Showing papers by "Allel Hadjali published in 2011"

Database preference queries--a possibilistic logic approach with symbolic priorities

Abstract: The paper presents a new approach to database preference queries, where preferences are represented in a possibilistic logic manner, using symbolic weights. The symbolic weights may be processed without assessing their precise value, which leaves the freedom for the user to not specify any priority among the preferences. The user may also enforce a (partial) ordering between them, if necessary. The approach can be related to the processing of fuzzy queries whose components are conditionally weighted in terms of importance. In this paper, importance levels are symbolically processed, and refinements of both Pareto ordering and minimum ordering are used. The representational power of the proposed setting is stressed, while the approach is compared with database Best operator-like methods and with the CP-net approach developed in artificial intelligence. The paper also provides a structured and rather broad overview of the different lines of research in the literature dealing with the handling of preferences in database queries.

On the Use of Fuzzy Dominance for Computing Service Skyline Based on QoS

Abstract: Nowadays, the exploding number of functionally similar Web services has led to a new challenge of selecting the most relevant services using quality of service (QoS) aspects. Traditionally, the relevance of a service is determined by computing an overall score that aggregates individual QoS values. Users are required to assign weights to QoS attributes. This is a rather demanding task and an imprecise specification of the weights could result in missing some user desired services. Recent approaches focus on computing service skyline over a set of QoS aspects. This can completely free users from assigning weights to QoS attributes. However, two main drawbacks characterize such approaches. First, the service skyline often privileges services with a bad compromise between different QoS attributes. Second, as the size of the service skyline may be quite large, users will be overwhelmed during the service selection process. In this paper, we introduce a new concept, called alpha-dominant service skyline, to address the above issues and we develop a suitable algorithm for computing it efficiently. Experimental evaluation conducted on synthetically generated datasets, demonstrates both the effectiveness of the introduced concept and the efficiency of the proposed algorithm.

On different types of fuzzy skylines

Abstract: This paper deals with database preference queries based on the skyline paradigm, which aim at retrieving the tuples non Paretodominated by any other. We propose different ways to fuzzify such queries in order to make them more flexible, to increase their discrimination power, to make them more drastic or more tolerant. In particular, some of these extensions make it possible to reduce the risk of getting many incomparable tuples, even when the number of dimensions is high.

Top-k Web Service Compositions Using Fuzzy Dominance Relationship

Abstract: Data Web service composition is a powerful means to answer users' complex queries. User preferences are a key aspect that must be taken into account in the composition scheme. In this paper, we present an approach to automatically compose Data Web services while taking into account the user preferences. User preferences are modeled thanks to fuzzy sets. We use an RDF query rewriting algorithm to determine the relevant services. The fuzzy constraints of the relevant services are matched to those of the query using a set of matching methods. We rank-order services using a justification of Pareto dominance, then compute the top-k service compositions. We propose also a method to improve the diversity of returned compositions while maintaining as possible the compositions with the highest scores. Finally, we present a thorough experimental study of our approach.

On possibilistic skyline queries

Abstract: This paper deals with Skyline queries in the context of possilistic databases, where uncertain attribute values are represented by possibility distributions. In this framework, Skyline queries aim at computing the extent to which any tuple from a given relation is possibly/certainly not dominated by any other tuple from that relation. Beside the interpretation of possibilistic Skyline queries, a basic algorithm suited to their evaluation is provided.

Selecting and ranking business processes with preferences: an approach based on fuzzy sets

Abstract: Current approaches for service discovery are based on semantic knowledge, such as ontologies and service behavior (described as process model). However, these approaches still remain with a high selectivity rate, resulting in a large number of services offering similar functionalities and behavior. One way to improve the selectivity rate and to provide the best suited services is to cope with user preferences defined on quality attributes. In this paper, we propose and evaluate a novel approach for service retrieval that takes into account the service 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 attributes are modelled by means of fuzzy sets. A flexible evaluation strategy based on fuzzy linguistic quantifiers (such as almost all) is introduced. Then, two families of ranking methods are discussed. Finally, an extensive set of experiments based on real data sets is conducted, on one hand, to demonstrate the efficiency and the scalability of our approach, and on the other hand, to analyze the effectiveness and the accuracy of the proposed ranking methods compared to expert evaluation.

FuDoCS: a web service composition system based on fuzzy dominance for preference query answering

Abstract: Modern enterprises are increasingly moving towards a service oriented architecture for data sharing by putting their data sources behind services, thereby providing an interoperable way to interact with their data. This class of services is known as DaaS (Data-as-a-Service) services. DaaS Composition is a powerful solution to answer the user's complex queries by combining primitive DaaS services. User preferences are a key aspect that must be considered in the service composition process. A more general and suitable approach to model preferences is based on fuzzy sets theory [3]. Fuzzy sets are very well suited to the interpretation of linguistic terms and constitute a convenient way for a user to express her/his preferences. For example, when expressing preferences about the price of a car, users often employ fuzzy terms like rather cheap, affordable, etc. However as DaaS services proliferate, a large number of candidate compositions that would use different (most likely competing) services may be used to answer the same query. Hence, it is important to set up an effective service composition framework that would identify and retrieve the most relevant services and return the top-k compositions according to the user preferences.

Top-k service compositions: a fuzzy set-based approach

Abstract: Data as a Service (DaaS) is a flexible way that allows enterprises to expose their data. Composition of DaaS services provides bridges to answer queries. User preferences are becoming increasingly important to personalizing the composition process. In this paper, we propose an approach to compose DaaS services in the context of preference queries where preferences are modeled by means of fuzzy sets that allow for a large variety of flexible terms such as 'cheap', 'affordable' and 'fairly expensive'. The proposed approach is based on RDF-based query rewritings to take into account the partial matching between individual DaaS services and parts of the user query. Matching degrees between DaaS services and fuzzy preference constraints are computed by means of different constraints inclusion methods. Such degrees express to which extent a service is relevant to the resolution of the query. A fuzzification of Pareto dominance is also proposed to better rank composite services by computing the score of services. The resulting scores are then used to compute the top-k DaaS service compositions that cover the user query.

Efficient detection of minimal failing subqueries in a fuzzy querying context

Abstract: This paper deals with conjunctive fuzzy queries that yield an empty or unsatisfactory answer set. We propose a cooperative answering approach which efficiently retrieves the minimal failing subqueries of the initial query (which can then be used to explain the failure). The detection of the minimal failing subqueries relies on a prior step of fuzzy cardinalities computation. The main advantage of this strategy is to imply a single scan of the database. Moreover, the storage of such knowledge about the data distributions easily fits in memory.

A similarity skyline approach for handling graph queries - A preliminary report

Abstract: One of the fundamental problems in graph databases is similarity search for graphs of interest. Existing approaches dealing with this problem rely on a single similarity measure between graph structures. In this paper, we suggest an alternative approach allowing for searching similar graphs to a graph query where similarity between graphs is rather modeled by a vector of scalars than a unique scalar. To this end, we introduce the notion of similarity skyline of a graph query defined by the subset of graphs of the target database that are the most similar to the query in a Pareto sense. The idea is to achieve a d-dimensional comparison between graphs in terms of d local distance (or similarity) measures and to retrieve those graphs that are maximally similar in the sense of the Pareto dominance relation. A diversity-based method for refining the retrieval result is proposed as well.

A fuzzy framework for selecting top-k web service compositions

Abstract: Data Web services allow users to access information provided by different companies. Web users often need to compose different Web services to achieve a more complex task that can not be fulfilled by an individual Web service. In addition, user preferences are becoming increasingly important to personalize the composition process. In this paper, we propose an approach to compose data Web services in the context of preference queries where preferences are modeled thanks to fuzzy sets that allow for a large variety of flexible terms such as "cheap", "affordable" and "fairly expensive". Our main objective is to find the top-k data Web service compositions that better satisfy the user preferences. The proposed approach is based on an RDF query rewriting algorithm to find the relevant data Web services that can contribute to the resolution of a given preference query. The constraints of the relevant data Web services are matched to the preferences involved in the query using a set of matching methods. A ranking criterion based on a fuzzyfication of Pareto dominance is defined in order to better rank the different data Web services/compositions. To select the top-k data Web services/compositions we develop a suitable algorithm that allows eliminating less relevant data Web services before the composition process. Finally, we evaluate our approach through a set of experiments.

A cooperative answering approach to fuzzy preferences queries in service discovery

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 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.

An Approach Based on Fuzzy Sets to Selecting and Ranking Business Processes

Abstract: Current approaches for service discovery are based on semantic knowledge, such as ontologies and service behavior (described as a process model). However, these approaches have high selectivity rate, resulting in a large number of services offering similar functionalities and behavior. One way to improve the selectivity rate is to cope with user preferences defined on quality attributes. In this paper, we propose a novel approach for service retrieval that takes into account the service 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 attributes are modelled by means of fuzzy sets. A flexible evaluation strategy based on fuzzy linguistic quantifiers is introduced. Finally, different ranking methods are discussed.

On database queries involving competitive conditional preferences

Abstract: This paper introduces a new type of database queries involving preferences. The idea is to consider competitive conditional preference clauses structured as a tree, of the type “preferably P1 or ⋅⋅⋅ or Pn; if P1 then preferably P1,1 or …; if P2 then preferably P2,1 or …,” where the Pis are not exclusive (thus the notion of competition). The paper defines two possible interpretations of such queries and outlines two evaluation techniques which follow from them. © 2010 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.

Processing fuzzy queries in a peer data management system using distributed fuzzy summaries

Abstract: In this paper, we consider the situation where a fuzzy query is submitted to distributed data sources. In order to save bandwith and processing cost, we propose an approach whose aim is to forward the query to the most relevant sources only. An efficient fuzzy-cardinality-based technique for summarizing each data source is described. The approach we propose consists in estimating the relevance of a source with respect to a user query, based on its associated summary. Some experiments illustrate the efficiency of the approach.

Selecting and Ranking Business Process with Preferences: An Approach Based on Fuzzy Set

Abstract: Current approaches for service discovery are based on semantic knowledge, such as ontologies and service behavior (described as process model). However, these approaches still remain with a high selectivity rate, resulting in a large number of services offering similar functionalities and behaviour. One way to improve the selectivity rate and to provide the best suited services is to cope with user preferences defined on quality attributes. In this paper, we propose and evaluate a novel approach for service retrieval that takes into account the service 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 attributes are modelled by means of fuzzy sets. A fexible evaluation strategy based on fuzzy linguistic quantifiers (such as almost all) is introduced. Then, two families of ranking methods are discussed. Finally, an extensive set of experiments based on real data sets is conducted, on the one hand, to demonstrate the efficiency and the scalability of our approach, and on the other hand, to analyze the effectiveness and the accuracy of the proposed ranking methods compared to expert evaluation.

Top-k web services compositions: A fuzzy-set-based approach

Abstract: Data as a Service (DaaS) is a flexible way that allows enter- prises to expose their data. Composition of DaaS services provides bridges to answer queries. User preferences are becoming increasingly important to personalizing the com- position process. In this paper, we propose an approach to compose DaaS services in the context of preference queries where preferences are modeled by means of fuzzy sets that allow for a large variety of flexible terms such as 'cheap', 'af- fordable' and 'fairly expensive'. The proposed approach is based on RDF-based query rewritings to take into account the partial matching between individual DaaS services and parts of the user query. Matching degrees between DaaS services and fuzzy preference constraints are computed by means of different constraints inclusion methods. Such de- grees express to which extent a service is relevant to the resolution of the query. A fuzzification of Pareto dominance is also proposed to better rank composite services by com- puting the score of services. The resulting scores are then used to compute the top-k DaaS service compositions that cover the user query.

On database queries involving inferred fuzzy predicates

Abstract: This paper deals with database preference queries involving fuzzy conditions which do not explicitly refer to an attribute from the database, but whose meaning is rather inferred from a set of rules. The approach we propose, which is based on some concepts from the fuzzy control domain (aggregation and defuzzification, in particular), significantly increases the expressivity of fuzzy query languages inasmuch as it allows for new types of predicates. An implementation strategy involving a coupling between a DBMS and a fuzzy reasoner is outlined.

A fuzzy-rule-based approach to the handling of inferred fuzzy predicates in database queries

Abstract: This paper deals with database preference queries involving fuzzy conditions which do not explicitly refer to an attribute from the database, but whose meaning is rather inferred from a set of fuzzy rules. The approach we propose, which is based on the fuzzy inference pattern called generalized modus ponens, significantly increases the expressivity of fuzzy query languages inasmuch as it allows for new types of predicates. An implementation strategy involving a coupling between a DBMS and an inference engine is outlined.

An Approach Based on Fuzzy Sets to Handle Preferences in Service Retrieval

Abstract: Current approaches for service discovery are based on semantic knowledge, such as ontologies and service behavior (described as a process model). However, these approaches have high selectivity rate, resulting in a large number of services offering similar functionalities and behavior. One way to improve the selectivity rate is to cope with user preferences defined on quality attributes. In this paper, we propose a novel approach for service retrieval that takes into account the service 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 attributes are modelled by means of fuzzy sets. A flexible evaluation strategy based on fuzzy linguistic quantifiers is introduced. Finally, different ranking methods are discussed.

Searching for a compromise between satisfaction and diversity in database fuzzy querying

Abstract: This paper deals with fuzzy queries and describes an approach that aims at providing users with a set of answers which satisfies a diversity criterion on one or several attributes. Different cases are considered and two types of algorithms are described. The first one, which has a linear complexity in terms of the number of tuples in the result, is suited to the case where the notion of similarity underlying the definition of diversity is crisp. The second one, based on a trial and error strategy, makes it possible to deal with fuzzy similarity, but its high complexity means that it can be employed only when a relatively small sets of tuples is used to increase diversity.

Une Approche Skyline pour l'Interrogation de Bases de Données de Graphes

Abstract: La recherche de graphes similaires a une requete a graphe est l'un des problemes fondamentaux des bases de donnees de graphes. Les approches exis- tantes traitant ce probleme s'appuient, generalement, sur une seule mesure de similarite entre les structures de graphes. Dans cet article, nous proposons une approche permettant de rechercher les graphes similaires au graphe d'une re- quete ou la similarite entre graphes n'est plus un scalaire unique mais un vecteur de scalaires. Pour cela, nous introduisons le concept de skyline par similarite d'une requete a graphe defini par un sous-ensemble de graphes, de la base de donnees interrogee, qui sont les plus similaires a la requete au sens de Pareto. Une methode pour raffiner le resultat de la recherche est aussi proposee en s'ap- puyant sur le critere de diversite entre les graphes.

A Framework Recommending Top-k Web Service Compositions: A Fuzzy Set-Based Approach

Abstract: Data Web services allow users to access information provided by different companies. Web users often need to compose different Web services to achieve a more complex task that can not be fulfilled by an individual Web service. In addition, user preferences are becoming increasingly important to personalize the composition process. In this paper, we propose an approach to compose data Web services in the context of preference queries where preferences are modelled thanks to fuzzy sets that allow for a large variety of flexible terms such as "cheap", "affordable" and "fairly expensive". Our main objective is to find the top-k data Web service compositions that better satisfy the user preferences. The proposed approach is based on an RDF query rewriting algorithm to find the relevant data Web services that can contribute to the resolution of a given preference query. The constraints of the relevant data Web services are matched to the preferences involved in the query using a set of matching methods. A ranking criterion based on a fuzzyfication of Pareto dominance is defined in order to better rank the different data Web services/compositions. To select the top-k data Web services/compositions we develop a suitable algorithm that allows eliminating less relevant data Web services before the composition process. Finally, we evaluate our approach through a set of experiments.