mainly performed using paper-based or neutral file formats based on ad hoc proprietary data models. Interoperability of various asset management systems is crucial to support better management of infrastructure data and to improve the information flow between various work processes. Standard data models can be used to significantly improve the availability and consistency of asset data across different software systems, to integrate data across various disciplines, and to exchange information between various stakeholders. This paper surveys a number of data standards that might be used in implementing interoperable and integrated infrastructure asset management systems. The main requirements for standard data models are outlined, and the importance of interoperability from an asset management perspective is highlighted. The role that spatial data and geographic information systems (GIS) can play in enhancing the efficiency of managing asset life-cycle data is also discussed. An ongoing effort to develop a standard data model for sewer systems is presented, and an example implementation of interoperable GIS and hydraulic modeling software is discussed.Key words: data standards, municipal infrastructure, asset management, data models, interoperability.
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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 | 0.023 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".