Start by clarifying requirements: case sensitivity, overlapping matches, and whether the text is static. Then propose a class that preprocesses the text (e.g., builds a suffix array or trie) to enable efficient queries, and discuss trade-offs between preprocessing time and query time.
Pro tip: Mention that for a static text, you can preprocess to answer queries in O(m + k) time using a suffix automaton or suffix array with binary search, which is optimal. Also, clarify if the keyword can be empty or if matches should be case-insensitive.
Ask about case sensitivity, overlapping matches, expected query frequency, and text size to determine the best approach.
Decide between simple string search (O(n*m) per query) or preprocessing (e.g., suffix array, suffix automaton, trie) for faster queries.
Define constructor that takes text and builds the chosen data structure, and a query method that returns a list of starting indices.
For preprocessing approach, use the data structure to find all occurrences efficiently; for simple approach, use a string search algorithm like KMP.
Discuss time and space complexity of construction and query, and justify the choice based on expected usage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the current query method's behavior and constraints, then propose extending it using an edit-distance-aware search such as Levenshtein automaton or dynamic programming with early termination. Discuss trade-offs between preprocessing, time/space complexity, and practical implementation for the given scale.
Pro tip: Mention that you would first check if the existing query method can be augmented with a bounded edit distance filter, and consider using a trie or BK-tree if multiple queries are expected, showing awareness of real-world performance beyond the naive approach.
Ask about the expected input size, query frequency, allowed edits (only one), and whether the text is static or dynamic. This determines the appropriate algorithm and data structure.
Select a method like dynamic programming (Levenshtein distance with early exit), Levenshtein automaton, or trie-based search that efficiently finds matches within edit distance 1.
Modify the query method to iterate over candidate positions or use an index, applying the edit distance check and collecting positions where distance ≤ 1.
Compare time and space complexity of the chosen approach versus alternatives, and discuss when preprocessing (e.g., building an index) is beneficial.
Consider empty strings, keyword longer than text, multiple matches, and overlapping edits. Write unit tests to validate correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.