What are the different types of search techniques? Explain the one which is more efficient among them. Explain the following: i) Symbol table ii) Hash table iii) Dynamic tree table

Types of Search Techniques:

There are various search techniques used in computer science to efficiently retrieve data from a collection of items. Some of the commonly used techniques include:

1. Linear Search:

   • Linear search is the simplest search technique where each item in the collection is examined sequentially until the desired item is found or the entire collection is exhausted.
   • Advantages:
     • Easy to implement
   • Disadvantages:
     • Inefficient for large collections

2. Binary Search:

   • Binary search is a divide-and-conquer search algorithm that repeatedly divides the collection in half until the desired item is found.
   • Advantages:
     • Efficient for sorted collections
     • Time complexity: O(log n)
   • Disadvantages:
     • Requires the collection to be sorted

3. Hashing:

   • Hashing is a technique that uses a hash function to map each item to a unique key. The key is then used to directly access the item in a hash table, a data structure designed for fast lookup.
   • Advantages:
     • Very efficient for searching
     • Average time complexity: O(1)
   • Disadvantages:
     • Requires more memory than other techniques
     • Can suffer from collisions, where multiple items have the same hash key

4. Tree Search:

   • Tree search algorithms, such as binary search trees and AVL trees, organize data in a hierarchical structure, allowing for efficient searching and retrieval.
   • Advantages:
     • Efficient for sorted collections
     • Supports efficient insertion and deletion
   • Disadvantages:
     • More complex to implement than linear or binary search

Comparison of Efficiency:

Among the different search techniques, hashing is generally considered to be the most efficient for searching. This is because hashing allows for direct access to items based on their key, resulting in an average time complexity of O(1). In contrast, linear search has a time complexity of O(n), where n is the size of the collection, and binary search has a time complexity of O(log n).

Symbol Table:

A symbol table is a data structure that maps symbols, such as variable names or function names, to their corresponding values. Symbol tables are commonly used in compilers and interpreters to keep track of the names and values of variables and functions in a program.

Hash Table:

A hash table is a data structure that uses a hash function to map keys to values. Each key is associated with a unique value, and the hash function is used to determine the location of the value in the hash table. Hash tables are used in a variety of applications, such as databases, caches, and associative arrays.

Dynamic Tree Table:

A dynamic tree table is a data structure that supports efficient range queries on a set of data. It combines the efficiency of binary search trees with the flexibility of dynamic programming. Dynamic tree tables are used in a variety of applications, such as finding the minimum or maximum value in a range, finding the sum of values in a range, and finding the longest common substring between two strings.