Supporting Efficient Top-k Queries in Type-A h ead Search

Supporting Efficient Top-k Queries in Type-Ahead

SearchGuoliang Li1, Jiannan Wang1, Chen Li2,

Jianhua Feng1

1 Tsinghua University2 UC Irvine, Bimaple Technology Inc.

SIGIR 2012, Portland, Oregon

Tsinghua/UC Irvine/Bimaple 2

Query suggestions

Li, Wang, Li, and Feng


Type-ahead search (instant search)


Finding answers instantly!


ipubmed.ics.uci.edu


Fuzzy search


Advantages of instant fuzzy search


Save time

Fat fingers!

Mobile friendly

Correct errors


Challenges Speed

“100ms rule” Prefix matching Fuzzy matching


Quality


Techniques for computing top-k answers in instant fuzzy search

without generating all candidates


Contributions

Ranking framework Index Structures Algorithms Experimental evaluation


Outline

Problem Formulation Instant exact search Instant fuzzy search Experiments



Problem Formulation Data: records Query:

w1, w2, …, wm wm partial keyword

Answers: k best records


graph icde li

ID Recordr0 graph icdmr1 graph group luir2 gray icde liur3 graph icde lin luir4 graph group icdm lin

liur5 graph gray gross icdm

lin liur6 gray group icdm lin liur7 gray gross group icde

linr8 gross icde liur9 icdm liu

Prefix


Aggregate

Ranking Framework


graph, gray, gross, icde, lin, liu

Record

Query graph

icde

li

Score(graph) Score(icde) Score(lin)Score(liu)

Max


Trie

Index structures

gr

a

i lcd

e mo

p yh

s ups

i uin u


ID Recordr0 graph icdmr1 graph group luir2 gray icde liur3 graph icde lin luir4 graph group icdm lin

liur5 graph gray gross

icdm lin liur6 gray group icdm lin

liur7 gray gross group icde

linr8 gross icde liur9 icdm liu

Inverted Index


{graph, icde, li} k=1

Basic Solution

gr

a

i lcd

e mo

p yh

s ups

i uin u

graph icdelin

liu


Too many candidates


Optimization 1: Heap-based MethodAggregate

Max Heap

𝑟 ,9𝑟5 ,8

graphicde

linliu

GetMax()



Optimization 2: Top-k List-Merging Algorithm

Example: Threshold algorithm


T = 15

= 17= 14= 12= 12

Random Access

Sorted Access

Sorted Access

Early termination


Efficient Random Access: How?ID “grap

h”“icde” “li”

7 0 ?

gr

a

i lcd

e mo

p yh

s ups

i uin u

ID Recordr0 graph icdmr1 graph group luir2 gray icde liur3 graph icde lin luir4 graph group icdm

lin liu… …



Forward index [Ji et al. WWW’09]ID “grap


7 0 ?

ID Forward listr0 <1, 2> <5, 3>r1 <1, 3> <1, 9> <9, 6>r2 <2, 9> <5, 2> <8, 3>r3 <1, 4> <5, 2> <7, 9> <9,

4>r4 <1, 7> <4, 3> <6, 9> <7,

2> <8, 7>… …Keyword IDWeight


gr

a

i lcd

e mo

p yh

s ups

i uin u

12

3 45 6

7 8 9[1, 1][1, 2]

[3,3] [4, 4]

[3, 4]

[1, 4]

[1, 4]

[2,2][5, 6][5, 6][5, 6] [7, 8]

[7, 9]

[9, 9]


gr

a

i lcd

e mo

p yh

s ups

i uin u

12

3 45 6

7 8 9[1, 1][1, 2]

[3,3] [4, 4]

[3, 4]

[1, 4]

[1, 4]

[2,2][5, 6][5, 6][5, 6] [7, 8]

[7, 9]

[9, 9]

ID Forward listr0 <1, 2> <5, 3>

r1 <1, 3> <1, 9> <9, 6>

r2 <2, 9> <5, 2> <8, 3>

r3 <1, 4> <5, 2> <7, 9> <9, 4>

r4 <1, 7> <4, 3> <6, 9> <7, 2> <8, 7>

… …

Random Access Using Forward IndexID “grap


7 07?



Outline




Ranking Framework (Fuzzy matching)


Aggregate

graph, gray, icdm, gross, lin, liu

Record

Query graph

icde

li

Score(graph) Sim(icde,icdm)*Score(icdm) Score(lin)Score(liu)

MaxSim(li,i)*Score(lin)


{graph, icde, li}, similarity threshold τ=0.45

Computing Similar Prefixes [Ji et al. WWW’09]

gr

a

i lcd

e mo

p yh

s ups

i uin u



Top-k Algorithm

icdeicdm lin

liu

lui

Max Heap

𝑟3 ,9𝑟5 ,8 𝑟 4 ,4.5

3 2

similarity

×0.5 ×1 ×1 ×0.5×0.5

icdeicdm

×0.5×1

𝑟 4 ,4.54 Max Heap

𝑟5 ,9Max Heap

GetMax()

sum

×1

graph icde li

graph

GetMax() GetMax()



Probing on Forward Lists

Efficient Random Access (method 1)

ID “graph”

“icde” “li”

7 9 ?

ID Forward listr0 <1, 2> <5, 3>r1 <1, 3> <1, 9> <9, 6>r2 <2, 9> <5, 2> <8, 3>r3 <1, 4> <5, 2> <7, 9> <9,

4>r4 <1, 7> <4, 3> <6, 9> <7,

2> <8, 7>… …

Binary Search: [5,6], [7,9], [7,8], [9,9], 7, 8, 9


gr

a

i lcd

e mo

p yh

s ups

i uin u

12

3 45 6

7 8 9[1, 1][1, 2]

[3,3] [4, 4]

[3, 4]

[1, 4]

[1, 4]

[2,2][5, 6][5, 6][5, 6] [7, 8]

[7, 9]

[9, 9]


Efficient Random Access (method 2) Probing on Trie Leaf Nodes

ID “graph”

“icde” “li”

7 9 ?

gr

a

i lcd

l mo

p yh

s ups

i uin u

12

3 4

5 67 8 9[1,1]

[1,2][3,3] [4,4]

[3,4]

[1,4][1,4]

[2,2][5,6][5,6][5,6] [7,8]

[7,9]

[9,9]ID Forward listr0 <1, 2> <5, 3>

r1 <1, 3> <1, 9> <9, 6>

r2 <2, 9> <5, 2> <8, 3>

r3 <1, 4> <5, 2> <7, 9> <9, 4>

r4 <1, 7> <4, 3> <6, 9> <7, 2> <8, 7>

… …

li, 0.5 li, 0.5

li, 1li, 1

li, 0.5

Traverse the forward list of Li, Wang, Li, and Feng


Optimization by materializing union lists

gr

a

i lcd

e mo

p yh

s ups

i uin u

Time/space tradeoff Cost-based analysis for a space budget



Outline




Data sets and index costs



Exact Search (DBLP)

k=10, similarity threshold τ=0.6Li, Wang, Li, and Feng


Exact Search (DBLP)

k=10, similarity threshold τ=0.6Li, Wang, Li, and Feng


Fuzzy Search

DBLP, k=10, similarity threshold τ=0.6Li, Wang, Li, and Feng

TA

NRA


Other results (not included in the paper)

More general ranking (e.g., positional information)

Other languages Location-based search



Conclusions (ipubmed.ics.uci.edu)

Efficient techniques for instant fuzzy search



Acknowledgements The authors have financial interest in Bimaple

Technology Inc., a company currently commercializing some of the techniques described in this publication.

Chen Li was partially supported by NIH grant 1R21LM010143-01A1.

Guoliang Li, Jianan Wang, and Jianhua Feng were partly supported by the National Natural Science Foundation of China under Grant No. 61003004, the National Grand Fundamental Research 973 Program of China under Grant No. 2011CB302206, a project of Tsinghua University under Grant No. 20111081073, and the “NExT Research Center” funded by MDA, Singapore, under the Grant No. WBS:R-252-300-001-490.


Documents

Supporting Efficient Top-k Queries in Type-A h ead Search