A Big Data Analytics Solution to Mine Frequent Patterns and Detect Anomalies from 311 City Service Requests
Bibliographic record
Abstract
This work investigates the use of 311 city service request data as a valuable resource for identifying urban challenges in a city, with the aim of developing a transferable model for other municipalities. The 311 system, which enables residents to report non-emergency concerns, functions as a critical mechanism for improving service delivery and optimizing resource allocation. By applying frequent pattern mining, seasonal decomposition, and the median absolute deviation (MAD) method, we present in this paper a big data analytics solution to mine frequent patterns and detect anomalies, which signal potential service disruptions or inefficiencies. Such a solution is expected to enhance municipal decision-making by enabling cities to allocate resources more strategically and respond more effectively to residents' needs. Evaluation on real-world data from a mid-sized Canadian city of Winnipeg demonstrates the usefulness of our big data analytics solution in frequent pattern mining and anomaly detection from city service requests.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".