Write short notes on:<br>i) Common operation in Data Structure <br>ii) Tournament Tree <br>iii) Data tracker in commin operation

i) Common operations in Data Structures:

• Searching: Finding a specific element in a data structure.
• Insertion: Adding a new element to a data structure.
• Deletion: Removing an element from a data structure.
• Traversal: Visiting each element in a data structure, in some order.
• Sorting: Arranging the elements of a data structure in some order, such as ascending or descending order.
• Merging: Combining two or more data structures into a single data structure.
• Splitting: Dividing a data structure into two or more smaller data structures.

ii) Tournament Tree:

A tournament tree is a complete binary tree in which each node represents a tournament participant. The root of the tree represents the winner of the tournament, and the leaves of the tree represent the participants who were eliminated in the first round.

Tournament trees are used to efficiently determine the winner of a tournament. The algorithm for finding the winner is as follows:

1. Start at the root of the tree.
2. Compare the two children of the current node.
3. The winner of the comparison advances to the next round, and the loser is eliminated.
4. Repeat steps 2 and 3 until there is only one node left in the tree.
5. The winner of the tournament is the participant represented by the remaining node.

Tournament trees have a number of advantages over other methods for determining the winner of a tournament. First, they are very efficient. The algorithm for finding the winner takes O(log n) time, where n is the number of participants in the tournament. Second, tournament trees are fair. Each participant has an equal chance of winning the tournament.

iii) Data tracker in common operations:

A data tracker is a tool that can be used to monitor and track the performance of common data structure operations. Data trackers can be used to identify performance bottlenecks, optimize data structures, and improve the overall performance of an application.

Data trackers can track a variety of metrics, including:

• The number of operations performed
• The time taken to perform each operation
• The memory usage of the data structure
• The number of collisions in a hash table
• The number of resizes in a dynamic array

Data trackers can be used to identify a number of common performance problems, including:

• Slow operations: Data trackers can identify operations that are taking longer than expected to perform. This can help to identify performance bottlenecks and areas where the data structure can be optimized.
• High memory usage: Data trackers can identify data structures that are using a lot of memory. This can help to identify memory leaks and areas where the data structure can be made more efficient.
• Collisions in hash tables: Data trackers can identify hash tables that are experiencing a lot of collisions. This can help to identify hash functions that are not performing well and areas where the hash table can be made more efficient.
• Resizes in dynamic arrays: Data trackers can identify dynamic arrays that are being resized frequently. This can help to identify areas where the dynamic array can be made more efficient.

Data trackers are a valuable tool for improving the performance of data structures and applications. By tracking the performance of common operations, data trackers can help to identify performance bottlenecks, optimize data structures, and improve the overall performance of an application.