You are given a list of user activity logs. Each log record has the form `[user_id, timestamp, action]`. Example input: ```text logs = [ [100, 1, "A"], [101, 2, "A"], [100, 3, "B"], [102, 4, "B"], [101, 5, "C"], [100, 6, "C"], [103, 7, "A"], [103, 8, "B"] ] ``` For each user, reconstruct that user's journey by ordering the actions by `timestamp`. Then aggregate all users' journeys and compute the count of every path prefix. Using the example above, the reconstructed journeys are: - `100 -> A, B, C` - `101 -> A, C` - `102 -> B` - `103 -> A, B` The aggregated prefix counts are: ```text A (3) B (2) C (1) C (1) B (1) ``` Explanation: - `A` appears as the first action for 3 users. - `A -> B` appears for 2 users. - `A -> B -> C` appears for 1 user. - `A -> C` appears for 1 user. - `B` appears as the first action for 1 user. Return the aggregated prefix counts in any reasonable structured format, such as a trie, nested map, or a list of prefixes with counts. Discuss the time and space complexity of your approach, and whether the trie can be updated while scanning the logs instead of first building full per-user action lists.