= Question 3. In the following hash table, we insert elements using hashing with open addressing with quadratic probing. Elements have as keys integer numbers, and the hash function for the i-th probe is given by hash(x, i) = (x + ²) %11. We insert, in the given order, the elements with the following keys: 46, 58, 57, 39. Indicate, where these elements will be placed in the table. index value 0 1 2 3 4 5 6 7 8 9 10

= Question 3. In the following hash table, we insert elements using hashing with open addressing with quadratic probing. Elements have as keys integer numbers, and the hash function for the i-th probe is given by hash(x, i) = (x + ²) %11. We insert, in the given order, the elements with the following keys: 46, 58, 57, 39. Indicate, where these elements will be placed in the table. index value 0 1 2 3 4 5 6 7 8 9 10

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Case study: An event company asks you to design the data structure and program prototype for their running event system. The participant for the running event is expected to reach more than 1000 in various categories. You decided to use hashing function method to store the information for each participant's BIB number in the hash table. To begin the problem solving, suppose that 10 registered runners need to be stored in a hash table, HT, with a size of 13. The sample BIB number of the runners are: 101, 102, 103, 104, 107, 111, 121, 217, 157, and 185. Furthermore, you set the hash function to determine the index of the participant in the HT as: hash (BIB) = BIB % table size (or 13) However, if the hash index given by hash(BIB) is already occupied (collision), the linear probing hash function will be used as: hash (BIB) = (Hash (BIB)+1) % 17 and, further collision with hash function: hash (BIB) = (Hash(BIB)+2) % 17 or, hash(BIB) = (Hash (BIB) + n) % 17, where n is probe increment. Refer…
TOPIC: hash table with chaining * For the insert method, we use table[hash(new_entry)].insert(0, new_entry); Why do we insert the new entry at position 0 of the list, not at the end of the list? * When we search for key 100001, the time for each search methods are displayed. Rank the four methods (sequential, binary, hash table 1 and hash table 2) from the slowest to the fastest. * Explain the ranking of the search methods based on time complexity and load factor (for hashing).
The hash table array has capacity of 10. Capacity is the number of slots present in the array that is used to build the hashtable. The hash function returns the absolute value of the key mod the capacity of the hash table. a) Insert these keys in the hash table: 3,23,11,21,1,7,77,8 where the hash table uses quadratic probing to resolve collisions. b) Search and Delete 3 and 11 from the table. Be careful about changing the status of the table slot to “deleted” after deleting each item. c)Search 23 and 21 from the table and print their position.
Assume a hash table utilizes an array of 13 elements and that collisions are handled by separate chaining. Considering the hash function is defined as: h(k)=k mod 13. i) Draw the contents of the table after inserting elements with the following keys: 36, 243, 261, 180, 217, 180, 21, 16, 182, 202, 91, 97, 166, 78, 33, 70, 51, 58.
3. Double hashing is one of the methods to resolve collision. Write a function to implement this method. The equations used in this method are given below. Note: implement everything within the double hash function. P = (P + INCREMENT(Key)) mod TABLE_SIZE INCREMENT(Key) = 1 + (Key mod INCR)
60. The field on which the equality condition is placed is hashing technique is called a. hash field b. cluster filed c. spanned field d. sequential field
Task 2. Instead of using the default hashCode method to each key, create a hash function applying the folding method. Within your hash function Split the string into 3 sub-strings, based from the reg-ex "-" Parse your sub-strings to Integer and add (folding method) Parse the summed value to String Initialize an int variable for your hash to 0 Loop to each character index of the String Apply the general formula for hashing: hash=31*hash+character in that index position; Append & 0xFFFFFFF to it to make the resulting hash positive Convert the hash to index of tablesize=20 using the % operator Return hash What index has the most occurrence of collisions?
struct insert_into_hash_table { // Function takes a constant Book as a parameter, inserts that book indexed by // the book's ISBN into a hash table, and returns nothing. void operator()(const Book& book) { ///// /// TO-DO (8) ||||. // Write the lines of code to insert the key (book's ISBN) and value // ("book") pair into "my_hash_table". /// END-TO-DO (8) // } std::unordered_map& my_hash_table; };
In java Create a hash table using an array with elements 324,221,563,679,234,569,890,5678,654 then perform the following operations. 1) Insert 227 2) Delete 679 and Insert 9 3) Insert 67 4) Display the hash table content Note: This hash table uses technique linear probing when encounters a collision.
which index is also reffered to as a non-clustering index? a) Bidirectional Index b) Primary Index c) Hash Index d) Secondary Index
struct search_within_hash_table { // Function takes no parameters, searches a hash table for a book with an ISBN // matching the target ISBN, and returns a pointer to that found book if such // a book is found, nullptr otherwise. Book* operator()(const Book& unused) { //// TO-DO (21) ||| (/ //// // Write the lines of code to search for the Book within "my_hash_table" // with an ISBN matching "target_isbn". Return a pointer to that book // immediately upon finding it, or a null pointer when you know the book is // not in the container. // // NOTE: Do not implement a linear search, i.e., do not loop from beginning // to end. ///// END-TO-DO (21) |||// //// } std::unordered_map& my_hash_table; const std::string target_isbn; };
import hashlib def hash_function(password, salt, al): if al == 'md5': #md5 hash = hashlib.md5(salt.encode() + password.encode()) hash.update(password.encode('utf-8')) return hash.hexdigest() + ':' + salt elif (al == 'sha1'): #sha1 hash = hashlib.sha1() hash.update(password.encode('utf-8')) return hash.hexdigest() + ':' + salt elif al == 'sha224': #sha224 hash = hashlib.sha224() hash.update(password.encode('utf-8')) return hash.hexdigest() + ':' + salt elif al == 'sha256': #sha256 hash = hashlib.sha256() hash.update(password.encode('utf-8')) return hash.hexdigest() + ':' + salt elif al == 'blake2b': #blake2b512 hash = hashlib.blake2b() hash.update(password.encode('utf-8')) return hash.hexdigest() + ':' + salt else: print("Error: No Algorithm!") if __name__ == "__main__": # TODO: create a list called hash_list that contains # the five hashing algorithsm as strings # md5, sha1, sha224, sha256, blake2b hash_list =…