application of tries
DESCRIPTION
TRANSCRIPT
![Page 1: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/1.jpg)
APPLICATION OF TRIES
![Page 2: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/2.jpg)
Why Trie Data Structure?
• Searching trees in general favor keys which are of
fixed size since this leads to efficient storage
management.
• However in case of applications which are retrieval
based and which call for keys varying length, tries
provide better options.
• Tries are also called as Lexicographic Search trees.
• The name trie (pronounced as “try”)originated from
the word “retrieval”.
![Page 3: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/3.jpg)
Definition For Trie:
• A trie of order m may be empty.
• If not empty, then it consists of an ordered sequence
of exactly m tries of order m.
• The branching at any level of the trie is determined
only by the portion and not by the whole word.
• Alphabetic keys require a trie of order 27(26 letters of
the alphabet + a blank) for their storage and retrieval.
![Page 4: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/4.jpg)
Representation Of Trie
• The trie have two category of node structures.▫Branch node
▫ Information node
• A branch node is merely a collection of LINK
fields each pointing either to a branch node or to
an information node.
• An information node holds the keys that is to be
stored in the trie.
![Page 5: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/5.jpg)
Operations In Trie
•The three operations in the trie data
Structure are
•Searching a trie
• Insertion
•Deletion
![Page 6: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/6.jpg)
Example
•Construct a Trie for the keys001,100,111,011,010STEP 1: Insert (001,100)
0 1
001 100
![Page 7: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/7.jpg)
Example
•STEP 2: 0 1Insert(111)
0 1
001
111100
![Page 8: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/8.jpg)
Example•STEP 3:Insert(011) 0 1
0 1 0 1
111100001 011
![Page 9: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/9.jpg)
Example
•STEP 4:Insert(010)
0 1
0 1 0 1
001 111100
010 011
![Page 10: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/10.jpg)
INSERTION
•To Insert a key K into the trie we begin as
we would to search for the key k, possibly
moving down the trie.
•At the point where the LINK field of the
branch node leads to NIL, the key k is
inserted as an information node.
![Page 11: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/11.jpg)
Insertion • In the Above constructed trie INSERT A
KEY 101. 0 1
0 1 0 1
001 111
010 011100
101
![Page 12: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/12.jpg)
Deletion• The deletion of a key K from a trie proceeds as one would
to search for the key.• On reaching the information node(node l)holding k, the
same is deleted.▫ It need to be ensured the branch node to which node l
is linked accommodates other information node as well! If there is more than1 information node/if there is at
least one LINK field/or both ,then deletion id done. If it leaves the branch node with just one more
key ,we delete the branch node and push the node to a higher level
If the situation leads to node being the only non empty node , once again we delete the branch node and push node to a higher level.
![Page 13: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/13.jpg)
Deletion•Delete 010: 0 1
0 1 0 1
001 111
010 011100
101
![Page 14: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/14.jpg)
Performance Of trie
• The performance of search trees is determined by the
number of keys that form the tree.
• The complexities of the search ,delete and insert
operations were given by O(h) where the height h is
dependent on the number of keys represented in the
search tree.
• In contrast, the performance of the trie is dependent on
the length of the key-The number of characters forming
the key rather than the number of keys itself.
![Page 15: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/15.jpg)
APPLICATIONS OF TRIE DATA STRUCTURES
![Page 16: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/16.jpg)
TRIES IN AUTO COMPLETE
• Since a trie is a tree-like data structure in which each
node contains an array of pointers, one pointer for each
character in the alphabet.
• Starting at the root node, we can trace a word by
following pointers corresponding to the letters in the
target word.
• Starting from the root node, you can check if a word
exists in the trie easily by following pointers
corresponding to the letters in the target word.
![Page 17: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/17.jpg)
AUTO COMPLETE
•Auto-complete functionality is used widely
over the internet and mobile apps. A lot of
websites and apps try to complete your
input as soon as you start typing.
•All the descendants of a node have a
common prefix of the string associated with
that node.
![Page 18: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/18.jpg)
![Page 19: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/19.jpg)
AUTO COMPLETE IN GOOGLE SEARCH
![Page 20: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/20.jpg)
WHY TRIES IN AUTO COMPLETE
• Implementing auto complete using a trie is
easy.
•We simply trace pointers to get to a node
that represents the string the user
entered. By exploring the trie from that
node down, we can enumerate all strings
that complete user’s input.
![Page 21: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/21.jpg)
CRIMINOLOGY
• Suppose that you are at the scene of a crime and
observe the first few characters CRX on the registration
plate of the getaway car. If we have a trie of
registration numbers, we can use the characters CRX
to reach a subtrie that contains all registration
numbers that begin with CRX. The elements in this
subtrie can then be examined to see which cars satisfy
other properties that might have been observed.
![Page 22: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/22.jpg)
AUTOMATIC COMMAND COMPLETION
• When using an operating system such as Unix
or DOS, we type in system commands to
accomplish certain tasks. For example, the
Unix and DOS command cd may be used to
change the current directory.
![Page 23: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/23.jpg)
Commands that have the prefix “ps”
• ps2ascii ps2pdf psbook psmandup psselect
• ps2epsi ps2pk pscal psmerge pstopnm
• ps2frag ps2ps psidtopgm psnup pstops
• ps2gif psbb pslatex psresize pstruct
• Figure 10 Commands that begin with "ps"
We can simply the task of typing in commands by providing a command completion facility which automatically types in the command suffix once the user has typed in a long enough prefix to uniquely identify the command. For instance, once the letters psi have been entered, we know that the command must be psidtopgm because there is only one command that has the prefix psi. In this case, we replace the need to type in a 9 character command name by the need to type in just the first 3 characters of the command!
![Page 24: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/24.jpg)
• Longest prefix match (also called Maximum prefix length match)
refers to an algorithm used by routers in Internet Protocol (IP)
networking to select an entry from a routing table .
• Because each entry in a routing table may specify a network, one
destination address may match more than one routing table entry.
The most specific table entry — the one with the highest subnet
mask — is called the longest prefix match. It is called this because
it is also the entry where the largest number of leading address
bits in the table entry match those of the destination address.
LONGEST PREFIX MATCHING
![Page 25: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/25.jpg)
For example, consider this IPv4 routing table (CIDR notation
is used):
192.168.20.16/28
192.168.0.0/16
When the address 192.168.20.19 needs to be looked up,
both entries in the routing table "match". That is, both
entries contain the looked up address. In this case, the
longest prefix of the candidate routes is 192.168.20.16/28,
since its subnet mask (/28) is higher than the other entry's
mask (/16), making the route more specific.
![Page 26: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/26.jpg)
• A network browser keeps a history of the URLs of
sites that you have visited. By organizing this
history as a trie, the user need only type the prefix
of a previously used URL and the browser can
complete the URL.
![Page 27: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/27.jpg)
![Page 28: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/28.jpg)
SPELL CHECKERS
• Spell checkers are ubiquitous. Word
processors have spell checkers, as
do browser-based e-mail clients.
They all work the same way: a
dictionary is stored in some data
structure, then each word of input
is submitted to a search in the data
structure, and those that fail are
flagged as spelling errors
![Page 29: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/29.jpg)
SPELL CHECKERS
•There are many appropriate data
structures to store the word list, including
a sorted array accessed via binary search,
a hash table, or a bloom filter. In this
exercise you are challenged to store the
word list character-by-character in a trie.
![Page 30: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/30.jpg)
Spell Check..
![Page 31: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/31.jpg)
Spell Check..a b c … p … z
i u
a g s
peg pest
0
1
2
a e
page
0
pig
pug
![Page 32: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/32.jpg)
PHONE BOOK SEARCH..
•Trie data structure are mostly used to search for a contact on phone book.
•Prefix Matching a = a*
![Page 33: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/33.jpg)
Example
albertoramsankarstarstella
a b c … r s … z
t
ram
a e
star stella
0
1
2
Contacts in Phone book
a
sankar
alberto
![Page 34: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/34.jpg)
PHONE BOOK SEARCH..
• Suffix Matching
• Can be used to index all
suffixes in a text in order
to carry out fast full text
searches.
an = *an*
E
![Page 35: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/35.jpg)
TRIES IN T9
• T9 is a technology used on many mobile phones to make
typing text messages easier.
• The idea is simple - each number of the phone's keypad
corresponds to 3-4 letters of the alphabet.
• Many phones will notice when you type in a word that is
not in its dictionary, and will add that word. Others keep
track of the frequency of certain words and favor those
words over other words that have the same sequence of
keypresses.
![Page 36: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/36.jpg)
TRIES IN T9
•How does a T9 dictionary work?
• It can be implemented in several ways, one of
them is Trie. The route is represented by the
digits and the nodes point to collection of words.
• T9 works by filtering the possibilities down
sequentially starting with the first possible
letters.
![Page 37: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/37.jpg)
TRIES IN T9
• It can be implemented using nested hash
tables as well, the key of the hash table is a
letter and on every digit the algorithm
calculates all possible routes (O(3^n) routes).
•For example, If we type '4663' we get 'good'
when we press down button we get 'gone'
then 'home' etc..
![Page 38: Application of tries](https://reader034.vdocuments.site/reader034/viewer/2022051817/547a81c7b4af9f56508b4590/html5/thumbnails/38.jpg)
THANK YOU