Rencontres sur la Recherche en Informatique R 2 I 2011 12-14 Juin 2011 Tizi-Ouzou, Algérie Aicha...

Rencontres sur la Recherche en Informatique R2I 201112-14 Juin 2011

Tizi-Ouzou, Algérie

Empty Answers to Flexible Queries: A Novel Approach

AGGOUNE Aïcha

Presented by:

Aicha AGGOUNE1, Allel HADJALI2 1University of Sétif, Algeria

2IRISA/ENSSAT, Université de Rennes 1, France

Content

Introduction & Problematic1

Flexible queries & EAP2

3Measuring proximity

4 Approaching the EAP

Conclusion & perspectives5

Introduction & Problematic

One of the key elements of the quality of DBMS is the pertinence

of the answers, in particular, by the consideration of the

preferences of the users in the queries.

It was shown that the theory of fuzzy sets provides effective

tools for expressing preferences. In this case, queries are called the

"Flexible queries (or Fuzzy) ".

One of the problems with which the users are confronted, in a

process of interrogation, is the problem of the empty answers

(EAP).

R2I'2011, 12-14 Juin 2011, Tizi Ouzou, Algérie 02/16

Introduction & Problematic

The flexible queries limit to a large extent the risk of obtaining

of the empty answers, however this risk is not totally

eliminated.

?Proposition of a semantic proximity measure between flexible

queries for the treatment of the EAP, this measure uses a

particular distance, called the Hausdorff distance.


Flexible queries & EAP

Generalization of boolean query

Included the vague and imprecise terms

Expression of criteria

importance levels

Expression of preferences

Flexible Query



Gradual Operators (Clearly superior to)

Simples fuzzy predicates (Cheap, Expensive,…)

Fuzzy predicates + modifiers (Very, relatively,…)

Compund fuzzy predicates (Young andWell Paid,…)

Fuzzy predicates + quantifiers

(plus, less, …)

Fuzzy Criteria



Flexible Query Languages :

Extensions of classical interrogations languages .

The result of a flexible query is a fuzzy relation for which

every tuple t is a degree of membership noted / t.

Extension of the relational algebra. Ex: rUs(u)= max(r(u),

s(u))

Three languages of type SQL:

SQLF, SQLF Etendu, FQUERY


Example: (SQLF)

SELECT * FROM VOITURE

WHERE Marque = ‘Famous' AND Prix = ‘Cheap';

Mark Colour Price

BMW vert 30000

AUDI rouge 15100VW blanc 15000

Mark Colour Price Degree Mark Degree Price

BMW vert 30000 0.9 0.2AUDI rouge 15100 0.7 0.7VW blanc 15000 0.5 0.9

Mark Colour Price Degree

BMW vert 30000 0.2

AUDI rouge 15100 0.7

VW blanc 15000 0.5




« The problem of empty answers means that all the answers

does not inform the user about their questions and satisfy

their needs. "

Godfrey

«A query is said an empty response if it produces no result» Motro

Empty Answers Problem : EAP


Hausdorff Distance

The Hausdorff distance is a topological tool to deduct the

similarity between fuzzy sets, it measures the resemblance or

the dissimilarity of two subsets of a metric space.

The Hausdorff distance between two sets A and B is : dH(A, B) = max {supaA infbB d(a, b), supbB infaA d(a, b)}

In the case of a discrete fuzzy set and according to Rosenfeld‘s

approach

Measuring proximity


In the continuous case :

The sementic proximity is: .

The simplest form is :

Ranking approximate

queries is done by descending order of

semantic proximity and number of approximate answers.

Measuring proximity

Hausdorff Distance


Single Predicate Queries:Let Q=P and Q'=P' (P, P' are pertaining to the same attribute A ) :

Compound Queries (more predicates ):Let Q= P1P2…Pk

There are 3 cases: Case 1: Q and Q' cover the same attributes exactly.

Case 2: Q' covers all the attributes specified in Q.

Case 3: Q' does not cover all the attributes specified in Q.

Q'= P'1 P'2… P'k (Pi, Pi' are pertaining to the same attribute Ai)

Case 2: Q' covers all the attributes specified in Q.

Q'=P'1 ... P'kP'k+1… P's.

(S1) Increased Q : Q = P1 ... Pk D(Ak+1 ) … D(As).

(S2) Weakening of Q' : Q'R = P'1 ... P'k .

Case 3: Q' does not cover all the attributes specified in Q.

Q' = P'1 ... P'b P'k+1 … P's with b < k and (Pi, P'i) D(Ai), for i = 1 to b, predicats Pj (for j = k+1 to s) are not specified in Q.

i. Increased Q' : Q' = P'1 ... P'b D(Ab+1)… D(Ak) P'k+1 … P's

ii. Applying one of two strategies described in case 2.

Measuring proximity


Approaching the EAP


Using a set of queries previously evaluated by the system

and whose answers are not

empty (Workload of past queries).

Given the workload, estimating the semantic proximity of

the empty answers query Q whith queries of the workload.

The approximate answer to the query is a set of tuples

ordered by the closest query in query EA.

Principle

Principle

Approaching the EAP

Treatment of EAP

Algorithme TEA

Q

Q

Q'

QappQ

∑Q

∑Qapp


Partitionnons the workload W ( D ) in 03 subsets:

W=(D, Q) = {Q' / Q' W(D) |D(Q') D(Q)| = k},

W> (D, Q) = {Q' / Q' W(D) |D(Q') D(Q)| > k},

W< (D, Q) = {Q' / Q' W(D) |D(Q') D(Q)| < k},

(a)- If W= (D, Q) , for element Q' W=(D, Q), estimate the proximity Prox(Q, Q'). Ranking in descending order of queries.

Choose the closest query Qapp in Q, and affect it as degree of membership, the value of proximity measured Prox (Q, Qapp).

1

2

3

(b)- Else if W >(D, Q) , then we consider W >(D, Q) and apply (a).

Approaching the EAP


Approach

(c)- Else, we consider W<(D, Q) and apply (a).

Conclusion & perspectives


The introduction of flexibility on the elements of

DB, based on the theory of fuzzy sets;

The proposition of a measure of semantic proximity between

flexible queries;

Using this measure to treat the EAP.

The main contributions are:

Conclusion & perspectives


Experiment of the proposed approach.

Proposition a search algorithm for closest queries to EA query.

Analysis of results.

Nevertheless, our approach opens future perspectives. In the

short term, the most important are the following ones:

Compare the proposed approach to existing approaches.

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