I got the basic logic down pretty quick: scan through, track whether you're in an alert state, open an interval when you cross the threshold and close it when you drop back.
Clarify the input format and edge cases, then propose a single-pass linear scan that tracks whether we are inside an interval. When the reading exceeds the threshold and we are not in an interval, start a new interval; when it drops to or below and we are in an interval, close it at the previous timestamp. Handle the end-of-list case by closing any open interval at the last timestamp.
Pro tip: Mention that you would confirm whether the sensor readings are sorted by timestamp and whether duplicate timestamps can occur, as these affect the algorithm and edge-case handling. Also, explicitly state that the interval ends at the last timestamp before the drop, not at the drop itself, to show attention to detail.
Ask about input format (list of (timestamp, value) pairs), whether timestamps are sorted, if duplicates are possible, and how to handle empty lists or all values above/below threshold.
Explain that you will iterate through the readings once, maintaining a state variable to indicate if you are currently inside an interval, and record start and end timestamps accordingly.
When value > threshold and not in interval, set start = current timestamp and mark in interval. When value <= threshold and in interval, set end = previous timestamp, add interval, and mark not in interval.
After the loop, if still in an interval, close it at the last timestamp of the list and add it to the result.
State that the algorithm runs in O(n) time and O(1) extra space (excluding output), and walk through a small example to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Classic interval merge, but the touching-counts-as-overlap rule is the small twist.
Clarify that the input is a collection of intervals from multiple cameras, then flatten all intervals into a single list and sort by start time. Iterate through the sorted intervals, merging any that overlap or touch (i.e., when the next start <= current end), and output the merged list.
Pro tip: Explicitly state that you treat touching intervals as overlapping by using a <= comparison, and mention that this handles edge cases like zero-length intervals and ensures correctness. Also, discuss the trade-off between sorting all intervals upfront (O(n log n)) versus using a heap for streaming data, showing awareness of scalability.
Confirm that the input is a list of intervals (each with start and end) from multiple cameras, and the output should be a sorted list of non-overlapping intervals with touching intervals merged.
Combine all intervals into a single list and sort them by start time (and end time if starts are equal). This ensures we process intervals in order.
Initialize an empty result list. For each interval in sorted order, if the result is empty or the current interval's start is greater than the last merged interval's end, append it; otherwise, merge by updating the last interval's end to the maximum of both ends.
Use a <= comparison when checking overlap (i.e., if current.start <= last.end, merge) to treat touching intervals as overlapping.
State time complexity O(n log n) due to sorting and space O(n) for the output. Discuss edge cases: empty input, single interval, all intervals overlapping, and intervals with same start times.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.