generalised fuzzy types and querying.implementation within the hibernate framework

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Motivation

Context

Proposal

Example

Comparison

Conclusions and . . .

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Generalised fuzzy types andquerying.

Implementation within theHibernate Framework.

Jose Enrique Pons Olga Pons CapoteIgnacio Blanco Medina

Department of Computer Science and Artificial IntelligenceUniversity of Granada, Spain{jpons,opc,iblanco}@decsai.ugr.es

October 27, 2011

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1. Contents

The structure of the presentation is:

• Motivation.

• Context:

– Fuzzy relational databases.

– Hibernate Framework.

• Proposal.

• Comparison.

• Conclusions and future work.

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Proposal

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2. Motivation

• A true standard for fuzzy databases does not ex-ist.

• Relational databases are usually the base for theimplementation of fuzzy databases.

• General model to represent and querying fuzzytypes in any relational database.

• Implementation within the Hibernate Frame-work.

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3. Context

• 3.1 Fuzzy relational databases (FBD):

– Representation: fuzzy data types.

– Querying: flexible querying.

• 3.2 Hibernate Framework:

– Architecture.

– Querying in the Hibernate Framework

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3.1. Fuzzy relational databases

Two main features:

• Fuzzy representation”The restaurant X in the database is cheap inaverage.””The restaurant X in the database is a ham-burguer restaurant.”

• Flexible querying:”The user wants to obtain a list with cheaprestaurants.”

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3.1.1. Fuzzy data types

Two main types:

• Ordered underlying domain:

1

016 18 24 26

possibility

Cheap Middle-price Expensive

Price

This data type may be represented as a trapezoidin the form: [α, β, γ, δ].E.g. Middle-price = [16, 18, 24, 26]

• Non-ordered underlying domain:Fast food Hamburger Chinese

Fast food 1 0.8 0.6Hamburger 0.8 1 0.2

Chinese 0.6 0.2 1

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3.1.2. Flexible Querying

The user may query with vagueness in the database:”The user want to obtain a list of cheap restau-rants.”

ID Name AVG Quality001 Amadeus [15,10,20,5] 5002 Atlantis [11,5,17,6] 3003 Cafe Theatre [20,10,30,10] 2004 De Graslei [23,18,28,5] 5005 Pakhuis [13,11,15,2] 3006 De 3 Biggetjes [45,25,65,20] 4

1

016 18 24 26

possibility

Cheap Middle-price Expensive

Price

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The selected restaurants are:

ID Name AVG Quality001 Amadeus [15,10,20,5] 5002 Atlantis [11,5,17,6] 3005 Pakhuis [13,11,15,2] 3

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Operators for fuzzy querying:

Possibility Necessity Possibly / NecessarilyFEQ NFEQ Fuzzy =FGT NFGT Fuzzy >

FGEQ NFGEQ Fuzzy ≥FLT NFLT Fuzzy.<

FLEQ NFLEQ Fuzzy ≤MGT NMGT Much >MLT NMLT Much <

FEQ(p1, p2) = supd∈U

min(πp1(d), πp2(d)) (1)

Where U is the underlying domain, p1, p2 are twovalues of a given fuzzy type (e.g. fuzzy type 2) andπp1(d), πp2(d) are the associated possibility distribu-tions.

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Context

• 3.1 Fuzzy relational databases (FBD):

– Representation: fuzzy data types.

– Querying: flexible querying.

• 3.2 Hibernate Framework:

– Architecture.

– Querying in the Hibernate Framework

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3.2. Hibernate Framework

The Hibernate Framework is a collection of opensource projects that enable developers to makeobject-relational mapping. Features:

• Database independent by means of dialects.

• HQL: an object-oriented query language.

• Open source.

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3.2.1. Architecture

An application working with Hibernate has 3 layers:

1. Application layer: CRUD (CReate,Updateand Delete) operations:

(a) Persistent Objects: A current database state.

(b) Transient Objects: These objects do not rep-resent a current database state.

2. Hibernate layer: An abstraction layer be-tween the DBMS and the application.

3. The database: The running DBMS: MySQL,PostgreSQL, Oracle...etc.

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Hibernate Architecture:

HIBERNATE

XML mapping

Application

Persistent Objects

hibernate.

properties

Database

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3.2.2. Querying in Hibernate

1. Query by criteria:createCriteria(Person.class).Add(Restrictions.eq(”login”,”lmmn”));

2. Query by example:Person p.login = ”lmmn”;

3. HQL:Select p from Person p where p.login =”lmmn”;

4. SQL:Select * from Person as p where p.login =’lmmn’;

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4. Proposal

• Architecture for the general framework for:

– Fuzzy representation.

– Fuzzy querying.

• Implementation within the Hibernate frame-work.

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4.1. Fuzzy Representation

Conditions for portability:

1. The SQL standard is the interface.

2. The fuzzy types are represented in the databaseby basic SQL types.

3. The meta-data for managing properly the ex-tended types relies outside the database catalog.

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4.1.1. Architecture for the general model

The architecture for a generalized object-orientedmodel needs the following elements:

Object-Oriented Layer.

(OORL)

MySQL

Object-Oriented to Relational.

(OO2RL).

O1 ON

R1 RN Relational Layer.

(RRL)

PostgreSQL Oracle

Conversion Layer. (CL)

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4.1.2. Implementation

The model has the following layers:

Application

Fuzzy Data Types Fuzzy Domains Fuzzy

Constraint

ValidatorValues Labels Constraint

Fuzzy Data

Types Adaptor

Fuzzy Domain

Adaptor

Hibernate Core

Database

Type 2 Type 3

Representation

Layer (OORL)

Adaptation

Layer (OO2RRL)

& (RRL)

Conversion

Layer (CL)

F

S

Q

L

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4.2. Fuzzy Querying

The model uses the following elements:

• Declarative implementation for each fuzzyoperator. This implementation should be donein the SQL language.

• Abstract syntax tree (AST) representa-tion for the query. These representation allowsa customization process done by the conversionlayer (CL).

• Conversion Layer: This layer customizes theAST for the running database.

The implementation of the fuzzy querying is done bymodifying the HQL language. The fuzzy operatorsare implemented in a declarative way in the SQLlanguage.

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Query translation:

Select

rfrom

whereRestaurant

r.priceAvg [15,10,20,5]

FEQ

• The framework builds an abstract syntax tree(AST) once the query passed lexical and syntac-tical analysis.

• The AST represents tokens as nodes. The se-mantic analyzer renders the tree in the into SQLsentences. Then the dialect customizes the SQLsentence.

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Example:The user wants restaurants that have an averageprice around 15 euro:

SELECT r FROM Restaurant r WHEREr.PriceAvg FEQ [15, 10, 20, 5];

The FEQ node is rendered to its implementation inSQL:

SELECT * FROM Restaurant as r WHERE

1 < CASE WHEN (r.priceAvg.gamma <= beta2)

OR (r.priceAvg.beta >= gamma2) THEN 0

WHEN (r.priceAvg.alpha = alpha2) THEN 1

WHEN (r.priceAvg.gamma > beta2) AND (r.priceAvg.alpha < alpha2)

THEN (r.priceAvg.gamma - beta2) / ( r.priceAvg.delta - delta2 )

ELSE (gamma2 - r.priceAvg.beta) / ( r.priceAvg.delta + delta2 );

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5. Comparison

Portability among the proposals:

• FSQL server: The reference implementation of the FIRST inter-face on the GEFRED model. The first implementation works withOracle database, although there is an implementation in Post-greSQL.

• SQLfi: The implementation for the SQLf language.

• FDBLL: Fuzzy database language and library . A fuzzy SQLimplementation in C language over a relational DBMS.

• PSQL: An extension of the FSQL model. The main features arethe use of priority fuzzy logic and the portability.

• Hibernate FSQL: The proposed implementation.

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Comparison table:

Fuzzy DB Catalog Interface Query language Query ProcessorFSQL Inside DB. FSQL client FSQL Procedural.SQLfi Inside DB. Client app. SQLf Procedural.FDBLL Inside DB. Client app. Fuzzy SQL Procedural.PFSQL Inside DB. JDBC client PFSQL Procedural.H. FSQL Outside DB. Entity Classes Fuzzy HQL Declarative.

The main differences are:

• The meta data for the fuzzy types are not stored in the database, therefore,to change the running database is as easy as changing some parameters inthe Hibernate configuration file.

• There is no need to create or modify fuzzy meta tables in the DBMS catalog.

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6. Conclusions and FutureWork

• We introduced a general model for the represen-tation of (fuzzy) types and for fuzzy querying.

• The implementation within Hibernate is basedon the portability.

• The drawback for the portability is the depen-dency between the application and the frame-work.

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Future work:

• Fuzzy querying: bipolar specification in thequeries

• Practical applications with the proposed frame-work.

• Fuzzy temporal representation: representationfor Fuzzy Validity Periods and implementationof temporal comparisons within the Allen’s rela-tions.

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Thank you!

Questions?

Contact:

[email protected]

http://decsai.ugr.es/˜ jpons

generalised fuzzy types and querying.implementation within the hibernate framework

Technology

fuzzy sql page

home page context

fuzzy querying

home page idnameavgquality

home page price0

home page ist

post title page gresql

fuzzy database language