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L11: DictionariesSlide 2
Definition
The Dictionary Data Structure structure that facilitates searching objects are stored with search keys;
insertion of an object must include a key searching requires a key and returns the
key-object pair removal also requires a key
Need an Entry interface/class Entry encapsulates the key-object pair
(just like with priority queues)
L11: DictionariesSlide 3
Sample Applications
An actual dictionary key: word object: word record (definition,
pronunciation, etc.) Record keeping applications
Bank account records (key: account number, object: holder and bank account info)
Student records (key: id number, object: student info)
L11: DictionariesSlide 4
Dictionary Interface
public interface Dictionary{ public int size(); public boolean isEmpty(); public Entry insert( int key, Object value )
throws DuplicateKeyException;public Entry find( int key );
// return null if not found public Entry remove( int key )
// return null if not found;}
L11: DictionariesSlide 5
Dictionary details/variations
Key types For simplicity, we assume that the keys are ints But the keys can be any kind of object as long as
they can be ordered (e.g., string and alphabetical ordering)
Duplicate entries (entries with the same key) may be allowed Our textbook calls the data structure that does
not allows duplicates a Map, while a Dictionary allows duplicates
For purposes of this discussion, we assume that dictionaries do not allow duplicates
L11: DictionariesSlide 6
Dictionary Implementations
Unordered list (section 8.3.1) Ordered table (section 8.3.3) Binary search tree (section 9.1)
L11: DictionariesSlide 7
Unordered list
Strategy: store the entries in the order that they arrive O( 1 ) insert operation
Can use an array, ArrayList, or linked list Find operation requires scanning the list
until a matching key value is found Scanning implies an O( n ) operation
Remove operation similar to find operation Entries need to be adjusted if using array/ArrayList O( n ) operation
L11: DictionariesSlide 8
Ordered table
Idea: if the list was ordered by key, searching is simpler/easier
Just like for priority queues, insertion is slightly more complex Need to search for proper position of
element -> O( n ) Find: don’t do a linear scan; instead,
do a binary search Note: use array/ArrayList; not a linked
list
L11: DictionariesSlide 9
Binary search
Take advantage of the fact that the elements are ordered
Compare the target key with middle element to reduce the search space in half
Repeat the process until the element is found or search space reduces to 1
Arithmetic on array indexes facilitate easy computation of middle position Middle of S[low] and S[high] is S[(low+high)/2] Not possible with linked lists
L11: DictionariesSlide 10
Binary Search Algorithm
Algorithm BinarySearch( S, k, low, high )
if low > high then return null; // not foundelse mid (low+high)/2 e S[mid]; if k = e.getKey() then return e; else if k < e.getKey() then return BinarySearch( S, k, low, mid-1 ) else return BinarySearch( S, k, mid+1, high )
array of Entries target key
BinarySearch( S, someKey, 0, size-1 );
L11: DictionariesSlide 11
Binary Search Algorithm
42 5 7 8 9 12 14 17 19 22 25 27 28 33 37
low mid high
find(22)
mid = (low+high)/2
L11: DictionariesSlide 12
Binary Search Algorithm
42 5 7 8 9 12 14 17 19 22 25 27 28 33 37
highlow mid
find(22)
mid = (low+high)/2
L11: DictionariesSlide 13
low
Binary Search Algorithm
42 5 7 8 9 12 14 17 19 22 25 27 28 33 37
midhigh
find(22)
mid = (low+high)/2
L11: DictionariesSlide 14
low=mid=high
Binary Search Algorithm
42 5 7 8 9 12 14 17 19 22 25 27 28 33 37
find(22)
mid = (low+high)/2
L11: DictionariesSlide 15
Time complexity of binary search
Search space reduces by half until it becomes 1
n -> n/2 -> n/4 -> … -> 1 log n steps
Find operation using binary search isO( log n )
L11: DictionariesSlide 16
Time complexity
O( log n )
O( n )
find()
O(n )O( n )Ordered Table
O( n )O( 1 )Unsorted List
remove()
insert()
Operation
L11: DictionariesSlide 17
Binary Search Tree (BST)
Strategy: store entries as nodes in a tree such that an inorder traversal of the entries would list them in increasing order
Search, remove, and insert are allO( log n ) operations All operations require a search that
mimics binary search: go to left or right subtree depending on target key value
L11: DictionariesSlide 18
Traversing a BST Insert, remove, and find operations all
require a key First step involves checking for a matching
key in the tree Start with the root, go to left or right child
depending on key value Repeat the process until key is found or a null
child is encountered (not found) For insert operation, duplicate key error occurs if
key already exists Operation is proportional to height of tree
( usually O(log n ) )
L11: DictionariesSlide 19
Insertion in BST (insert 78)44
17 88
32
28
65 97
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7629
80
44
88
65
82
76
80
L11: DictionariesSlide 21
78
Removal from BST (Ex. 1)44
17 88
32
28
65 97
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7629
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w
z
Remove 32
L11: DictionariesSlide 25
78
Removal from BST (Ex. 2)44
17 88
32
28
65 97
8254
7629
80
44
88
65
82
76
80
w
y
x
54
L11: DictionariesSlide 26
Removal from BST (Ex. 2)44
17 88
32
28
65 97
8254
29
44
88
65
82
w
54
78
8080
76
L11: DictionariesSlide 27
Time complexity for BSTs
O( log n ) operations not guaranteed since resulting tree is not necessarily “balanced”
If tree is excessively skewed, operations would be O( n ) since the structure degenerates to a list
Tree could be periodically reordered to prevent skewedness
L11: DictionariesSlide 28
Time complexity (average case)
O(n )O( log n )
O( n )Ordered Table
O( log n )
O( n )
find()
O( log n )
O( log n )
BST
O( n )O( 1 )Unsorted List
remove()
insert()Operation
L11: DictionariesSlide 29
Time complexity (worst case)
O(n )O( log n )
O( n )Ordered Table
O( n )
O( n )
find()
O( n )O( n )BST
O( n )O( 1 )Unsorted List
remove()
insert()Operation
L11: DictionariesSlide 30
About BSTs
AVL tree: BST that “self-balances” Ensures that after every operation, the
difference between the left subtree height and the right subtree height is at most 1
O( log n ) operation is guaranteed Many efficient searching methods are
variants of binary search trees Database indexes are B-trees (number of
children > 2, but the same principles apply)