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

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

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

CrowdSC: Building Smart Cities with Large Scale Citizen Participation

An elegant way to make cities smarter would be to design a platform where every citizen is given an opportunity to be effectively connected to the governing bodies in their location and to contribute to the general well being. In this paper, we present CrowdSC, an effective crowdsourcing framework designed for smarter cities. We show that it is possible to combine data collection, data selection and data assessment crowdsourcing activities in a crowdsourcing process to achieve sophisticated goals in a predefined context. We propose different strategies for managing this process. We also present an experimental study that evaluate outcomes of the process depending on these execution strategies

CrowdSC: Building Smart Cities with Large-Scale Citizen Participation

International audienceAn elegant way to make cities smarter would be to design a platform where citizens are given an opportunity to be effectively connected to the governing bodies in their location and to contribute to the general well being. In this paper, we present CrowdSC, a crowdsourcing framework designed for smarter cities. We show that it is possible to combine data collection, data selection and data assessment crowdsourcing activities in a crowdsourcing process to achieve sophisticated goals in a predefined context. We show that depending on the executing strategy of this process, different kind of outcomes can be produced. We present an experimental study that evaluate these process outcomes depending on different execution strategie

Answering Complex Location-Based Queries with Crowdsourcing

International audienceCrowdsourcing platforms provide powerful means to execute queries that require some human knowledge, intelligence and experience instead of just automated machine computation, such as image recognition, data filtering and labeling. With the development of mobile devices and the rapid prevalence of smartphones that boosted mobile Internet access, location-based crowdsourcing is quickly becoming ubiquitous, enabling location-based queries assigned to and performed by humans. In sharp contrast of existing location-based crowdsourcing approaches that focus on simple queries, in this paper, we describe a crowdsourcing process model that supports queries including several crowd activities, and can be applied in a variety of location-based crowdsourcing scenarios. We also propose different strategies for managing this crowdsourcing process. Finally, we describe the architecture of our system, and present an experimental study conducted on pseudo-real dataset that evaluates the process outcomes depending on these execution strategies

Measuring the radicalisation risk in social networks

Social networks (SNs) have become a powerful tool for the jihadism as they serve as recruitment assets, live forums, psychological warfare, as well as sharing platforms. SNs enable vulnerable individuals to reach radicalized people, hence triggering their own radicalization process. There are many vulnerability factors linked to socio-economic and demographic conditions that make jihadist militants suitable targets for their radicalization. We focus on these vulnera bility factors, studying, understanding, and identifying them on the Internet. Here, we present a set of radicalization indicators and a model to assess them using a data set of tweets published by several Islamic State of Iraq and Sham sympathizers. Results show that there is a strong correlation between the values assigned by the model to the indicatorsThis work was supported in part by EphemeCH, Spanish Ministry of Economy and Competitivity, under the European Regional Development Fund FEDER, under Grant TIN2014-56494-C4-4-P and in part by the Justice Programme of the European Union (2014-2020) 723180, RiskTrack, under Grant JUST-2015-JCOO-AG and Grant JUST-2015-JCOO-AG-1

Techniques avancées pour l’optimisation de requêtes de services Web

As we move from a Web of data to a Web of services, enhancing the capabilities of the current Web search engines with effective and efficient techniques for Web services retrieval and selection becomes an important issue. In this dissertation, we present a framework that identifies the top-k Web service compositions according to the user fuzzy preferences based on a fuzzification of the Pareto dominance relationship. We also provide a method to improve the diversity of the top-k compositions. An efficient algorithm is proposed for each method. We evaluate our approach through a set of thorough experiments. After that, we consider the problem of Web service selection under multiple users preferences. We introduce a novel concept called majority service skyline for this problem based on the majority rule. This allows users to make a “democratic” decision on which Web services are the most appropriate. We develop a suitable algorithm for computing the majority service skyline. We conduct a set of thorough experiments to evaluate the effectiveness of the majority service skyline and the efficiency of our algorithm. We then propose the notion of α-dominant service skyline based on a fuzzification of Pareto dominance relationship, which allows the inclusion of Web services with a good compromise between QoS parameters, and the exclusion ofWeb services with a bad compromise between QoS parameters. We develop an efficient algorithm based on R-Tree index structure for computing efficiently the α-dominant service skyline. We evaluate the effectiveness of the α-dominant service skyline and the efficiency of the algorithm through a set of experiments. Finally, we consider the uncertainty of the QoS delivered by Web services. We model each uncertain QoS attribute using a possibility distribution, and we introduce the notion of pos-dominant service skyline and the notion of nec-dominant service skyline that facilitates users to select their desired Web services with the presence of uncertainty in their QoS. We then developappropriate algorithms to efficiently compute both the pos-dominant service skyline and nec-dominant service skyline. We conduct extensive sets of experiments to evaluate the proposed service skyline extensions and algorithmsDe nos jours, nous assistons à l’émigration du Web de données vers le Web orienté services. L’amélioration des capacités et fonctionnalités des moteurs actuels de recherche sur le Web, par des techniques efficaces de recherche et de sélection de services, devient de plus en plus importante. Dans cette thèse, dans un premier temps, nous proposons un cadre de composition de services Web en tenant compte des préférences utilisateurs. Le modèle fondé sur la théorie des ensembles flous est utilisé pour représenter les préférences. L’approche proposée est basée sur une version étendue du principe d’optimalité de Pareto. Ainsi, la notion des top-k compositions est introduite pour répondre à des requêtes utilisateurs de nature complexe. Afin d’améliorer la qualité de l’ensemble des compositions retournées, un second filtre est appliqué à cet ensemble en utilisant le critère de diversité. Dans un second temps, nous avons considéré le problème de la sélection des services Web en présence de préférences émanant de plusieurs utilisateurs. Une nouvelle variante, appelée Skyline de services à majorité, du Skyline de services traditionnel est défini. Ce qui permet aux utilisateurs de prendre une décision « démocratique » conduisant aux services les plus appropriés. Un autre type de Skyline de services est également discuté dans cette thèse. Il s’agit d’un Skyline de Services de nature graduelle et se fonde sur une relation de dominance floue. Comme résultat, les services Web présentant un meilleur compromis entre les paramètres QoS sont retenus, alors que les services Web ayant un mauvais compromis entre les QoS sont exclus. Finalement, nous avons aussi absorbé le cas où les QoS décrivant les services Web sont entachés d’incertitude. La théorie des possibilités est utilisée comme modèle de l’incertain. Ainsi, un Skyline de Services possibilité est proposé pour permettre à l’utilisateur de sélectionner les services Web désirés en présence de QoS incertains. De riches expérimentations ont été conduites afin d’évaluer et de valider toutes les approches proposées dans cette thès

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

Top-k Cloud Service Plans Using Trust and QoS

International audienc