Asu Cse310 Assignment 5 Spring 2023 Name Of Author Asu Id

Asu Cse310 Assignment 5 Spring 2023 Name Of Author Asu Id

Developing an efficient hash table implementation requires designing core functions such as hashInsert, hashDelete, hashSearch, and hashDisplay. These functions manage Employee records stored within linked lists at each hash table slot. The primary goal is to ensure data is efficiently inserted, searched, deleted, and displayed, with an emphasis on minimizing collisions and optimizing load factor. The hash function should generate indices based on employee attributes, typically combining first name, last name, and ID. Proper memory management through constructors and destructors is essential, alongside functions to compute load factors and display the entire table. This assignment involves showcasing the functionality through a main program that reads input, performs operations, and reports performance metrics of the hash function under different scenarios.

Paper For Above instruction

Efficient data retrieval in large datasets necessitates the use of hash tables, and implementing these structures efficiently involves designing fundamental operations such as insertion, deletion, search, and display functions. In the context of employee data management, these functions facilitate the effective organization and access of employee records stored within a hash table, which employs chaining through linked lists to handle collisions. This paper discusses the design and implementation of core hash table operations, focusing on their algorithmic logic, potential performance issues, and optimization strategies.

The hash table in consideration is represented as an array of linked lists, dynamically allocated to match the specified size upon initialization. Each employee record comprises attributes such as first name, last name, employee ID, and salary, encapsulated within Employee objects. The hash table class, named Hash, includes private members such as the array of linked lists, the size parameter, and the number of slots. Its public interface includes functions for searching, inserting, deleting employees, and displaying the entire content of the table.

The core of hash table management is the hash function, which is crucial for distributing employee records uniformly across available slots to minimize collision chances. A straightforward approach involves summing the ASCII values of all characters in the key string, which is typically a concatenation of employee attributes, and then taking the modulus with the number of slots to determine the appropriate index. This method, while simple, can be improved based on empirical performance analysis to reduce clustering and collision rates.

Insertion into the hash table involves hashing the combined employee key and then inserting the new employee record into the corresponding linked list. The insertion function checks if the employee already exists to prevent duplicates and updates counters like tableSize accordingly. Deletion follows a similar approach, hashing the key to locate the appropriate slot and then removing the employee node from the linked list if found. Searching utilizes the hash function to quickly find the candidate list and then traverses it linearly to identify the specific employee based on ID and names, ensuring accurate data retrieval.

The display function iterates through all table slots, printing the contents of each linked list to provide a comprehensive snapshot of the current hash table state. Load factor computation involves identifying the maximum linked list length among all slots, thereby providing an estimate of table utilization and collision severity. Optimizing load factor measurement and employing dynamic resizing mechanisms can further improve performance.

Analysis of the hash function’s performance typically involves measuring collision rates, load factors, and linked list lengths across multiple datasets. If collision rates are high, suggesting poor distribution, the hash function can be refined by adopting more sophisticated algorithms such as polynomial rolling hashes or incorporating additional hashing techniques to better spread the keys. This refinement is essential for maintaining efficient average-case complexities for search, insert, and delete operations.

The implementation of these core functions, coupled with a robust hash function, ensures the effectiveness of the hash table in managing employee records. Proper management of memory through destructors prevents leaks, while displaying functions facilitate debugging and performance evaluation. Empirical testing with various datasets helps tune the hash function and table size, balancing space and time complexities to achieve optimal performance.

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