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

International audienceData 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

Elastic-PPQ: A two-level autonomic system for spatial preference query processing over dynamic data streams

Paradigms like Internet of Things and the most recent Internet of Everything are shifting the attention towards systems able to process unbounded sequences of items in the form of data streams. In the real world, data streams may be highly variable, exhibiting burstiness in the arrival rate and non-stationarities such as trends and cyclic behaviors. Furthermore, input items may be not ordered according to timestamps. This raises the complexity of stream processing systems, which must support elastic resource management and autonomic QoS control through sophisticated strategies and run-time mechanisms. In this paper we present Elastic-PPQ, a system for processing spatial preference queries over dynamic data streams. The key aspect of the system design is the existence of two adaptation levels handling workload variations at different time-scales. To address fast time-scale variations we design a fine regulatory mechanism of load balancing supported by a control-theoretic approach. The logic of the second adaptation level, targeting slower time-scale variations, is incorporated in a Fuzzy Logic Controller that makes scale in/out decisions of the system parallelism degree. The approach has been successfully evaluated under synthetic and real-world datasets

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

International audienceData 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

Energy-Efficient β

As the first priority of query processing in wireless sensor networks is to save the limited energy of sensor nodes and in many sensing applications a part of skyline result is enough for the user’s requirement, calculating the exact skyline is not energy-efficient relatively. Therefore, a new approximate skyline query, β-approximate skyline query which is limited by a guaranteed error bound, is proposed in this paper. With an objective to reduce the communication cost in evaluating β-approximate skyline queries, we also propose an energy-efficient processing algorithm using mapping and filtering strategies, named Actual Approximate Skyline (AAS). And more than that, an extended algorithm named Hypothetical Approximate Skyline (HAS) which replaces the real tuples with the hypothetical ones is proposed to further reduce the communication cost. Extensive experiments on synthetic data have demonstrated the efficiency and effectiveness of our proposed approaches with various experimental settings

A New Manufacturing Service Selection and Composition Method Using Improved Flower Pollination Algorithm

With an increasing number of manufacturing services, the means by which to select and compose these manufacturing services have become a challenging problem. It can be regarded as a multiobjective optimization problem that involves a variety of conflicting quality of service (QoS) attributes. In this study, a multiobjective optimization model of manufacturing service composition is presented that is based on QoS and an environmental index. Next, the skyline operator is applied to reduce the solution space. And then a new method called improved Flower Pollination Algorithm (FPA) is proposed for solving the problem of manufacturing service selection and composition. The improved FPA enhances the performance of basic FPA by combining the latter with crossover and mutation operators of the Differential Evolution (DE) algorithm. Finally, a case study is conducted to compare the proposed method with other evolutionary algorithms, including the Genetic Algorithm, DE, basic FPA, and extended FPA. The experimental results reveal that the proposed method performs best at solving the problem of manufacturing service selection and composition

Call Limit-Based Composite Service Selection

International audienceAPIs allow companies to export, via the Internet, their skills and know-how, or even to open up new markets and new media for sale. But to fully exploit the advantages of these services, customers, mainly developers, must be equipped with tools giving the possibility of being able to assemble different services together. Fortunately, the notion of service composition is quite advanced, and different tools exist to compose services. However, as APIs with similar functionality are expected to be provided by competing providers, the key challenge is to find the most relevant compositions. This issue has been addressed in the context of QoS-based composite service selection. The downside, in practice, customers choose services based on the number of call limits. In this paper, we propose an approach to select the most relevant compositions based on the notion of call limit. Specifically, we show how the call limits of the individual services can be aggregated to obtain the call limits of a given composition. Then, we introduce the notion of minimal budget skyline, which comprises the most interesting compositions that fit within the customer's budget. In addition, we develop two algorithms, based on effective pruning strategies, to efficiently compute the minimal budget skyline. Finally, we present a thorough experimental evaluation of our approach

Modeling and Selection of Software Service Variants

Providers and consumers have to deal with variants, meaning alternative instances of a service?s design, implementation, deployment, or operation, when developing or delivering software services. This work presents service feature modeling to deal with associated challenges, comprising a language to represent software service variants and a set of methods for modeling and subsequent variant selection. This work?s evaluation includes a POC implementation and two real-life use cases

Methods and algorithms for service selection and recommendation (preference and aggregation based)

In order for service users to get the best service that meets their requirements, they prefer to personalize their non-functional attributes, such as reliability and price. However, the personalization makes it challenging because service providers have to deal with conflicting non-functional attributes when selecting services for users. In addition, users may sometimes want to explicitly specify their trade-offs among non-functional attributes to make their preferences known to service providers. Typically, users\u27 service search requests with conflicting non-functional attributes may result in a ranked list of services that partially meet their needs. When this happens, it is natural for users to submit other similar requests, with varying preferences on non-functional attributes, in an attempt to find services that fully meet their needs. This situation produces a challenge for the users to choose an optimal service based on their preferences, from the multiple ranked lists that partially satisfy their request. Existing memory-based collaborative filtering (CF) service recommendation methods that employ this recommendation technique usually depend on non-functional attribute values obtained at service invocation to compute the similarity between users or items, and also to predict missing non-functional attributes. However, this approach is not sufficient because the non-functional attribute values of invoked services may not necessarily satisfy their personalized preferences. The main contributions of this work are threefold. First, a novel service selection method, which is based on fuzzy logic, that considers users\u27 personalized preferences and their trade-offs on non-functional attributes during service selection is presented. Second, a method that aggregates multiple ranked lists of services into a single aggregated ranked list, where top ranked services are selected for the user is also presented. Two algorithms were proposed: 1) Rank Aggregation for Complete Lists (RACoL), that aggregates complete ranked lists and 2) Rank Aggregation for Incomplete Lists (RAIL) to aggregate incomplete ranked lists. Finally, a CF-based service recommendation method that considers users\u27 personalized preference on non-functional attributes if proposed. Examples using real-world services are presented to evaluate the proposed methods and experiments are carried out to validate their performance --Abstract, page iii

Toward a More Accurate Web Service Selection Using Modified Interval DEA Models with Undesirable Outputs

With the growing number of Web services on the internet, there is a challenge to select the best Web service which can offer more quality-of-service (QoS) values at the lowest price. Another challenge is the uncertainty of QoS values over time due to the unpredictable nature of the internet. In this paper, we modify the interval data envelopment analysis (DEA) models [Wang, Greatbanks and Yang (2005)] for QoS-aware Web service selection considering the uncertainty of QoS attributes in the presence of desirable and undesirable factors. We conduct a set of experiments using a synthesized dataset to show the capabilities of the proposed models. The experimental results show that the correlation between the proposed models and the interval DEA models is significant. Also, the proposed models provide almost robust results and represent more stable behavior than the interval DEA models against QoS variations. Finally, we demonstrate the usefulness of the proposed models for QoS-aware Web service composition. Experimental results indicate that the proposed models significantly improve the fitness of the resultant compositions when they filter out unsatisfactory candidate services for each abstract service in the preprocessing phase. These models help users to select the best possible cloud service considering the dynamic internet environment and they help service providers to improve their Web services in the marke

Contribution à l'interrogation flexible et personnalisée d'objets complexes modélisés par des graphes

Plusieurs domaines d'application traitent des objets et des données complexes dont la structure et la sémantique de leurs composants sont des informations importantes pour leur manipulation et leur exploitation. La structure de graphe a été bien souvent adoptée, comme modèles de représentation, dans ces domaines. Elle permet de véhiculer un maximum d'informations, liées à la structure, la sémantique et au comportement de ces objets, nécessaires pour assurer une meilleure représentation et une manipulation e cace. Ainsi, lors d'une comparaison entre deux objets complexes, l'opération d'appariement est appliquée entre les graphes les modélisant. Nous nous sommes intéressés dans cette thèse à l'appariement approximatif qui permet de sélectionner les graphes les plus similaires au graphe d'une requête. L'objectif de notre travail est de contribuer à l'interrogation exible et personnalisée d'objets complexes modélisés sous forme de graphes pour identi er les graphes les plus pertinents aux besoins de l'utilisateur, exprimés d'une manière partielle ou imprécise. Dans un premier temps, nous avons proposé un cadre de sélection de services Web modélisés sous forme de graphes qui permet (i) d'améliorer le processus d'appariement en intégrant les préférences des utilisateurs et l'aspect structurel des graphes comparés, et (ii) de retourner les services les plus pertinents. Une deuxième méthode d'évaluation de requêtes de recherche de graphes par similarité a également été présentée pour calculer le skyline de graphes d'une requête utilisateur en tenant compte de plusieurs mesures de distance de graphes. En n, des approches de ra nement ont été dé nies pour réduire la taille, souvent importante, du skyline. Elles ont pour but d'identi er et d'ordonner les points skyline qui répondent le mieux à la requête de l'utilisateur.Several application domains deal with complex objects whose structure and semantics of their components are crucial for their handling. For this, graph structure has been adopted, as a model of representation, in these areas to capture a maximum of information, related to the structure, semantics and behavior of such objects, necessary for e ective representation and processing. Thus, when comparing two complex objects, a matching technique is applied between their graph structures. In this thesis, we are interested in approximate matching techniques which constitute suitable tools to automatically nd and select the most similar graphs to user graph query. The aim of our work is to develop methods to personalized and exible querying of repositories of complex objects modeled thanks to graphs and then to return the graphs results that t best the users needs, often expressed partially and in an imprecise way. In a rst time, we propose a exible approach for Web service retrieval that relies both on preference satis ability and structural similarity between process model graphs. This approach allows (i) to improve the matching process by integrating user preferences and the graph structural aspect, and (ii) to return the most relevant services. A second method for evaluating graph similarity queries is also presented. It retrieves graph similarity skyline of a user query by considering a vector of several graph distance measures instead of a single measure. Thus, graphs which are maximally similar to graph query are returned in an ordered way. Finally, re nement methods have been developed to reduce the size of the skyline when it is of a signi cant size. They aim to identify and order skyline points that match best the user query.RENNES1-Bibl. électronique (352382106) / SudocSudocFranceF