The irregular timestamps tripped me up more than I expected.
Start by clarifying requirements and assumptions (e.g., expected course definition, deviation threshold, real-time vs batch). Then propose a high-level architecture that ingests position records, handles irregular timestamps, computes expected paths, and detects deviations. Finally, dive into data modeling, algorithms, and scalability considerations.
Pro tip: Emphasize the importance of handling irregular time intervals by resampling or using interpolation, and discuss how to define 'expected course'—whether from historical patterns, planned routes, or predictive models. This shows you understand the data's nuances and avoid oversimplifying.
Ask about data volume, latency requirements (real-time vs batch), definition of expected course, and what constitutes a deviation. Establish assumptions to scope the problem.
Propose a pipeline to ingest streaming or batch position records, store them in a time-series database or data lake, and handle irregular timestamps via interpolation or windowing.
Define expected course using historical trajectories, route plans, or predictive models. Compute deviation metrics (e.g., distance from expected path, heading change) and set thresholds.
Choose algorithms (e.g., clustering, Kalman filters, LSTM) to predict expected positions and compare with actuals. Handle irregular intervals by resampling or using time-aware models.
Discuss scaling with distributed processing (e.g., Spark, Flink), alerting mechanisms, and monitoring. Consider false positives and feedback loops.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.