COSC 2007 Data Structures II

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# COSC 2007 Data Structures II - PowerPoint PPT Presentation

COSC 2007 Data Structures II. Chapter 13 Advanced Implementation of Tables III. Topics. Hashing Definition Hash function Key Hash value collision Open hashing. Common Problem. A common pattern in many programs is to store and look up data Find student record, given ID#

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### COSC 2007 Data Structures II

Chapter 13

Topics
• Hashing
• Definition
• Hash function
• Key
• Hash value
• collision
• Open hashing
Common Problem
• A common pattern in many programs is to store and look up data
• Find student record, given ID#
• Find person address, given phone #
• Because it is so common, many data structures for it have been investigated
• How?
Phone Number Problem
• Problem: phone company wants to implement caller ID.
• given a phone number (the key), look up person’s name or address(the data)
• lots of phone numbers (P=107-1) in a given area code
• only a small fraction of them are in use
• Nobody has a phone number :0000000 or 0000001
Comparison of Time Complexity (average)

Operation Insertion Deletion Search

Unsorted Array O(1) O(n) O(n)

Unsorted reference O(1) O(n) O(n)

Sorted Array O(n) O(n) O(logn)

Sorted reference O(n) O(n) O(n)

BST O(logn) O(logn) O(logn)

Can we do better than O(logn)?

Can we do better than O(log N)?
• All previous searching techniques require a specified amount of time (O(logn) or O(n))
• Time usually depends on number of elements (n) stored in the table
• In some situations searching should be almost instantaneous -- how?
• Examples
• 911 emergency system
• Air-traffic control system

•••

Null

Sub

Null

Null

Null

Null

Xu

Null

•••

•••

•••

000-0000

000-0001

000-0002

259-1623

263-3049

Can we do better than O(log N)?
• Answer: Yes … sort of, if we\'re lucky.
• General idea: take the key of the data record you’re inserting, and use that number directly as the item number in a list (array).
• Search is O(1), but huge amount of space wasted. – how to solve this?
Hashing
• Basic idea:
• Don\'t use the data value directly.
• Given an array of size B, use a hash function, h(x), which maps the given data record x to some (hopefully) unique index (“bucket”) in the array.

0

1

h

x

h(x)

B-1

What is Hash Table?
• The simplest kind of hash table is an array of records.
• This example has 101 records.

[ 0 ]

[ 1 ]

[ 2 ]

[ 3 ]

[ 4 ]

[ 5 ]

[100]

. . .

An array of records

[ 4 ]

What is Hash Table?

Number 256-2879

8888 Queen St.

Linda Kim

• Each record has a special

field, called its key.

• In this example, the key

is a long integer field

called Number.

[ 0 ]

[ 1 ]

[ 2 ]

[ 3 ]

[ 4 ]

[ 5 ]

[100]

. . .

An array of records

[ 4 ]

What is Hash Table?

Number 256-2879

• The number is person\'s

phone number,

and the rest is

[ 0 ]

[ 1 ]

[ 2 ]

[ 3 ]

[ 4 ]

[ 5 ]

[100]

. . .

An array of records

Number 281942902

Number 233667136

Number 506643548

Number 155778322

What is Hash Table?
• When a hash table is in use, some spots contain valid records, and other spots are "empty".

[ 0 ]

[ 1 ]

[ 2 ]

[ 3 ]

[ 4 ]

[ 5 ]

[100]

. . .

An array of records

Number 281942902

Number 233667136

Number 506643548

Number 155778322

Inserting a New Record?

Number 265-1556

• In order to insert a new record,

the key must somehow be

converted toan array index.

• The index is called the

hash valueof the key.

[ 0 ]

[ 1 ]

[ 2 ]

[ 3 ]

[ 4 ]

[ 5 ]

[100]

. . .

An array of records

Number 281942902

Number 233667136

Number 506643548

Number 155778322

Inserting a New Record?

Number 265-1556

• Typical way to create a hash value:

(Number mod 101)

What is (265-1556 mod 101) ?

[ 0 ]

[ 1 ]

[ 2 ]

[ 3 ]

[ 4 ]

[ 5 ]

[100]

. . .

An array of records

Number 281942902

Number 233667136

Number 506643548

Number 155778322

Inserting a New Record?

Number 265-1556

• Typical way to create a hash value:

(Number mod 101)

3

What is (2651556 mod 101) ?

[ 0 ]

[ 1 ]

[ 2 ]

[ 3 ]

[ 4 ]

[ 5 ]

[100]

. . .

An array of records

[3]

Number 281942902

Number 233667136

Number 506643548

Number 155778322

Number 265-1556

Inserting a New Record?
• The hash value is used for

the location of the

new record.

[ 0 ]

[ 1 ]

[ 2 ]

[ 3 ]

[ 4 ]

[ 5 ]

[100]

. . .

An array of records

Number 281942902

Number 233667136

Number 580625685

Number 506643548

Number 155778322

Inserting a New Record?
• The hash value is used for the location of the new record.

[ 0 ]

[ 1 ]

[ 2 ]

[ 3 ]

[ 4 ]

[ 5 ]

[100]

. . .

An array of records

What is Hashing?
• What is hashing?
• Each item has a unique key.
• Use a large array called a Hash Table.
• Use a Hash Function.
• Hashing is like indexing in that it involves associating a key with a relative record address.
• Hashing, however, is different from indexing in two important ways:
• With hashing, there is no obvious connection between the key and the location.
• With hashing two different keys may be transformed to the same address.
• A Hash functionis a function h(K) which transforms a key K into an address.

0

Array

(Hash table)

Search key

(Hash function)

N-1

What is Hashing?
• An address calculator (hashing function) is used to determine the location of the item
What Can Be Hashed?
• Anything!
• Can hash on numbers, strings, structures, etc.
• Java defines a hashing method for general objects which returns an integer value.
Where do we use Hashing?
• Databases (phone book, student name list).
• Spell checkers.
• Computer chess games.
• Compilers.
Hashing and Tables
• Hashing gives us another implementation of Table ADT
• Hashing operations
• Initialize
• all locations in Hash Table are empty.
• Insert
• Search
• Delete
• Hash the key; this gives an index; use it to find the value stored in the table in O(1)
• Great improvement over Log N.
Hashing
• Insert pseudocode

tableInsert (newItem)

i = the array index that the address calculator gives you for the new item’s search key

table[i]=newItem

• Retrieval pseudocode

tableRerieve (searchKey)

i = array index for searchKey given by the hash function

if (table[i].getKey( ) == searchKey)

return table[i]

else

return null

Hashing
• Deletion pseudocode

tableDelete (searchKey)

i = array index for searchKey given by the hash function

success=(tabke[I].getKey() equals searchKey

if (success)

Delete the item from table[i]

Return success

Hash Tables

Table size

Entries are numbered 0 to TSIZE-1

Mapping

Simple to compute

Ideally 1-1: not possible

Even distribution

Main problems

Choosing table size

Choosing a good hash function

What to do on collisions

TSIZE = 11

110

0

110

210

320

460

520

600

110

210

320

460

520

600

0

210,320

1

0

15

20

22

26

49

54

20

1

2

1

520

2

3

22

2

4

3

3

5

4

4

54

5

6

5

600

15

6

7

6

26

7

8

7

8

9

8

460

9

10

9

49

How to choose the Table Size?

H (Key) = Key mod TSIZE

TSIZE = 10

How to choose a Hashing Function?
• The hash function we choose depends on the type of the key field (the key we use to do our lookup).
• Finding a good one can be hard
• Rule
• Be easy to calculate.
• Use all of the key.
How to choose a Hashing Function?
• Example:
• Student Ids (integers)

h(idNumber) = idNumber % B

eg. h(678921) = 678921 % 100 = 21

• Names (char strings)

h(name) = (sum over the ascii values) % B

eg. h(“Bill”) = (66+105+108+108) % 101 = 86

Number 281942902

Number 233667136

Number 580625685

Number 506643548

Number 155778322

Number 2641455

Collision
• Here is another new record to

insert, with a hash value of 2.

My hash

value is [2].

[ 0 ]

[ 1 ]

[ 2 ]

[ 3 ]

[ 4 ]

[ 5 ]

[100]

. . .

An array of records

What to do on collisions?

Open hashing (separate chaining)

Linear Probing

Double hashing

0

49

4

81

1

0

25

9

16

36

64

1

2

3

4

5

6

7

8

9

Open hashing (separate chaining)
• Keep a list of all elements that hash to the same value.

0

1

4

9

16

25

36

49

64

81

0

9

16

49

36

64

1

81

4

25

0

1

2

3

4

5

6

7

8

9

Open hashing (separate chaining)
• Secondary Data Structure
• List
• Search tree
• another hash table
• We expect small collision
• List
• Simple
Operations with Chaining
• Insert with chaining
• Apply hash function to get a position.
• Insert key into the Linked List at this position.
• Search with chaining
• Apply hash function to get a position.
• Search the Linked List at this position.
Open hashing (separate chaining)

public class ChainNode

{

Private KeyedItem item;

private ChainNode next;

public ChainNode(KeyedItem newItem, ChainNode nextNode) {

item = newItem;

next= nextNode;

// set and get methods

}

} // end of ChainNode

Open hashing (separate chaining)

public class HashTable

{

private final int HASH_TABLE_SIZE = 101; // size of hash table

private ChainNode [] table; //hash table

private int size; //size of hash table

public HashTable() {

table = new ChainNode [HASH_TABLE_SIZE];

size =0;

}

public bool tableIsEmpty() { return size ==0;}

public int tableLength() { return size;}

public void tableInsert(KeyedItem newItem) throws HashException {}

public boolean tableDelete(Comparable searchKey) {}

public KeyedIten tableRetrieve(Comparable searchKey) {}

} // end of hashtable

Open hashing (separate chaining)

tableInsert(newItem)

if (table is not full) {

searchKey= the search key of newItem

i = hashIndex (searchKey)

node= reference to a new node containing newItem

node.setNext (table[I]);

table[I] = node

}

else //table full

throw new HashException ()

Open hashing (separate chaining)

tableRetrieve (searchKey)

i = hashIndex (searchKey)

node= table [I];

while ((node !=null)&& node.getItem().getKey()!= searchKey )

node=getNext ()

if (node !=null)

return node.getITem()

else

return null

Evaluation of Chaining
• More complex to implement.
• Search and Delete are harder. We need to know: The number of elements in the table (N); the number of buckets (B); the quality of the hash function
• Worse case (O(n)) for searching
• Insertions is easy and quick.
• Allows more records to be stored.
• The size of table is dynamic
Review
• A(n) ______ maps the search key of a table item into a location that will contain the item.
• hash function
• hash table
• AVL tree
• heap
Review
• A hash table is a(n) ______.
• stack
• queue
• array
• list
Review
• The condition that occurs when a hash function maps two or more distinct search keys into the same location is called a(n) ______.
• disturbance
• collision
• Rotation
• congestion
Review
• ______ is a collision-resolution scheme that searches the hash table sequentially, starting from the original location specified by the hash function, for an unoccupied location.
• Linear probing
• Double hashing
• Separate chaining
Review
• ______ is a collision-resolution scheme that searches the hash table for an unoccupied location beginning with the original location that the hash function specifies and continuing at increments of 12, 22, 32, and so on.
• Linear probing
• Double hashing