Efficient Type-Ahead Search on Relational Data: a TASTIER Approach

Guoliang Li1, Shengyue Ji2, Chen Li2, Jianhua Feng1

1 Tsinghua University, Beijing, China2 University of California, Irvine, CA, USA

Tsinghua & UC IrvineEfficient Type-Ahead Search on Relational DataGuoliang Li, Shengyue Ji, Chen Li, Jianhua Feng

Traditional Keyword Search

MUST Type in Complete keywords

Type-Ahead Search

Advantages: Interactive: data

exploration in relational databases

Full-text search: full-text search on-the-fly

Challenges and Preliminaries Efficiency requirement (milliseconds vs.

seconds) Client-side processing Network delay Server-side processing

Opportunities: Subsequent queries can be answered

incrementally

Fundamentals Data

R: a relational database with a set of tables D: a set of distinct words tokenized from the

data in R

Fundamentals Query

Q = {p1, p2, …, pl}: a set of prefixes Query result

RQ: a set of subtrees (called Steiner trees) such that each subtree has all query prefixes, i.e., a set of relevant tuples connected through foreign keys such that each answer has all query prefixes (conjunctive)

Traditional Keyword Search Data Graph

database search sigmod sigir signature

Query: {database search sigmod} Answers:Steiner trees(radius r)

a2 a3 a5

Type-Ahead Search Data Graph

database search sigmod sigir signature

Query: {database search sig} Answer:Steiner trees(radius r)

a2 a3 a5

Type-Ahead Search in Relational Data Step 1

Incremental prefix matching Step 2

Incrementally find relevant connected tuples that contain query prefixes

Contributions Efficiently Finding answers using -step forward

index Improving search efficiency

graph partition query prediction

Step 1: Incremental Prefix Matching Example

D = {sigmod, search, spark, yu, graph}

Q = “graph s” Ws={sigmod, search, spark}

Q’ = “graph sig” Wsig={sigmod}

Tire Index

Incremental Prefix Matching sigmod, search, spark, yu, graph

graph search

sigmod

Step 2: Finding answers graph

How to efficiently find answers?

Contributions Step 1

Efficiently Finding answers using -step forward index

Improving search efficiency graph partition query prediction

-step forward index

Search

Finding answers using -step forward index

Improving search efficiency graph partition query prediction

Graph Partition

Step 1 Find subgraphs that contain query prefixes

Step 2 Find answers within subgraphs

Graph Partition

Q= “Graph Yu” Step 1: find subgraphs S2, S3 Step 2: find answers within S2, S3

High-Quality Graph Partition

A: S1,S2

B: S1,S2

C: S1,S2

S1 S2 S3

D: S1,S2

E: S1,S2

F: S1,S2

E: S3,S4

F: S3,S4

Advantages:1. Shorten List2. Subgraph Pruning

Keyword-Sensitive Partition Graph Hypergraph

G(V, E) Gh(Vh,Eh) Vh=V if (u,v) E, then (u,v) Eh , if u1, u2, …, un contain a same keyword, then (u1, u2, …, un ) Eh

Hypergraph PartitionB

improving search efficiency graph partition query prediction

Query Prediction

Previous Method vs. Query Prediction Previous method

Find all potential compute words of query prefixes and compute corresponding answers

e.g., {sigmod, sigir, signature, …,} for sig Query prediction

Predict the complete keywords with maximal probabilities and compute corresponding answers using the predicted keywords

E.g., predict 2 best keyword {sigmod, sigir} for sig

Query Prediction Query-prediction model

Bayesin network Pr(ki) = #of occurrences of ki/ # of nodes Pr(ki|kj, kn) = Pr(ki|kn)

Query Prediction

Q=“keyword s”

keyword search Q=“keyword search r”keyword search relation

Experimental Results Setting

C++, Gnu compiler, FastCGI, Ubuntu, X5450 3.0GHz CPU, 3GB RAM

Datasets DBLP IMDB

Search Efficiency

Scalability: Index Size

Scalability: Search Time

Questions?

Thank You!Questions?

http://tastier.ics.uci.edu/ http://tastier.cs.tsinghua.edu.cn/Search: tastier type-ahead search

Efficient Type-Ahead Search on Relational Data: a TASTIER Approach

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