BIM-GIS ORIENTED INTELLIGENT KNOWLEDGE DISCOVERY
Bibliographic record
Abstract
Abstract. Urban and population growth results in increasing pressure on the public utilities like transport, energy, healthcare services, crime management and emergency services in the realm of smart city management. Smart management of these services increases the necessity of dealing with big data which is come from different sources with various types and formats like 3D city information, GPS, traffic, mobile, Building Information Model (BIM), environmental, social activities and IoT stream data. Therefore, an approach to mine/analysis/interpret these data and extract useful knowledge from this diverse big data sources emerges in order to extract the hidden pattern of data using computational algorithms from statistics, machine learning and information theory. However, inconsistency, duplication and repetition and misconducting with the different type of discrete and continuous data can cause erroneous decision-making. This paper focuses on providing a rules extraction and supervised-decision making methods for facilitating the fusion of BIM and 2D and 3D GIS-based information coupling with IoT stream data residing in a spatial database and 3D BIM data. The proposed methods can be used in those applications like Emergency Response, Evacuation Planning, Occupancy Mapping, and Urban Monitoring to Smart Multi-Buildings so that their input data mostly come from 2D and 3D GIS, BIM and IoT stream. This research focus on proposing the unified rules extraction and decision engine to help smart citizens and managers using BIM and GIS data to make smart decision rather than focus on applications in certain field of BIM and GIS.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".