Showing papers by "Allel Hadjali published in 2012"

Selecting Skyline Web Services from Uncertain QoS

Abstract: Quality of service (QoS) has been considered as a significant criterion for selecting among functionally similar Web services. Recent approaches focus on computing the skyline over a set of QoS attributes. This can completely free users from assigning weights to QoS attributes. However, these approaches are not sufficient in a dynamic Web service environment where the delivered QoS by a Web service is inherently uncertain. In this paper, we tackle the problem of skyline on uncertain QoS. We represent each QoS attribute of a Web service using a possibility distribution and introduce two skyline extensions on uncertain QoS called pos-dominant skyline and nec-dominant skyline. We then develop appropriate algorithms to efficiently compute both the pos-dominant skyline and nec-dominant skyline. Finally, we present our experimental results that show both the effectiveness of the introduced skyline extensions and the efficiency of the proposed algorithms.

WS-Sky: An Efficient and Flexible Framework for QoS-Aware Web Service Selection

Abstract: With the development of Service oriented Computing (SOC), more and more functionally similar Web services are deployed over the Web. Quality of service (QoS) aspects (e.g., availability, response time, etc.) are thus crucial for selecting among functionally similar Web services. Moreover, the skyline has been considered as an important concept for selecting Web service based on QoS. In this paper, we propose WS-Sky, a Web service selection framework based on QoS. Our framework leverages two variants of the notion of skyline to effectively and efficiently select Web services that better fit the user needs. Our experimental evaluation on both real-world and synthetic datasets demonstrates that WS-Sky assists users to find the most relevant Web services in a flexible way, and allows users to control the size of the retrieved Web services.

Selecting Skyline Web Services for Multiple Users Preferences

Abstract: In this paper, we introduce a novel concept called collective skyline to deal with the problem of multiple users preferences. We then conduct a set of experiments that demonstrate the effectiveness of the introduced concept

Majority-Rule-Based web service selection

Abstract: In many Web service selection scenarios, the responsibility to decide which is the appropriate service is shared among multiple parties, e.g., among the department heads of a university. The standard approach is to discard services which are unanimously inappropriate, and return the rest. However, as the involved parties may have conflicting interests, it is possible that only few services are eliminated, and thus almost all discovered services need to be considered. This work addresses this shortcoming, by enforcing the majority rule: a service is discarded if the majority of the parties find it inappropriate. We formulate the majority-rule-based service selection problem based on the notions of dominance and skyline. Furthermore, we propose an algorithm that returns a more manageable set of services, eliminating many inappropriate ones, and is more efficient that standard skyline techniques.

Answering fuzzy preference queries over data web services

Abstract: This paper describes a system that supports preference query answering over a set of data Web services. The proposed system is capable to rank-order the query results in the presence of fuzzy preferences. To do so, we provide different software components organized into two main modules. The first module provides the top-k service compositions. It is mainly based on (i) query rewriting techniques to generate relevant services and compositions, (ii) fuzzy dominance relationship to rank both individual and composite services. The second module adopts a fuzzy database approach to provide a graded service composition execution engine ranking returned data results.

A preference-aware query model for data web services

Abstract: Data Mashup is a special class of applications mashup that combines data elements from multiple data sources (that are often exported as data web services) to respond to transient business needs on the fly. In this paper, we propose a semantic model for data services along with a declarative approach for creating data mashups without any programming involved. Given a query formulated over domain ontologies, and a set of preferences modeled using the fuzzy set theory, our approach selects the relevant data services based on their semantic modeling using an RDF query rewriting algorithm. Selected services are then orchestrated using a ranking-aware algebra to rank their returned results based on user's preferences at the data mashup execution time.

A Bipolar Approach to the Handling of User Preferences in Business Processes Retrieval

Abstract: In the context of retrieving business processes, most approaches still lead to a high selectivity rate, resulting in a large number of services offering similar functionalities and behavior As these services may offer different Quality of Services (QoS) values, taking into account user preferences on QoS attributes allow to retrieve the most appropriate services In this paper, we advocate an approach for handling preferences on QoS parameters in a bipolar way, ie, distinguishing between negative and positive preferences as it seems to be the case in the human mind Negative preferences represent constraints that have to be satisfied by the returned services, while positive preferences express wishes and are not compulsory Finally, a set of experiments based on real data is conducted to demonstrate the relevance and the scalability of our proposal

An Approach Based on Predicate Correlation to the Reduction of Plethoric Answer Sets

Abstract: Seeking data from large-scale databases often leads to a plethoric answer problem. A possible approach to reduce the set of retrieved items and to make it more manageable is to constrain the initial query with additional predicates. The approach presented in this paper relies on the identification of correlation links between predicates related to attributes of the relation of interest. Thus, the initial query is strengthened by additional predicates that are semantically close to the user-specified ones.

A Fuzzy-Rule-Based Model for Handling Contextual Preference Queries

Abstract: Users’ preferences have traditionally been exploited in query personalization to better serve their information needs. Most of the time, user preferences depend on context, that is, they may have different values depending on the situation of the user. In this paper, we propose a fuzzy-rule-based model for the representation of contextual preferences in a database querying framework. We discuss the augmentation of a query with preferences deduced from information regarding the current context of the user. To this end, we make use of an appropriate inference pattern, called generalized modus ponens, able to deal with data and knowledge when they are described in a fuzzy way.

Intégration de préférences dans la découverte et la sélection des services web

Abstract: Les methodes actuelles pour la decouverte de services basees sur le comportement – decrit comme un processus metier (PM) – retournent encore un grand nombre de services offrant des fonctionnalites similaires. Dans cet article, nous proposons une strategie d’evaluation pour la recherche de services basee sur les quantificateurs linguistiques flous et qui prend en compte la similarite structurelle entre les PM de services et la satisfaction des preferences sur les attributs de qualite de services (QdS). La requete utilisateur et les PM cibles sont representes a l’aide de graphes annotes ; les preferences de QdS sont modelisees au moyen des ensembles flous.