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Book ChapterDOI

A cooperative answering approach to fuzzy preferences queries in service discovery

26 Oct 2011-Vol. 7022, pp 318-329

TL;DR: A novel approach for service retrieval that takes into account the service behavior and relies both on preference satisfiability and structural similarity is proposed and a flexible evaluation method based on fuzzy linguistic quantifiers is introduced.

AbstractIn this paper, we propose a novel approach for service retrieval that takes into account the service behavior (described as process model) and relies both on preference satisfiability and structural similarity. User query and target process models are represented as annotated graphs, where user preferences on QoS (Quality of Service) attributes (such as response time, availability and throughput) are modelled by means of fuzzy sets. To avoid empty results, a flexible evaluation method based on fuzzy linguistic quantifiers (such as almost all) is introduced. The retrieved results are easily interpreted by the end users thanks to the clear semantics conveyed by that method. Finally, two families of ranking methods are discussed.

Topics: Fuzzy set (56%), Ranking (information retrieval) (56%), Service discovery (56%), Fuzzy logic (55%), Quality of service (51%)

Summary (2 min read)

1 Introduction

  • Nowadays, an increasing number of companies and organizations are moving towards a service-oriented and model-driven architectures for offering their services on the Web.
  • Most of these approaches are based on the matchmaking of process inputs/outputs [8], service behavior [2] or ontological knowledge [5].
  • Section 6 proposes an illustrative example and finally Section 7 concludes the paper.

2.2 Preferences in Process Model Specification

  • Many languages are currently available to describe service process models, e.g., OWLS [12].
  • They represent a process model as a set of primitive activities combined using control flow structures.
  • A preference can be specified over a process model graph (global preference) or over an atomic activity (activity preference).

3.1 Atomic Preferences

  • For numerical atomic preferences, the satisfiability degree is obtained using to userspecific membership functions.
  • Table 1 summarizes the fuzzy modelling of numerical preferences of interest.
  • For non-numerical preferences, the satisfiability degree is based on the semantic similarity between concepts.

3.2 Complex Preferences

  • Sp, first the authors construct a preference tree tp that represents the semantics of the set Sp.
  • In that tree, the nodes represent atomic preferences and the edges represent a more important than relation (prioritized preference) from parent to child.
  • Preferences of the same level and having the same parent express Pareto preference.
  • This new interpretation of pi considers as acceptable any value outside of its support with the degree 1−ωi.
  • At the end, the authors calculate the satisfiability degree of user atomic preferences considering their constructors and the complex preferences containing them.

4 Process Model Similarity: A Linguistic Quantifier-Based Method

  • The authors describe a method to compute similarity between process model graphs according to user preferences.
  • The authors assume here that max i=1,nwi = 1. similarity between two process model graphs.
  • The edit operations considered are simple graph edit operations: node/edge deletion, node/edge addition and node substitution.
  • In the figure, SS (v, w) denotes the semantic similarity between activities v and w; the authors use the metric proposed in [5] that considers the activity name, inputs and outputs.
  • Such a quantifier, which is seen as a relaxation form of the universal quantifier “all”, constitutes an appropriate tool to avoid the empty answers.

4.1 Preference Satisfiability between Process Models

  • A natural user interpretation of the similarity between query and target process models according to user preferences is given by the truth degree of the following proposition: γ1: Almost all preferences of q are satisfied by t.
  • (i) the user is totally satisfied if at least 80% of preferences are satisfied and (ii) the user is not satisfied at all if at most 50% of preferences are satisfied.

4.2 Structural Similarity between Process Models

  • Similarly, the authors can apply the technique based on fuzzy quantifiers to compute a structural similarity degree between two process models.
  • In their approach, the authors consider particularly formulae (3) and (4) where μQ (i/n) = i/n.
  • Thus, the meaning of delivered degrees has a simple and clear semantics for the user [10].

5 Process Model Ranking

  • Given a set of target graphs that are relevant to a query, the authors discuss some methods to rank-order these graphs according to their structural similarity and preference satisfiability.
  • Similarly, let SS (q, t,M,E) be the structural similarity between q and t obtained by formula (5) w.r.t.
  • Two kinds of ranking methods can be used.
  • Ranking methods aggregate both structural similarity and preference satisfiability into a unique degree used to rank-order the target graphs.
  • Two aggregations can be considered: Weighted average-based aggregation.

6 Illustrative Example

  • First, the authors compute the preference satisfiability degree between q1 and the potential target graphs.
  • The satisfiability degree δ (p2, a2) between preference p2 and annotation a2 is obtained by μmax [reliability].
  • Table 5 summarizes the results of the different ranking methods discussed in Section 5 (where ωSS = 0.75).

7 Conclusion

  • The authors have proposed an approach for web services selection and ranking where both structural similarity and preference satisfiability are taken into account in the evaluation step.
  • User preferences are modelled thanks to fuzzy predicates while linguistic quantifiers are used as a basis to compute the process model similarity.
  • So, the matchmaking process is achieved in a more cooperative and flexible way.
  • Some ranking methods have been discussed in the scope of services retrieval.

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A Cooperative Answering Approach to Fuzzy
Preferences Queries in Service Discovery
Katia Abbaci, Fernando Lemos, Allel Hadjali, Daniela Grigori, Ludovic
Lietard, Daniel Rocacher, Mokrane Bouzeghoub
To cite this version:
Katia Abbaci, Fernando Lemos, Allel Hadjali, Daniela Grigori, Ludovic Lietard, et al.. A Cooperative
Answering Approach to Fuzzy Preferences Queries in Service Discovery. Flexible Query Answering
Systems, 2011, Belgium. pp.318-329. �hal-00656155�

A Cooperative Answering Approach to Fuzzy
Preferences Queries in Service Discovery
Katia Abbaci
1
, Fernando Lemos
2
, Allel Hadjali
1
, Daniela Grigori
2
,
Ludovic Liétard
3
, Daniel Rocacher
1
, and Mokrane Bouzeghoub
2
1
IRISA/ENSSAT, Rue de Kérampont BP 80518 Lannion, France
{katia.abbaci,allel.hadjali,daniel.rocacher}@enssat.fr
2
PRiSM Lab, 45 Av. des États Unis 78000 Versailles, France
{fernando.lemos,daniela.grigori,
mokrane.bouzeghoub}@prism.uvsq.fr
3
IRISA/IUT, Rue Edouard Branly BP 30219 Lannion, France
ludovic.lietard@univ-rennes1.fr
Abstract. In this paper, we propose a novel approach for service retrieval that
takes into account the service behavior (described as process model) and relies
both on preference satisfiability and structural similarity. User query and tar-
get process models are represented as annotated graphs, where user preferences
on QoS (Quality of Service) attributes (such as response time, availability and
throughput) are modelled by means of fuzzy sets. To avoid empty results, a ex-
ible evaluation method based on fuzzy linguistic quantifiers (such as almost all)
is introduced. The retrieved results are easily interpreted by the end users thanks
to the clear semantics conveyed by that method. Finally, two families of ranking
methods are discussed.
Keyw ords: Cooperative answering, service retrieval, quality of services, fuzzy
preferences, linguistic quantifiers.
1 Introduction
Nowadays, an increasing number of companies and organizations are moving towards a
service-oriented and model-driven architectures for offering their services on the Web.
Searching a specific service within service repositories becomes a critical issue for the
success of these architectures. This issue has recently received much attention and many
approaches have been proposed [8,2,5]. Most of these approaches are based on the
matchmaking of process inputs/outputs [8], service behavior [2] or ontological knowl-
edge [5]. Unfortunately, these approaches often result in a large number of services
offering similar functionalities and behavior. One way to discriminate between such
similar services is to consider non-functional requirements such as QoS (Quality of
Service) (e.g., response time, throughput, availability and reliability). A recent trend
towards quality-aware approaches has been initiated [13,1,18], but remains limited and
not satisfactory for generic process model discovery.
On the other hand, several service discovery approaches based on fuzzy set theory
have been proposed. For instance, in [11] the authors treat the web service selection
H. Christiansen et al. (Eds.): FQAS 2011, LNAI 7022, pp. 318–329, 2011.
c
Springer-Verlag Berlin Heidelberg 2011

A Cooperative Answering Approach to Fuzzy Preferences Queries 319
for composition as a fuzzy constraint satisfiability problem. They assign to each QoS
criterion ve fuzzy sets (such as poorly acceptable, almost acceptable and acceptable)
describing its constraint levels. In [15], QoS based service selection is modelled as a
fuzzy multiple criteria decision making problem. Linguistic expressions are used to
evaluate and to express the weights of importance of QoS criteria. Hafeez et al. [6]
present a service selection mechanism allowing the service broker to intelligently se-
lect a set of available services from a user query with imprecise constraints defined by
fuzzy sets. The query evaluation is based on the aggregation of the obtained degrees
over constraints. ¸Sora et al. [1] propose an approach in which they automatically gen-
erate fuzzy rules from user preferences and rank the candidate services using a fuzzy
inference process.
The above fuzzy approaches only consider the preference satisfiability and ignore
the structural similarity of complex web services. Moreover, these works deal only with
services as black boxes, i.e., the service behavior level is not investigated. Our goal is to
go further these approaches into a unique integrated approach dealing with functional
and non-functional requirements and behavior specification in service retrieval.
Starting from the work done in [9], we propose a cooperative approach for handling
users process queries where both behavior specification and QoS preferences are spec-
ified inside these queries. User preferences on QoS properties are modelled by means
of fuzzy sets as they are more suitable to the interpretation of linguistic terms (such as
high or fast) that constitutes a convenient way for users to express their preferences. To
avoid empty answers for a given query, a flexible evaluation strategy based on fuzzy
linguistic quantifiers is introduced.
The remainder of this paper is organized as follows. Section 2 provides some basic
background. In Section 3, modelling fuzzy preferences and their evaluation are ad-
dressed. Section 4 presents our interpretation of process models similarity based on
linguistic quantifiers. In Section 5, service ranking methods are discussed. Section 6
proposes an illustrative example and finally Section 7 concludes the paper.
2 Background
In this section, we provide some basic definitions within the scope of web service se-
lection with preferences, and a short recall on fuzzy sets.
2.1 Fuzzy Sets
A fuzzy set F [17] on the universe X is described by a membership function μ
F
:
X [0, 1] , where μ
F
(x) represents the membership degree of x in F .Theset
{x F |μ
F
(x) > 0} (resp. {x F |μ
F
(x)=1}) represents the support (resp. core)
of F . In practice, the membership function associated to F is often represented by a
trapezoid (α, β, ϕ, ψ)
1
,where[α, ψ] (resp. [β,ϕ]) is its support (resp. core).
A Fuzzy set-based approach to preferences queries proposed in [3] relies on the use
of fuzzy set membership functions that describe the preference profiles of the user on
each attribute domain involved in the query. This is especially convenient and suitable
1
In our case, the quadruplet (α, β, ϕ, ψ) is user-defined to ensure the subjectivity property.

320 K. Abbaci et al.
when dealing with numerical domains, where a continuum of values is to be interfaced
for each domain with satisfiability degrees in the unit interval scale. Then individual
satisfiability degrees associated with elementary conditions are combined (commen-
surability assumption holds thanks to the membership functions) using a panoply of
fuzzy set connectives, which may go beyond conjunctive and disjunctive aggregations
(by possibly involving fuzzy quantifiers, if only the satisfiability of the most of the
elementary conditions in a query is required).
2.2 Preferences in Process Model Specification
Many languages are currently available to describe service process models, e.g., OWL-
S [12]. They represent a process model as a set of primitive activities combined using
control flow structures. Then, these languages can be abstracted as a direct graph G =
(V,E), where the vertices represent activities or control flow nodes, while the edges
represent the flow of execution. In this work, services are specified as graphs annotated
with QoS properties and user queries are specified as graphs annotated with preferences.
Figure 1 shows an example of a user query annotated with preferences. The example
presents a global preference indicating user prefers services providing RSA encryption.
Some activity preferences are also defined for activities A and B involving reliability,
response time and cost. Figure 2 shows an example of a process model annotated with
QoS attributes. The example presents a global annotation indicating the security of the
process model and activity annotations indicating the response time, reliability and cost
of some activities. In what follows, we present the formal definitions of our model.
start
AND
C
AND
end
݌
ǣ݉ܽݔݎ݈ܾ݈݁݅ܽ݅݅ݐݕ
݌
ǣܽݎ݋ݑ݊݀ݎ݁ݏ݌ܶ݅݉݁ǡʹͲ݉ݏ
݌
ǣܾ݁ݐݓ݁݁݊ܿ݋ݏݐǡͳͲǡʹͲ
݌
ǣƬ݌
ǡ݌
݌
ǣƬ݌
ǡ݌
݌
ǣ۪݌
ǡ݌
݌
ǣ݈݅݇݁ݏݏ݁ܿݑݎ݅ݐݕǡܴܵܣ
A
B
݌
ǣ݉ܽݔݎ݈ܾ݈݁݅ܽ݅݅ݐݕ
݌
ǣ݉݅݊ݎ݁ݏ݌ܶ݅݉݁
݌
ଵ଴
ǣƬ݌
ǡ݌
݌
ଵଵ
ǣ݉ܽݔݎ݈ܾ݈݁݅ܽ݅݅ݐݕ
݌
ଵଶ
ǣܾ݁ݐݓ݁݁݊ܿ݋ݏݐǡͺǡͳͷ
݌
ଵଷ
ǣܽݎ݋ݑ݊݀ݎ݁ݏ݌ܶ݅݉݁ǡͳͷ݉ݏ
݌
ଵସ
ǣƬ݌
ଵଶ
ǡ݌
ଵଷ
Fig. 1. Query Graph q
1
start
end
ܽ
ǣሺݎ݁ݏ݌ܶ݅݉݁ǡʹͷ݉ݏሻ
ܽ
ǣሺݎ݈ܾ݈݁݅ܽ݅݅ݐݕǡͻͲΨሻ
AND
AND
B'
ܽ
ǣ ݎ݁ݏ݌ܶ݅݉݁ǡ͵ͷ݉ݏ
ܽ
ǣሺܿ݋ݏݐǡ͹
ܽ
ଵ଴
ǣሺݎ݈ܾ݈݁݅ܽ݅݅ݐݕǡͻͷΨሻ
D'
ܽ
ǣሺݎ݁ݏ݌ܶ݅݉݁ǡʹͷ݉ݏሻ
C'
ܽ
ǣ ݏ݁ܿݑݎ݅ݐݕǡܴܵܣ
ܽ
ǣ ݎ݁ݏ݌ܶ݅݉݁ǡͳͷ݉ݏ
ܽ
ǣ ݎ݈ܾ݈݁݅ܽ݅݅ݐݕǡͺͲΨ
ܽ
ǣ ܿ݋ݏݐǡʹͷΨ
A'
Fig. 2. Target Graph t
1
Definition 1. An annotation is a pair (m, r),wherem is a QoS attribute obtained from
an ontology O and r is a value for m
2
. It can be specified over a process model graph
(global annotation) or over an atomic activity (activity annotation).
2
We abstract from the different units in which a value can be described.

A Cooperative Answering Approach to Fuzzy Preferences Queries 321
Definition 2. A preference is an expression that represents a desire of the user over the
QoS attributes of a process model or activity. It can be of one of the following forms
3
:
atomic preferences:
around ( m, r
desired
around
): for attribute m, this expression favors the value
r
desired
; otherwise, it favors the values close to r
desired
. μ
around
evaluates the
degree to which a value r satisfies r
desired
;
between (m, r
low
,r
up
between
): for attribute m, this expression favors the
values inside the interval [r
low
,r
up
]; otherwise, it favors the values close to the
limits. μ
between
evaluates the degree to which a value r satisfies the interval
[r
low
,r
up
];
max (m, μ
max
): for attribute m, this expression favors the highest value; oth-
erwise, the closest value to the maximum is favored, as example: the maximum
of reliability or availability is equal by default to 100%. μ
max
evaluates the
degree to which a value r satisfies the highest value of m;
min (m, μ
min
): for attribute m, this expression favors the lowest value; oth-
erwise, the closest value to the minimum is favored, as example: the minimum
of response time or cost is equal by default to 0. μ
min
evaluates the degree to
which a value r satisfies the lowest value of m;
likes (m, r
desired
): for attribute m, this expression favors the value r
desired
;
otherwise, any other value is accepted to some extent;
dislikes (m, r
undesired
): for attribute m, this expression favors the values that
are not equal to r
undesired
;otherwise,r
undesired
is accepted to some extent;
complex p references:
Pareto preference (p
i
,p
j
): this expression states that the two preference ex-
pressions p
i
and p
j
are equally important;
prioritized preference &(p
i
,p
j
): this expression states that the preference p
i
is more important than the preference p
j
.
A preference can be specified over a process model graph (global preference) or over
an atomic activity (activity preference).
In [9], this set of preferences has been used to develop a service selection approach
based on QoS where preference satisfiability is computed using to a unique distance
function for all numerical preferences. This way of doing does not take into account the
fact that preferences are context and user-dependent and assumes no commensurability
when combining individual satisfiability degrees.
3 A Fuzzy Model to Evaluate Preferences
In this section, we introduce a fuzzy set-based approach to handle the above set of pref-
erences involved in the annotated graph associated with the user query. In particular, we
propose a metric, called satisfiability degree (δ), that measures how well the annotations
of a target process model satisfy the preferences present in the query.
3
Based on a subset of preference operators of the model by [7] that leads to a partial order.

Citations
More filters

Journal ArticleDOI
01 Apr 2016
TL;DR: This paper proposes a flexible and effective WS selection framework, which gives users an adequate way to express their preferences using linguistic terms, and enhance the WS selection by leveraging their contexts and profiles, and introduces an effective strategy that allows for priority between the two kinds of preferences, for ranking candidate services.
Abstract: Graphical abstractDisplay Omitted HighlightsThis work falls within Web service query optimization and make use of explicit/implicit preference.An appropriate inference mechanism, borrowed from the fuzzy/approximate reasoning field, is used.A priority-based method is introduced to aggregate the elementary similarities.The top-k results are provided with user.A set of experiments is done to show the feasibility and the effectiveness of our proposal. With the development of Web technologies and the increasing usage of Internet, more and more Web Services (WS) are deployed over Internet. Therefore, there will be a large number of candidate services for fulfilling a desired task. In the last decade, several WS selection approaches are proposed to cope with this challenge. In sharp contrast to the existing WS selection approaches that focus only on user-specified preferences, in this paper, we propose a flexible and effective WS selection framework, which gives users an adequate way to express their preferences using linguistic terms, and enhance the WS selection by leveraging their contexts and profiles. The satisfaction of the candidate WS is expressed by an objective score that takes into consideration no only the user-specified preferences, but also additional preferences extracted from both his/her context and profile using fuzzy inference rules, so as to improve the effectiveness of the selection. We then introduce an effective strategy that allows for priority between the two kinds of preferences, for ranking candidate services. Experimental evaluation on a real case study demonstrates the effectiveness of our proposed strategy.

23 citations



Dissertation
24 Sep 2015
TL;DR: This dissertation proposes new models and techniques for the selection and the composition of uncertain data web services, based on well established fuzzy and probabilistic database theories, and extended the web service description standards to represent the outputs' probabilities.
Abstract: In this thesis we focus on the data web services composition problem and study the impact of the uncertainty that may be associated with the output of a service on the service selection and composition processes. This work is motivated by the increasing number of application domains where data web services may return uncertain data, including the e-commerce, scientific data exploration, open web data, etc. We call such services that return uncertain data as uncertain services. In this dissertation, we propose new models and techniques for the selection and the composition of uncertain data web services. Our techniques are based on well established fuzzy and probabilistic database theories and can handle the uncertainty efficiently. First, we proposed a composition model that takes into account the user preferences. In our model, user preferences are modelled as fuzzy constraints, and services are described with fuzzy constraints to better characterize their accessed data. The composition model features also a composition algebra that allows us to rank the returned results based on their relevance to user's preferences. Second, we proposed a probabilistic approach to model the uncertainty of the data returned by uncertain data services. Specifically, we extended the web service description standards (e.g., WSDL) to represent the outputs' probabilities. We also extended the service invocation process to take into account the uncertainty of input data. This extension is based on the possible worlds theory used in the probabilistic databases. We proposed also a set of probability-aware composition operators that are necessary to orchestrate uncertain data services. Since a composition may accept multiple orchestration plans and not all of them compute the correct probabilities of outputs, we defined a set of conditions to check if a plan is safe (i.e., computes the probabilities correctly) or not. We implemented our different techniques and applied them to the real-estate and e-commerce domains. We provide a performance study of our different composition techniques

DissertationDOI
20 Mar 2018
TL;DR: The solution presented in this paper represents an improvement in process mining on the specific context of creation annotated transition system and its use as a statistics generator for the whole modeled process.
Abstract: AMARAL, Claudio Aparecido Lira do. “Attribute selection for process mining on incident management process”. 2018. 136 p. Dissertation (Master of Science) – School of Arts, Sciences and Humanities, University of São Paulo, São Paulo, 2018. The incident management process is the most widely adopted by companies. However, still lacks techniques that can generate precise estimates for the completion time. This work performs a study in a real incident management process, by means of process mining, able to find out the real process model in the form of annotated transition system and propose automated means for selecting attributes that describe it accordingly, in order to generate realistic estimates of the time to conclusion. The resulting strategy of application feature selection techniques filter and wrapper is able to provide generation of more accurate annotated transition systems with some degree of generalization. The solution presented in this paper represents an improvement in process mining on the specific context of creation annotated transition system and its use as a statistics generator for the whole modeled process.

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