ORIGINAL COMPANY-CONTEXT PRACTICE
Summarize irregular sensor readings
Original practice scenario inspired by CNOOC Limited's public business context; this is not a reported interview question or a description of its internal systems. A fictional offshore operations dataset records a measurement whenever its value changes, with periodic heartbeats. Explain why an ordinary average may mislead and how to estimate a time-weighted daily mean.
Written for independent practice using company context; this is not a verified question from a company interview.
A REASONED APPROACH
Model answer.
Event-triggered samples overrepresent periods when values change frequently. A time-weighted mean asks how long each value represented the process, subject to a defensible interpolation and staleness rule.
Work through it
- Sort and deduplicate sensor events, normalize timestamps, and define whether values remain constant until the next reading or require another interpolation method.
- Intersect each valid interval with the reporting day, multiply value by covered duration, and divide by total valid covered time. Split intervals at day boundaries.
- Publish coverage and exclude intervals beyond the allowed heartbeat gap. Test midnight crossings, duplicate timestamps, missing days, and a long stale period.
Tradeoffs to discuss
- A step-function estimate is transparent but may be wrong for rapidly varying processes. More sophisticated interpolation needs domain evidence and still cannot recover unobserved events exactly.
Common pitfalls
- A simple row average depends on sampling behavior.
- Filling an entire outage with the last value can create a precise-looking daily mean from almost no valid coverage.