DBease: Making Databases User-Friendly and Easily

Accessible

Guoliang Li, Ju Fan, Hao Wu, Jiannan Wang, Jianhua Feng

Database Group, Department of Computer Science and Technology,

Tsinghua University, Beijing 100084, China

How to Access Databases?

• Traditional database-access methods:–SQL

Select title, author, booktitle, year From dblpWhere title Contains “search” And booktitle

Contains “cidr”

–Query-by-exmaple (Form)

–Keyword Search“search cidr”

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cidr

Comparison of Different Methods

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Usa

bil

ity

Too many

results!

Keyword Search• Is traditional keyword search good enough?

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No result!

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Form-based Search

• Form-based Search has the same problem.

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Complicated and

stillno result!

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Our Solution

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Type-Ahead Search

Type-Ahead Search in Forms

SQL SuggestionU

sab

ilit

y

What is Type-Ahead Search?

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Type-Ahead Search

• Advantages– On-the-fly giving users instant feedback– Helping users navigate the underlying

data– Tolerating inconsistencies between query

and data– Supporting Synonyms – Supporting XML data– Supporting Multiple tables

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Problem Formulation

• Data: A set of records• Query

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

– δ: Edit-distance threshold

• Result– A set of records having all query prefixes or their similar

forms (conjunctive)

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Edit Distance:The number of edit operations

(insertion, deletion, substitution)transformed a string to another

ed(string, stang) =2

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Indexing

• Trie Index• Words: root to leaves• Inverted lists on leaves

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Algorithm• Step 1: Find similar prefixes incrementally• Step 2: Retrieve the leaf nodes of similar prefixes • Step 3: Compute union lists of inverted lists of leaf nodes• Step 4: Intersect the union lists of query keywords

=cid r

Type-Ahead Search in Forms

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Type-Ahead Search

Type-Ahead Search in Forms

Usa

bil

ity

What is Type-Ahead Search in Forms?

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Type-Ahead Search in Forms

• Problem Formulation– Data: A relation with multiple attributes– Query: A set of prefixes on attributes in a

form interface – Answers:

• Local results of the focused attribute• Global results of the relation

• Advantages– On-the-fly Faceted Search– Supporting Aggregation

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Data Partition

• Global Table Local Tables

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ID Title Conf. Author

1 xml database

VLDB albert

2 xml database

SIGMOD

bob

3 xml search VLDB albert

4 xml security

VLDB alice

5 rdbms SIGMOD

charlie

ID Conf.

C1 VLDB

C2 SIGMOD

ID Author

A1 albert

A2 bob

A3 alice

A4 charlie

Indexing

• Each attribute– Trie– Mapping Tables

• Local Global• Global Local

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……

Φ

x

m

l

s

e

T1: xml datrabase

T2: xml search

T3: xml security

a c

T1

Trie:1, 2

T2 3

T3 4

T4 5

L-G Mapping Table:

1 T1

2 T1

3 T2

4 T3

G-L Mapping Table:

5 T4

Our Solution

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Author:

Title:

xml databasexml searchxml security

xml database (albert)xml database (bob)xml search (albert)xml security (alice)

……

Φ

x

m

l

s

e

T1: xml datrabase

T2: xml search

T3: xml security

a c

T1

Trie:1, 2

T2 3

T3 4

T4 5

L-G Mapping Table:

1 T1

2 T1

3 T2

4 T3

G-L Mapping Table:

5 T4

Author:

xmlTitle:

albertalice

xml database, albertxml search, albertxml security, aliceal

Our Solution

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l

b

e

r

i

c

5: alice

4: albert

e

T1

Trie

1, 2

T2 3

T3 4

T4 5

L-G Mapping Table

1 T1

2 T1

3 T2

4 T3

G- L Mapping Table

5 T4

a

a

SQL Suggestion

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Type-Ahead Search

Type-Ahead Search in Forms

SQL SuggestionU

sab

ilit

y

What is SQL Suggestion?

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SQL Suggestion

• Problem Formulation– Data: A database with multiple tables– Query: A set of keywords– Answers: Relevant SQL queries

• Advantages– Suggest SQL queries based on keywords– Help users formulate SQL queries to find accurate

results– Designed for both SQL programmers and Internet users– Group answers based on SQL structures– Support Aggregation– Support Range queries

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Our Solution

• Suggest Templates from Keywords– A template is a structure in

the databases– Modeled as a graph

• Nodes: entities (table names or attribute names)

• Edges: foreign keys or membership

• Suggest SQL queries from Templates– Mapping between keywords

and templates

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keyword paper ir(a) Query

(b) Template

(c) SQL

Template Suggestion

• Template Generation– Extension from basic entities

(tables)

• Template Ranking– Template weight

• Pagerank – Relevancy between a keyword

and an entity• Tf*idf

• Algorithms– Fagin algorithms– Threshold-based pruning

techniques

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SQL Suggestion

• SQL suggestion model– Mapping from keywords to templates – Matching is a set of mappings with all

keywords– Weighted set-covering problem (NP-hard)

• SQL ranking– Relevancy between keywords and attributes – Attribute weight

• Algorithms– Greedy algorithms

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Search: dbeasehttp://dbease.cs.tsinghua.edu.cn

Differences to Google Instant Search• Fuzzy prefix matching• Google firstly predicts queries, and

then use the top queries to search the documents. Google may involve false negatives, while we can find the accurate top-k answers.

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Differences to Complete Search• Fuzzy prefix matching• Different index structures• More efficient

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Differences to Keyword Search• Effectiveness

– SQL Suggestion supports range queries, and aggregation functions.

– SQL Suggestion can group answers.– SQL Suggestion can help users to express

their query intent more accurately.

• Efficiency– Faster

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