Wastewater wants early detection of pump anomalies from sensor streams plus work-order history. Which application class fits?
Select an answer to reveal the explanation.
Short Explanation
Pumps whisper through sensors; work orders remember past failures. Listen to both and you catch trouble early. That is operations monitoring with a predictive-maintenance flavor.
Full Explanation
Operations monitoring and predictive-maintenance-style applications combine live sensor streams with historical maintenance records. Wastewater pump anomaly detection from sensor streams plus work-order history is a standard civic instance of that class. The pattern depends on M2M Velocity plus curated operational history, not on vanity publishing alone.
Annual budget storytelling with no operational telemetry fails by concept: the pattern needs live and historical operational data. One-time open-data CSV publishing without monitoring fails by concept: publishing is not continuous anomaly detection. Person-to-person only sentiment dashboards for park redesigns fails by concept: that is a different application class.
Exam caveat: Ops monitoring / predictive-maintenance detection fuses sensor streams with work history—distinct from budget storytelling, one-shot open data, or parks sentiment.
Operational check: Wire pump telemetry to anomaly jobs, join to work-order history, set alert thresholds with maintenance owners, and track false-positive rates before expanding citywide. Document residual risk for civic operators and revisit after the next production change window. Confirm owners, SLAs, and rollback criteria in the runbook before go-live.