Download Data Structures for Car Racing Event Application: Design, Implementation, and Assessment and more Quizzes Database Programming in PDF only on Docsity!
Designing and Implementing Data
Structures for a Car Racing Event
Application
Specification, Implementation, and
Assessment of Data Structures for a Sample
Scenario
LO1: Examine abstract data types, concrete data
structures, and algorithms
P1: Create a design specification for data structures explaining the valid operations that can be carried out on the structures
The scenario presented involves organizing a car racing event with a maximum of 6 cars (participants) competing in 3 rounds. After each round, the car with the lowest rank is eliminated. The application developed for this event needs to handle the following functions:
Register car details Delete a car Insert 3 rounds of results Find the winners (1st, 2nd, 3rd) Search for a particular car
To manage this scenario, the following data structures can be used:
Array : To store the car details (car number, brand, sponsor, and driver details) Queue : To implement the elimination process after each round, where the car with the lowest rank is removed from the queue. Linked List : To store the registered car details in the order they were added, allowing the management to view the newest to oldest registered cars.
The valid operations that can be performed on these data structures are:
Array : Add a new car Delete a car Update car details
Search for a car
Queue :
Enqueue a car (add a car to the end of the queue) Dequeue a car (remove the car at the front of the queue) Peek at the front car
Check if the queue is empty
Linked List :
Insert a new car at the beginning of the list Delete a car from the list Search for a car in the list Traverse the list to display the cars in the order they were registered
M1: Illustrate, with an example, a concrete data structure for a First In First Out (FIFO) queue
A FIFO queue can be implemented using an array or a linked list. Here's an example of a FIFO queue implemented using an array:
private int size; public FIFOQueue(int capacity) { queue = new int[capacity]; front = 0; rear = -1; size = 0; } public void enqueue(int element) { if (rear == queue.length - 1) { rear = -1; } queue[++rear] = element; size++; } public int dequeue() { int element = queue[front++]; if (front == queue.length) { front = 0; } size--; return element; } public boolean isEmpty() { return size == 0; } public int size() { **D1: Analyse the operation, using illustrations, of two network shortest path algorithms, providing an example of each** Two common algorithms for finding the shortest path in a network are: **Dijkstra's Algorithm** : Dijkstra's algorithm is a greedy algorithm that finds the shortest path between a single source node and all other nodes in a weighted graph. It works by maintaining a set of visited nodes and a set of unvisited nodes. At each step, it selects the unvisited node with the smallest distance from the source and adds it to the visited set. The algorithm then updates the distances of all unvisited neighbors of the selected node, using the newly discovered path. The algorithm continues until all nodes have been visited or the destination node has been reached. Example: Consider the following weighted graph: A -- 2 -- B | \ | 3 4 | | \| C -- 1 -- D Using Dijkstra's algorithm, the shortest path from A to D would be A -> C -> D, with a total distance of 4. **Breadth-First Search (BFS)** : BFS is an algorithm for traversing or searching a graph or tree data structure. It starts at the source node and explores all the neighboring nodes at the present depth before moving on to the nodes at the next depth level. BFS uses a queue data structure to keep track of the nodes to be visited. The algorithm continues until all nodes have been visited or the destination node has been reached. Example: Consider the same weighted graph as before: A -- 2 -- B | \ | 3 4 | | \| C -- 1 -- D Using BFS, the shortest path from A to D would be A -> C -> D, with a total distance of 4. Both Dijkstra's algorithm and BFS can be used to find the shortest path in a network, but they have different strengths and weaknesses. Dijkstra's algorithm is better suited for weighted graphs, while BFS is more efficient for unweighted graphs. The choice between the two algorithms depends on the specific requirements of the problem. ## LO2: Specify abstract data types and algorithms in a formal ## notation **P3: Using an imperative definition, specify the abstract data type for a software stack** An abstract data type (ADT) for a software stack can be defined as follows: ADT Stack Data: - A collection of elements Operations: - push(element): Adds an element to the top of the stack - pop(): Removes and returns the element at the top of the stack - peek(): Returns the element at the top of the stack without removing it - isEmpty(): Returns true if the stack is empty, false otherwise - size(): Returns the number of elements in the stack Constraints: - The stack follows the Last-In-First-Out (LIFO) principle - The stack has a finite capacity This imperative definition of the Stack ADT specifies the data (a collection of elements) and the valid operations that can be performed on the stack, including pushing an element, popping an element, peeking at the top element, checking if the stack is empty, and getting the size of the stack. The constraints ensure that the stack follows the LIFO principle and has a finite capacity. **M3: Examine the advantages of encapsulation and information hiding when using an ADT** The use of an ADT provides several advantages, including encapsulation and information hiding: **Encapsulation** : Encapsulation is the principle of bundling data and the methods that operate on that data within a single unit or object. When using an ADT, the implementation details of the data structure are hidden from the client code, and the client can only interact with the ADT through the provided set of operations. This helps to ensure that the internal state of the ADT is always valid and consistent, as the client cannot directly modify the data. **Information Hiding** : Information hiding is the principle of hiding the internal implementation details of an ADT from the client code. By using an ADT, the client code does not need to know how the data structure is implemented internally. It only needs to know the set of operations provided by the ADT. This allows the implementation of the ADT to be changed without affecting the client code, as long as the set of operations remains the same. Information hiding promotes modularity, flexibility, and maintainability of the codebase. The advantages of encapsulation and information hiding when using an ADT include: **Abstraction** : The client code can work with the ADT without needing to understand the underlying implementation details. **Modularity** : The implementation of the ADT can be changed without affecting the client code, as long as the set of operations remains the same. **Flexibility** : The ADT can be used in different contexts and applications without the need to modify the client code. public void findWinners() { Car firstPlace = raceQueue.poll(); Car secondPlace = raceQueue.poll(); Car thirdPlace = raceQueue.poll(); System.out.println("1st Place: " + firstPlace.getBrand()); System.out.println("2nd Place: " + secondPlace.getBrand()); System.out.println("3rd Place: " + thirdPlace.getBrand()); } public Car searchCar(int carNumber) { for (Car car : cars) { if (car.getNumber() == carNumber) { return car; } } return null; } } ``` In this implementation, the CarRacingApp class represents the application for managing the car racing event. It uses an ArrayList to store the registered cars and a Queue to manage the elimination process after each round. The registerCar, deleteCar, insertRoundResults, findWinners, and searchCar methods implement the required functionality of the application. **P5: Implement error handling and report test results** To ensure the robustness of the application, we can implement error handling and report the test results. Here's an example: ```java public class CarRacingAppTest { public static void main(String[] args) { CarRacingApp app = new CarRacingApp(); // Test case 1: Register a car Car car1 = new Car(1, "Ferrari", "RedBull", "Lewis Hamilton"); try { app.registerCar(car1); System.out.println("Car registered successfully."); } catch (IllegalArgumentException e) { System.out.println("Error: " + e.getMessage()); } // Test case 2: Delete a car try { app.deleteCar(1); System.out.println("Car deleted successfully."); } catch (IllegalArgumentException e) { System.out.println("Error: " + e.getMessage()); } // Test case 3: Insert round results try { app.insertRoundResults(); System.out.println("Round results inserted successfully."); } catch (IllegalStateException e) { System.out.println("Error: " + e.getMessage()); } // Test case 4: Find winners try { app.findWinners(); System.out.println("Winners found successfully."); } catch (IllegalStateException e) { System.out.println("Error: " + e.getMessage()); } // Test case 5: Search for a car Car searchedCar = app.searchCar(1); if (searchedCar != null) { System.out.println("Car found: " + searchedCar.getBrand()); } else { System.out.println("Car not found."); } } } ``` In this example, we have implemented error handling using try-catch blocks to handle various exceptions that may occur during the execution of the application. The test results are reported by printing the appropriate messages to the console. **M4: Demonstrate how the implementation of an ADT/algorithm solves a well-defined problem** The implementation of the CarRacingApp class and its associated data structures and algorithms demonstrates how they can be used to solve the well-defined problem of managing a car racing event. The ArrayList is used to store the registered cars, which allows for efficient addition, deletion, and searching of cars. The Queue is used to manage the elimination process after each round, where the car with the lowest rank is removed from the queue. The registerCar, deleteCar, insertRoundResults, findWinners, and searchCar methods implement the required functionality of the application, using the appropriate data structures and algorithms to achieve the desired results.
