What is the time complexity of Alert Using Same Key-Card Three or More Times in a One Hour Period?

The optimal solution has a time complexity of O(nlogn).

What data structures are used to solve Alert Using Same Key-Card Three or More Times in a One Hour Period?

This problem uses Array, Hash Table, String, Sorting. Understanding these concepts is essential for solving this Medium problem efficiently.

What are the key insights for solving Alert Using Same Key-Card Three or More Times in a One Hour Period?

Insight 1: Convert time strings to a numerical representation (minutes) for easier comparison. Insight 2: Group keycard usage times by employee name using a hash table.

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Alert Using Same Key-Card Three or More Times in a One Hour Period - LeetCode 1604 Solution

Q: Which companies ask LeetCode 1604. Alert Using Same Key-Card Three or More Times in a One Hour Period in interviews?

This problem is frequently asked at Karat, Wayfair.

Alert Using Same Key-Card Three or More Times in a One Hour Period - Complete Solution Guide

Alert Using Same Key-Card Three or More Times in a One Hour Period is LeetCode problem 1604, a Medium level challenge. This complete guide provides step-by-step explanations, multiple solution approaches, and optimized code in python3, java, cpp, c.

Problem Statement

LeetCode company workers use key-cards to unlock office doors. Each time a worker uses their key-card, the security system saves the worker's name and the time when it was used. The system emits an alert if any worker uses the key-card three or more times in a one-hour period. You are given a list of strings keyName and keyTime where [keyName[i], keyTime[i]] corresponds to a person's name and the time when their key-card was used in a single day . Access times are given in the 24-hour time forma

Detailed Explanation

The problem asks us to identify employees who have used their keycards three or more times within any one-hour period on a given day. We are provided with two lists: `keyName` containing employee names, and `keyTime` containing the corresponding keycard usage times in "HH:MM" format. The goal is to return a sorted list of unique employee names who triggered an alert due to frequent keycard use.

Solution Approach

The solution involves first converting the 'HH:MM' time strings into integer representation in minutes from the start of the day. A hash map (dictionary) is then used to group access times for each employee. These times are sorted in ascending order for each employee. We then iterate through each employee's sorted access times, checking if there are at least three access times, and if there exist three accesses within a one-hour (60 minute) window. If this condition is met, the employee's name is added to a list of alerted users. Finally, the list of alerted users is sorted alphabetically and returned.

Step-by-Step Algorithm

Step 1: Create a hash map (dictionary) to store each employee's name as the key and a list of their access times (converted to minutes) as the value.
Step 2: Iterate through the `keyName` and `keyTime` lists. For each entry, convert the time string to minutes and append it to the corresponding employee's list in the hash map.
Step 3: Iterate through the hash map. For each employee:
Step 4: Sort the list of access times (in minutes) in ascending order.
Step 5: If the number of access times is less than 3, skip to the next employee.
Step 6: Iterate through the sorted access times, checking if there is a window of three accesses with a difference of 60 minutes or less. Specifically, check if `times[i + 2] - times[i] <= 60` for any valid index `i`.
Step 7: If such a window is found, add the employee's name to the `alerted_users` list if it's not already present, and break the inner loop.
Step 8: Sort the `alerted_users` list alphabetically.
Step 9: Return the `alerted_users` list.

Key Insights

Insight 1: Convert time strings to a numerical representation (minutes) for easier comparison.
Insight 2: Group keycard usage times by employee name using a hash table.
Insight 3: Sort the usage times for each employee to efficiently check for three accesses within an hour.
Insight 4: Handle duplicates by adding alerted employee names only once to the result list.

Complexity Analysis

Time Complexity: O(nlogn)

Space Complexity: O(n)

Topics

This problem involves: Array, Hash Table, String, Sorting.

Companies

Asked at: Karat, Wayfair.

Alert Using Same Key-Card Three or More Times in a One Hour Period | LeetCode 1604

Medium

Array Hash Table String Sorting

Time: O(nlogn)Space: O(n)

Solution Code

Available in:

import java.util.*;

class Solution {
    public List<String> alertNames(String[] keyName, String[] keyTime) {
        Map<String, List<Integer>> userTimes = new HashMap<>();
        for (int i = 0; i < keyName.length; i++) {
            String name = keyName[i];
            String timeStr = keyTime[i];
            String[] parts = timeStr.split(":");
            int hours = Integer.parseInt(parts[0]);
            int minutes = Integer.parseInt(parts[1]);
            int timeInMinutes = hours * 60 + minutes;
            if (!userTimes.containsKey(name)) {
                userTimes.put(name, new ArrayList<>());
            }
            userTimes.get(name).add(timeInMinutes);
        }

        List<String> alertedUsers = new ArrayList<>();
        for (Map.Entry<String, List<Integer>> entry : userTimes.entrySet()) {
            String name = entry.getKey();
            List<Integer> times = entry.getValue();
            Collections.sort(times);
            if (times.size() >= 3) {
                for (int i = 0; i < times.size() - 2; i++) {
                    if (times.get(i + 2) - times.get(i) <= 60) {
                        alertedUsers.add(name);
                        break;
                    }
                }
            }
        }

        Collections.sort(alertedUsers);
        return alertedUsers;
    }
}

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Problem Info

Description

Problem 1604: Alert Using Same Key-Card Three or More Times in a One Hour Period - Detailed explanation and solution approaches.

Topics

Array Hash Table String Sorting

Companies

KaratWayfair

Stats

Difficulty:Medium

Languages:4

Companies:2

Problem #:1604

Problem Explanation

💡

Think Before You Code

🤔 Ask Yourself

→What is the input? What output do I need?
→Can I solve a smaller version of this problem first?
→What patterns have I seen in similar problems?

✏️ Try This First

Before looking at the solution, try to solve it on paper with a simple example!

🚀 Try on LeetCode

Solution Approach

📋

Step-by-Step Solution

9 steps

1
Step 1: Create a hash map (dictionary) to store each employee's name as the key and a list of their access times (converted to minutes) as the value.
2
Step 2: Iterate through the `keyName` and `keyTime` lists. For each entry, convert the time string to minutes and append it to the corresponding employee's list in the hash map.
3
Step 3: Iterate through the hash map. For each employee:
4
Step 4: Sort the list of access times (in minutes) in ascending order.
5
Step 5: If the number of access times is less than 3, skip to the next employee.
6
Step 6: Iterate through the sorted access times, checking if there is a window of three accesses with a difference of 60 minutes or less. Specifically, check if `times[i + 2] - times[i] <= 60` for any valid index `i`.
7
Step 7: If such a window is found, add the employee's name to the `alerted_users` list if it's not already present, and break the inner loop.
8
Step 8: Sort the `alerted_users` list alphabetically.
9
Step 9: Return the `alerted_users` list.

💡

Pro Tip for Students

Try tracing through each step with a small example on paper before coding. Understanding "why" each step happens is more important than memorizing the code!

⚡

Complexity Analysis

⏱️

Time Complexity

O(nlogn)

💾

Space Complexity

O(n)

📦 Linear space - Memory grows with input size.

📝

Why?

The dominant factor in time complexity is the sorting step. The hashmap operations (insertion and retrieval) take O(1) on average. The space complexity is determined by the size of the hashmap, which can grow up to the number of input entries.

📖New to Big O? Click to learn more

Big O Notation - Quick Guide

O(1)Best - Instant

O(log n)Excellent - Binary Search

O(n)Good - One loop

O(n log n)OK - Sorting

O(n²)Slow - Nested loops

O(2ⁿ)Very Slow - Recursion

Alternative Approaches

Sliding Window Approach without sorting

Instead of sorting, maintain a sliding window of size 3 and iterate through the times for each person. Find the minimum and maximum time in the window. If (max - min) <= 60, an alert is triggered. This can be implemented using a priority queue (min/max heap) to find the min/max elements within the window, or by simple iteration over the window.

Time: O(n*k), where k is the size of the sliding window, which is constant (3). So effectively O(n) if we use a simple iteration to find min and max in window.Space: O(n), where n is the number of keycard entries to store employee times.

Trade-offs: The sorting approach can be slightly faster in practice for small `n` since efficient sorting algorithms are used. For extremely large n and relatively uniform distribution of data, the sliding window approach *might* be slightly faster if well-optimized with min/max heap, but adds considerable code complexity. The performance difference is often marginal, but sorting is cleaner code-wise.

✨

Key Insights

1Insight 1: Convert time strings to a numerical representation (minutes) for easier comparison.
2Insight 2: Group keycard usage times by employee name using a hash table.
3Insight 3: Sort the usage times for each employee to efficiently check for three accesses within an hour.
4Insight 4: Handle duplicates by adding alerted employee names only once to the result list.

📚

Key Terms Explained

For Students

Array

A collection of items stored at contiguous memory locations. Like a row of boxes.

Hash Table

A data structure that maps keys to values for quick lookup. Like a dictionary!

String

A sequence of characters. Like a word or sentence in programming!

Sorting

Arranging elements in a specific order (ascending/descending).

Edge Cases

Edge case 1: Employee has fewer than 3 access times. The solution handles this by checking `if len(times) >= 3` before proceeding with the window check, ensuring that employees with fewer than 3 accesses are not flagged.
Edge case 2: Duplicate entries for the same employee within the same hour. The solution correctly identifies employees with three or more entries in the time frame, even if there are some duplicates. The sorting ensures that closely spaced times are adjacent for checking.
Edge case 3: No alerted users. The solution correctly handles the case where no employees meet the alert criteria by returning an empty sorted list.

⚠️

Common Mistakes to Avoid

✗Common mistake 1: Forgetting to convert time strings to a numerical representation, making it difficult to compare times accurately.

✗Common mistake 2: Failing to sort the access times for each employee, which is essential for efficiently finding the three accesses within one hour.

✗Common mistake 3: Not handling duplicate employee names in the alerted list, resulting in multiple entries for the same employee in the output.

✗Common mistake 4: Integer overflow if the time difference is calculated before converting times into minutes.

✅

Learning Tip

Every mistake is a learning opportunity! When you make an error, understand why it happened before moving on.

🌍

Where is This Used in Real Life?

💼Real-world scenario 1: Security systems that monitor employee access patterns in secure facilities to identify potential security breaches.

🏦Real-world scenario 2: Fraud detection systems in banking that identify unusual transaction patterns within a short period, indicating potential fraudulent activity.

🎮Real-world scenario 3: Monitoring resource usage in cloud computing environments to detect users who are consuming excessive resources within a specific time frame, potentially indicating a denial-of-service attack or misconfigured application.

💡Understanding real-world applications helps you see the "why" behind algorithms. It's not just about passing interviews—these concepts power the apps and websites you use every day!

Author's Notes

💡 Key Insight

When dealing with arrays, always consider if sorting would simplify the problem. Many array problems have O(n) solutions using hash maps or two-pointer technique.

🎯 Interview Tip

Consider multiple approaches before coding. Mentioning alternatives shows breadth of knowledge.

⚠️ Watch Out

Off-by-one errors are the most common mistake. Double-check your loop bounds and array indices.

Written by Saswata Mondal

Software Engineer • Algorithms Enthusiast • Interview Coach

👨‍💻

About the Author

👨‍💻

Saswata MondalAuthor400+ LC Problems

B.Tech CSE, IEM Kolkata | LeetCode Rating: 1672 (Top 15%)

Certified: IBM Java Developer, NPTEL Programming in Java

Last updated: November 12, 2024LeetCode Profile GitHub

This solution has been crafted based on my experience solving 400+ LeetCode problems. Each explanation is designed to help you understand the underlying concepts, not just memorize the code.

Community Tips & Insights

In a real interview, always clarify constraints first. Ask about input size and value ranges.

@TechLeadJul 14

Don't forget to handle the edge case when the input is empty. Many candidates miss this in interviews.

@DevExpert92Jul 7

❓

Frequently Asked Questions

What is the most efficient way to solve array problems?

Most array problems can be solved efficiently using techniques like two-pointer approach, sliding window, or hash maps. The key is identifying whether the array is sorted (enabling binary search) or if tracking seen elements (using hash maps) can help achieve O(n) time complexity.

What are the trade-offs of using a hash table?

Hash tables provide O(1) average-case lookup, insertion, and deletion, making them ideal for frequency counting and duplicate detection. However, they use O(n) extra space and may degrade to O(n) time in worst-case scenarios with hash collisions. Always consider if the space trade-off is acceptable.

What techniques are commonly tested in medium-difficulty problems?

Medium problems often require combining multiple techniques: two-pointer with hash maps, BFS/DFS with state tracking, dynamic programming with optimization, or divide-and-conquer approaches. They test your ability to recognize patterns and choose the right combination of techniques.

How should I practice algorithm problems effectively?

Focus on understanding patterns rather than memorizing solutions. After solving a problem, review optimal solutions and understand the intuition. Group similar problems to recognize patterns. Practice explaining your approach out loud. Review problems after a few days to test retention.

What is the optimal approach for "Alert Using Same Key-Card Three or More Times in a One Hour Period"?

For this medium-level Array problem, the key is to identify the core pattern. Analyze the input constraints to determine acceptable time complexity. Consider whether hash table techniques can optimize the solution. Always handle edge cases and validate your approach with examples before coding.

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