1. Autocomplete Based on Ternary Search Tree
Niu Wenhao, Lu Yan, Wang Sangtian

2. Motivation and Introduction
Part 1
Motivation and Introduction
——By Niu Wenhao

3. Motivation Motivation
To improve the performance of autocomplete function of Acemap.

4. Original autocomplete function
There are some problems in original autocomplete function.
Large memory costs
Redis
Very slow for some keywords

5. Memory cost of 100,000 keywords
Our solution
Ternary Search Tree (TST)
Redis
TST
Memory cost of 100,000 keywords
210MB
136MB
Response time
Might be very long!
0.38ms

6. Demo
Demo
:8123/Autocomplete_v1.5
Return a JSON

7. Java Web Java Web Environment: Java SE Development Kit 8.0
Server: Apache Tomcat 7.0
IDE: Eclipse

8. Java Web
Server
Request
JSP Page
Servlet
Response

9. Part 2
Details about TST
——By Lu Yan

10. Assign a character to each node. Give each node three links:
Ternary Search Tree
Assign a character to each node.
Give each node three links:
Left link if key’s next character < node’s character
Middle link if key’s next character == node’s character
Right link if key’s next character > node’s character

11. After inserting “sam” s a s a w a m l d m p s e

12. After inserting “sam”, “sad”w a m l d m p d s e

13. After inserting “sam”, “sad”, “sap”w a m l d m p d p s e

14. After inserting “sam”, “sad”, “sap”, “same”w a m l d m p d e p s e

15. After inserting “sam”, “sad”, “sap”, “same”, “a”w a m l d m p d e p s e

16. After inserting “sam”, “sad”, “sap”, “same”, “a”, and “awls”

17. Performance Time Complexity Space Complexity Redis O(L) O(N * L * L)
(should be better since there could be many collisions)
Trie
O(K * L)
O(N * L * R)
Trie with hashmaps
O(N * L)
TST
O(K * (L + log N))
N = # number of words
L = # average length of words
K = # number of matches we wish to return
R = # size of alphabet

18. Keep TST in memory forever.
Very important!
Build a TST: long time
Search in TST: no more than 1ms

19. Improve user experience
Part 3
Improve user experience
——By Wang Sangtian

20. Improve user experience
Sorry, I type in wrong prefix!
What’s your middle name?
What about abbreviation?
They have same name……
Useless data in database.

21. Sorry, I type in wrong prefix!
Users may type in wrong prefix.
Xinbing Wang xinbing
xinbnig
Wrong
xinbiing

22. Sorry, I type wrong prefix!
Exhaustion method
Exchange adjacent characters
xibnnig
xinnbig
xinbnig
xinbing
xinbngi

23. Sorry, I type wrong prefix!
Exhaustion method
Delete one character
xibiing delete “n”
xiniig delete “b”
xinbiing
xinbing delete “i”
… delete “i”, “n”, “g”

24. What’s your middle name?
Some users don’t know the author’s middle name.
Author’s Full name
User’s input
“Obert P. Castleberry”
“obert cas”
“Brian B. Avants”
“brian ava”

25. What about abbreviation?
Users are more familiar with abbreviations of affiliations.
Affiliation
Abbreviation
Shanghai Jiao Tong University
SJTU
University of California Los Angeles
UCLA

26. They have same name…
There are 16 authors named “Tao Wang” in our database.
tao wang:7D1EAAC6
Name + ID tao wang:7DAE408A
tao wang:7DC34BA9

27. Useless data in database
There are some useless data in database.
Author name
Author ID
Paper count
“Zhang”
81ED2B9C
8059
“Wang”
85AA6692
4512

28. Q & A