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Application of tries

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Application of tries

  1. 1. APPLICATION OF TRIES
  2. 2. 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.
  3. 3. 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
  4. 4. 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.
  5. 5. Operations In Trie• The three operations in the trie data Structure are • Searching a trie • Insertion • Deletion
  6. 6. Example• Construct a Trie for the keys001,100,111,011,010STEP 1:Insert (001,100) 0 1 001 100
  7. 7. Example• STEP 2: 0 1Insert(111) 0 1 001 100 111
  8. 8. Example• STEP 3:Insert(011) 0 1 0 1 0 1 001 011 100 111
  9. 9. Example• STEP 4:Insert(010) 0 1 0 1 0 1 001 100 111 010 011
  10. 10. 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.
  11. 11. Insertion• In the Above constructed trie INSERT A KEY 101. 0 1 0 1 0 1 001 111 010 011 100 101
  12. 12. 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.
  13. 13. Deletion• Delete 010: 0 1 0 1 0 1 001 111 010 011 100 101
  14. 14. 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.
  15. 15. APPLICATIONS OF TRIE DATA STRUCTURES
  16. 16. 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.
  17. 17. 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.
  18. 18. AUTO COMPLETE IN GOOGLE SEARCH
  19. 19. 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.
  20. 20. 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.
  21. 21. 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.
  22. 22. 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!
  23. 23. LONGEST PREFIX MATCHING• 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.
  24. 24. For example, consider this IPv4 routing table (CIDR notation is used):192.168.20.16/28192.168.0.0/16When 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 entrys mask (/16), making the route more specific.
  25. 25. • 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.
  26. 26. 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
  27. 27. 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.
  28. 28. Spell Check..
  29. 29. Spell Check.. a bc… 0 p …z0 a e i u 1 a g s pug page 2 pig peg pest
  30. 30. PHONE BOOK SEARCH..• Trie data structure are mostly used to search for a contact on phone book.• Prefix Matching a = a*
  31. 31. Example a bc… r s …z Contacts in Phone book 0 alberto t ram a sankaralberto ram 1 star stella a e sanka 2 r star stella
  32. 32. PHONE BOOK SEARCH..• Suffix Matching• Can be used to index all Esuffixes in a text in order tocarry out fast full textsearches.
  33. 33. 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 phones 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.
  34. 34. 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.
  35. 35. 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..

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