1. Supporting of search-as-you-type using sql in databases
Guoliang Li, Chen Li

2. introduction
A search as you type system computes answer on the fly as user types in keyword query character by character
Finds the solution for both multi-keyword queries and single keyword queries
Finds how to support search as you type in relational database using SQL
Example:is Netflix

3. contents Introduction Preliminaries Problem formulation3
contents
Introduction
Preliminaries
Problem formulation
Exact search for single keyword
Fuzzy search for single keyword
Supporting multi keyword queries
Supporting first N queries
Supporting updates efficiently
Related work
conclusion

4. preliminaries
Let T be the relational table
A1,A2,…….,Al are the attributes
R={r1,r2…..,rn} is collection of records
ri[A j]-denotes content of the record ri in the attribute Aj
W is the set of tokenized keywords in R

5. r1 Relational table(T) r2 r3 r4 r5 r6 r7 ID Title Authors Book title
Year r1
K-Auto morphism: A General Framework for Privacy Preserving Network
Publication
Lei Zou, Lei Chen, M. Tamer O¨ zsu
PVLDB
2009 r2
Privacy-Preserving Singular Value Decomposition
Shuguo Han, Wee Keong Ng, Philip S. Yu
ICDE r3
Privacy Preservation of Aggregates in Hidden Databases: Why and How?
Arjun Dasgupta, Nan Zhang, Gautam Das, Surajit Chaudhuri
SIGMOD r4
Privacy-preserving Indexing of Documents on the Network
Mayank Bawa, Roberto J. Bayardo, Rakesh Agrawal,
Jaideep Vaidya
VLDBJ r5
On Anti-Corruption Privacy Preserving Publication
Yufei Tao, Xiaokui Xiao, Jiexing Li, Donghui Zhang
2008 r6
Preservation of Proximity Privacy in Publishing Numerical Sensitive Data
Jiexing Li, Yufei Tao, Xiaokui Xiao r7
Hiding in the Crowd: Privacy Preservation on Evolving Streams through
Correlation Tracking
Feifei Li, Jimeng Sun, Spiros Papadimitriou, George A.
Mihaila, Ioana Stanoi
SIGIR
2007

6. Search as you type for single keyword queries6
Search as you type for single keyword queries
Exact search: as user types a single partial key word w character by character,search as you type system finds the record that contain keyword with a prefix w
It is also known as prefix search
eg: if user types in a query “sig”, the system returns
Returns records r3,r6 and r7

7. search as you type for single keyword queries
Fuzzy search: As user types in a single partial keyword w character by character ,system finds records with keywords similar to the query keyword
Eg:when we types in query “corel” ,record r7 is relevant answer since it contain keyword”correlation”
Edit distance measure is used for finding similarity between strings
ed(s1,s2)

8. Search as you type for multi-keyword queries
Exact search: given a multi-keyword query Q with m keywords w1,w2,…wm
Wm is considered as partial keyword and others are complete keyword
Fuzzy search:finds the record that contains keyword similar to the complete keyword and a keyword with a prefix similar to the partial keyword w
Wm is the partial keyword
Using the edit distance

9. Different approaches for search as you type
1.Use separate application layer
It can achieve high performance
2.Use database extenders
not safe method to query engine
Depends on API of specific database
3.Use SQL
More compatible since it is using standard SQL
More portable to different platforms than other method

10. Exact search for single keyword
No index methods
Index based methods
Issues a SQL query that scans each records and verifies whether the record is an answer to the query
1.Calling user defined functions (UDF)
Add functions to the database
2.Using LIKE predicate
LIKE predicate allow users to perform string matching
LIKE predicate is used to check whether a record contain the query keyword

11. Index based method Building auxiliary tables as index structures
Inverted index table
Prefix table
Inverted- index table
Inverted Index table (IT)- records are in the form<kid,rid>
Assigning unique id to the keywords in Table(T)
Kid-id of the keyword
Rid-is the id of the record

12. Prefix table
Build prefix table with records in the form<p,lkid,ukid>
P-prefix of the keyword
lkid-smallest id of the keyword with prefix p
Ukid-largest id of the keyword with prefix p
Prefix table can be used to find the range of keyword with the prefix
Eg:the ids of the keyword with prefix “sig” must be
In the range [k6,k7]
<“sig”,k6,k7>

13. Steps for exact search For given partial keyword w
Get the keyword range [lkid,ukid] using prefix table
Find the records that have a keyword in the range through the inverted index table IT
Then use the following SQL to answer the prefix- search query w
SELECT T .* FROM PT , IT , T
WHERE PT .prefix = “w” AND
PT .ukid ≥ IT .kid AND PT .lkid ≤ IT .kid AND
IT .rid = T.rid.

14. The inverted-index table and prefix table.
lkid
ukid ic k1 k2 p k3 k6 pr k4
pri K4 pu k5 pv K6
pvl
kid
keyword k1
icde K2
icdt K3
preserving K4
privacy K5
publishing K6
sigmoid k7
sigir
kid
rid K2
r10 K5 r6 r8 k6 r1 K7 r9 k8 r3

15. fuzzy search for single keyword
No index methods
Using UDF(user defined function) for supporting prefix search
LIKE predicate does not support fuzzy search
PED (w,s)-returns minimal edit distance between w and the prefix of the keyword in s
Eg:PED(“pvb”,r10[title])=PED(“pvb”, “privacy in database publishing”)=1
Uses the edit distance threshold and forms new UDF
PEDTH(w,s,t) if edit distance is within t then UDF returns true

16. Index based method
Using the inverted index table and prefix table to support search as you type
Find the similar prefixes from the prefix table(PT)
Finds the keyword ranges of similar prefixes
Finds the answer based on the keyword ranges using inverted index table
Method to finding similar prefixes
Using UDF
Gram-based method
Neighborhood generation method

17. Using UDF Using a UDF to find similar prefixes
Issues an SQL query which scans each prefix in PT and calls the UDF to check if the prefix is similar to w
Gram based method
q-gram based methods are used for string matching
For a string “s” its q-grams are substrings with length q

18. Neighborhood generation method
i-deletion neighborhood- for a given keyword w, the substring of w by deleting ”i” characters is called i-deletion neighborhood
Di(w) denotes i-deletion neighborhood
For eg: for a keyword “pvldb”
D0(pvldb)={pvldb}
D1(pvldb)={vldb,pldb,pvdb,pvld,pvld}

19. Supporting multi keyword queries
Computing answer from scratch
Q –multi keyword query with m keyword w1,w2,..wm
Using an “INTERSECT” operator
first find the record for each keyword
use the “INTERSECT”operator to join the records
2. Using full text indexes
first find the records matching the first m-1 complete keyword
Find the record matching last prefix keyword
Join the result
Use of CONTAINS command

20. Word level incremental computation
User types query Q with keyword w1,w2..wm
Temporary table CQ is created to cache the record ids of the query Q
if user types new keyword and submit a new query Q1 ,temporary table is used to incrementally answer the query
two types of word level incremental computation
Exact search
Fuzzy search

21. Supporting first n queries
As user types in a query character by character system gives the first-N results as instant feedback
Exact first-N queries
“LIMIT “Syntax is used which returns first N results from the database
E.g.:”LIMIT n1,n2” which returns n2 rows starting from n1th row
Fuzzy first-N queries
Fuzzy search for single keyword query w, find the result with edit distance=0
If there is N answer, terminate the execution otherwise progressively increase the edit distance threshold and select the record with edit distance threshold 1,2,..t until get the N answer

22. Supporting updates efficiently
Data updates includes deleting and insertion of records
Insertion
When a record is inserted then first assign it a new record id
Insert each keyword in the inverted index table
If prefix is not in the prefix table add prefixes to the prefix table
Deletion
If a record is deleted inverted index table use a bit to denote whether the record is deleted
We can use the deleted prefix ids for future insertions

23. Related work
1.Auto completion and search as you type
An auto completion system can predict a word or phrase that a user may type in next based on partial string the user has already typed
2.Approximate string search and similarity join
In this method, Given a set of String and query string,all string in the set are similar to the query string
3.Keyword search in database

24. conclusion
Need to leverage the existing DBMS functionalities to meet the high performance requirement achieve
an interactive speed
To support prefix matching,there is proposed solutions that use auxiliary tables as index structures
Extended the techniques in the case of fuzzy queries
Proposed incremental computation techniques to answer multi-keyword queries
Studied how support first N queries

25. references
[1] S. Agrawal, K. Chakrabarti, S. Chaudhuri, and V. Ganti. Scalable ad-hoc
entity extraction from text collections. PVLDB, 1(1):945–957, 2008.
[2] S. Agrawal, S. Chaudhuri, and G. Das. Dbxplorer: A system for
keyword-based search over relational databases. In ICDE, pages 5–16,2002.

26. thank you