Start by clarifying the problem: intervals from different sources are non-overlapping within each source, but may overlap across sources. Then propose an efficient algorithm that merges all intervals and applies highlights in a single pass, discussing time/space complexity and edge cases.
Pro tip: Mention that you would first sort intervals by start index and merge overlapping ones to avoid redundant work, and that you'd handle edge cases like empty text, empty intervals, and intervals at boundaries. This shows attention to detail and performance.
Ask about input sizes, whether intervals are inclusive/exclusive, and if highlights should be merged or kept separate. Confirm that intervals from different sources may overlap.
Propose collecting all intervals, sorting by start index, and merging overlapping intervals. Then apply highlights by iterating through the text and inserting highlight markers at merged interval boundaries.
Discuss time complexity (O(n log n) due to sorting, where n is total intervals) and space complexity (O(n) for merged intervals). Compare with alternative approaches like using a boolean array for small texts.
Cover cases like empty text, no intervals, intervals covering the entire text, and intervals that touch but don't overlap. Explain how the algorithm handles them.
Write clean code with clear variable names, and walk through a small example to verify correctness. Mention potential optimizations if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the problem: we need to sort highlight segments by source (based on segment count) and then apply them in that order, skipping overlaps with already-highlighted text. Then, outline an algorithm that groups segments by source, sorts sources by segment count descending, and for each segment in order, computes the non-overlapping portions to apply. Finally, discuss trade-offs such as time complexity, data structures for efficient overlap detection, and potential edge cases.
Pro tip: Mention that you would use an interval tree or a sorted list of intervals to efficiently track highlighted regions and detect overlaps, which shows you think about scalability and performance beyond the naive approach.
Confirm the definition of a 'source' and 'highlight segment', and whether segments within a source are already sorted or need sorting. Ask about expected input size to guide algorithm choice.
Create a mapping from each source to its list of segments. Sort the sources by the number of segments in descending order.
Maintain a data structure of already-highlighted intervals. For each segment, compute the sub-intervals that do not overlap with existing intervals, and add those to the highlighted set.
Discuss time and space complexity. Compare naive O(n^2) overlap checking with more efficient interval trees or sweep-line algorithms, and justify your choice based on constraints.
Consider cases like empty segments, identical segments, segments that fully overlap, and sources with equal segment counts. 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.