ABSTRACT Context‐awareness is increasingly recognised as a fundamental principle in the development of ubiquitous computing and ambient intelligence. By leveraging contextual data about users and their environments, systems can gain a deeper understanding of the evolving user situation. This empowers them to dynamically adapt their operations, leading to optimised resource utilisation, enhanced decision‐making, and ultimately, greater user satisfaction. However, a critical challenge lies in effectively representing user situations with a high degree of expressiveness. While ontology‐based data models have emerged as a promising approach due to their ability to handle the inherent heterogeneity of context information, existing ontologies have limitations in terms of information coverage, data heterogeneity and uncertainties consideration, and reusability across various application domains. This paper addresses these limitations by proposing uCSN, an ontology that builds upon and extends the Data Privacy Vocabulary (DPV), Semantic Sensor Network (SSN) and W3C Uncertainty ontologies, to provide a rich and expressive vocabulary for representing diverse user situations. We evaluate uCSN based on its consistency, accuracy, clarity and performance.

The Internet of Things (IoT) has connected the physical and digital worlds, resulting in a pervasive and self-organized network. Yet, due to the configurable model's behavioral constraints, ambiguity emerges during the design stage while selecting the most relevant composition plan. The constraints placed on the composition plan model restrict its behavior, leading to a single desired composition plan while eliminating the undesired ones. In this paper, we offer an entropy-based uncertainty metric to assess the predictability and efficiency of IoT object composition plans. Our method assesses the degree of uncertainty associated with a specific composition plan and provides insights into optimizing the performance of the IoT system.

As a class of context-aware systems, context-aware service recommendation (CASR) aims to bind high-quality services to users, w.r.t. their context requirements (e.g., invocation time, location, social profiles, connectivity). However, current CASR lacks a rich context modelling and does not allow for multi-relational interactions between users and services in different contexts. We propose a context-sensitive service recommendation, by constructing a contextual service knowledge graph (C-SKG), which we translated into a low-dimensional vector space to facilitate its processing. Dilated Recurrent Neural Networks are applied to allow a context-aware C-SKG embedding, based on the principles of subgraph-aware proximity. A recommendation algorithm, finally, returns the top-rated services w.r.t. the target user’s context and the proximity degrees.

Indexing techniques are essential for efficiently storing and retrieving big data. Traditional indexing methods may not be suitable for uncertain environments due to their reliance on complete and accurate data. In such cases, probabilistic indexing techniques can be employed. These techniques assign probabilities to different values or attributes of the data, allowing for efficient retrieval even in the presence of uncertainty. Examples of probabilistic indexing techniques include inverted indexes with probabilistic weights and Bayesian networks. Handling uncertainty is a critical aspect of indexing big data in an uncertain environment. Our main objective in this paper is to propose a new method of semantic indexing of data in an uncertain environment. Basically, our proposed method is composed of two main parts: (i) a first part consists in processing the uncertain data in a textual document and, (ii) the second part consists in determining a new model of uncertain semantic indexing.We developed a series of experiments to evaluate our proposed solution by comparing it to the approaches studied in the literature. The results found showed that our solution is more efficient.

Summary In the context of Internet of Things, thingsourcing is poised to promote the collective behavior that should prevail among things despite their independent nature being confined into silos. By analogy with crowdsourcing where crowds of (sometimes anonymous) people are contacted for their expertise, thingsourcing abstracts crowds of things and provides the necessary mechanisms for composing things together so they collectively satisfy users' demands. However, to ensure successful discovery of things according to these demands' requirements, an ontology‐based semantic description of things is deemed necessary. This article presents an approach for enriching things' descriptions semantically in preparation for their composition with respect to specific scripts that define who will do what, when, and where. A system demonstrating semantic thingsourcing along with a case study about dairy supply‐chain is presented in this article as well.

Summary In the era of Internet Technology (IT), uncertainty management is a challenge in many fields. These include e‐commerce, social and sensor networks, scientific data production and mining, object tracking, data integration, geo‐located services, and recently Internet and Web of Things. Due to the uncertain data published on the web, web resources are diverse. Hence, identical resources could be available from heterogeneous platforms and heterogeneous resources could represent the same objects. These resources are hugely heterogeneous, conflict, inconsistent, or have incompatible formats. This uncertainty is inherently related to many facts, such as information extraction and integration. Hence, with web resources proliferation on the web, referencing through the uncertain web has become increasingly difficult. The traditional techniques used for the classical web could not handle uncertain navigation. Generally, it's implicitly represented, decided randomly, or even neglected. Harnessing these uncertain resources to their full potential in order to handle the uncertain navigation, raises major challenges that relate to each phase of their life cycle: creation, representation, and navigation. In this article, we establish a probabilistic approach to model and interpret uncertain web resources. We present operators to compute response uncertainty. Finally, we create algorithms in order to validate resources and achieve uncertain hypertext navigation.

Over two decades, context awareness has been incorporated into recommender systems in order to provide, not only the top-rated items to consumers but also the ones that are suitable to the user context. As a class of context-aware systems, context-aware service recommendation (CASR) aims to bind high-quality services to users, while taking into account their context requirements, including invocation time, location, social profiles, connectivity, and so on. However, current CASR approaches are not scalable with the huge amount of service data (QoS and context information, users reviews and feedbacks). In addition, they lack a rich representation of contextual information, as they adopt a simple matrix view. Moreover, current CASR approaches adopt the traditional user-service relation and they do not allow for multi-relational interactions between users and services in different contexts. To offer a scalable and context-sensitive service recommendation with great analysis and learning capabilities, we provide a rich and multi-relational representation of the CASR knowledge, based on the concept of knowledge graph. The constructedcontext-aware service knowledge graph(C-SKG) is, then, transformed into a low-dimensional vector space to facilitate its processing. For this purpose, we adopt Dilated Recurrent Neural Networks to propose a context-aware knowledge graph embedding, based on the principles of first-order and subgraph-aware proximity. Finally, a recommendation algorithm is defined to deliver the top-rated services according to the target user's context. Experiments have proved the accuracy and scalability of our solution, compared to state-of-the-art CASR approaches.

In today’s highly connected cyber-physical environments, users are becoming more and more concerned about their privacy and ask for more involvement in the control of their data. However, achieving effective involvement of users requires improving their privacy decision-making. This can be achieved by: (i) raising their awareness regarding the direct and indirect privacy risks they accept to take when sharing data with consumers; (ii) helping them in optimizing their privacy protection decisions to meet their privacy requirements while maximizing data utility. In this article, we address the second goal by proposing a user-centric multi-objective approach for context-aware privacy management in connected environments, denoted δ- Risk . Our approach features a new privacy risk quantification model to dynamically calculate and select the best protection strategies for the user based on her preferences and contexts. Computed strategies are optimal in that they seek to closely satisfy user requirements and preferences while maximizing data utility and minimizing the cost of protection. We implemented our proposed approach and evaluated its performance and effectiveness in various scenarios. The results show that δ- Risk delivers scalability and low-complexity in time and space. Besides, it handles privacy reasoning in real-time, making it able to support the user in various contexts, including ephemeral ones. It also provides the user with at least one best strategy per context.

In this article, we present a privacy-preserving technique for user-centric multi-release graphs. Our technique consists of sequentially releasing anonymized versions of these graphs under Blowfish Privacy. To do so, we introduce a graph model that is augmented with a time dimension and sampled at discrete time steps. We show that the direct application of state-of-the-art privacy-preserving Differential Private techniques is weak against background knowledge attacker models. We present different scenarios where randomizing separate releases independently is vulnerable to correlation attacks. Our method is inspired by Differential Privacy (DP) and its extension Blowfish Privacy (BP). To validate it, we show its effectiveness as well as its utility by experimental simulations.

Service selection is a challenging task, and a lot of effort has been devoted to tools that assist the user in choosing the service whose non-functional parameters better match her/his preferences. In many practical situations, the responsibility to decide which is the appropriate service is shared among multiple parties. A standard approach to this service selection problem is to discard services that are unanimously considered inappropriate and focus on the rest. However, as the involved parties may have colliding interests, only a few services may be eliminated. This work addresses this shortcoming and enables users to reach a “democratic” decision by means of a majority vote: a service is eliminated if the majority of the parties find it inappropriate. We formulate the problem using dominance relationships, and propose algorithms that return an appropriate subset of services for the parties, while being more efficient than standard techniques. Moreover, we consider the problem of defining an appropriate ranking for the non eliminated services, and formulate it as an instance of a group recommendation problem. Finally, we demonstrate the effectiveness and the efficiency of our approach through extensive experimental evaluation on real-based and synthetic datasets.

Microservices have emerged as an alternative solution to many existing technologies allowing to break monolithic applications into “small” fine-grained, highly-cohesive, and loosely-coupled units. However, identifying microservices remains a challenge that could undermine this migration success. This paper proposes an approach for microservices automatic-identification from a set of business processes (BP). The approach is multi-models combining different independent models that represent a BP's control dependencies, data dependencies, semantic dependencies, respectively. the approach is also based on collaborative clustering. A case study about renting bikes is adopted to illustrate and demonstrate the approach. In term of precision, the results show how BPs as inputs permit to generate better microservices compared to other approaches discussed in the paper, as well.

This paper presents thingsourcing to enable thing collaboration in the context of IoT. Compared to crowdsourcing that refers to a crowd of persons, there is limited research in thingsourcing which deprives things from participating in complex business applications. In this paper, thingsourcing is associated with a platform that acts as an IoT marketplace where things sign-up and sign-off looking for opportunities to complete users' demands. The platform also has a set of mechanisms that allow to describe, search for, and “glue” things together. For demonstration purposes a car service center is used illustrating how things like service bays and vehicles collaborate in compliance with scripts defined in ComPOS (Composition language for Palcom Oblivious Services).

Summary This paper stresses out the struggle of organizations when managing multiple variants of the same business process. Each variant constitutes a response to structural and/or functional needs that overtime become unsustainable due to the multiplicity and complexity of these needs. To mitigate this struggle, a knowledge‐based approach for capturing these versions into a single configurable business process and splitting this process into fragments is discussed in the paper. The approach builds a configuration knowledge base to track the business process variability (ie, particularities of each business process variant). Variants are represented as a new configurable process structure tree resulting from fragmenting a business process. Implementation of the approach is, also, reported in this paper.

In modern information systems different information features, about the same individual, are often collected and managed by autonomous data collection services that may have different privacy policies. Answering many end-users' legitimate queries requires the integration of data from multiple such services. However, data integration is often hindered by the lack of a trusted entity, often called a mediator, with which the services can share their data and delegate the enforcement of their privacy policies. In this article, we propose a flexible privacy-preserving data integration approach for answering data integration queries without the need for a trusted mediator. In our approach, services are allowed to enforce their privacy policies locally. The mediator is considered to be untrusted, and only has access to encrypted information to allow it to link data subjects across the different services. Services, by virtue of a new privacy requirement, dubbed k-Protection, limiting privacy leaks, cannot infer information about the data held by each other. End-users, in turn, have access to privacy-sanitized data only. We evaluated our approach using an example and a real dataset from the healthcare application domain. The results are promising from both the privacy preservation and the performance perspectives.

Detecting radicalization on social networks is crucial to the fight against violent extremism and terrorism. In most cases, online radicalization has clear warning indicators that can be detected at the early stages of the radicalization process. In this paper, we focus on mining radicalization indicators from online messages by exploiting structured domain knowledge. More precisely, we propose an approach to automatically annotate social messages with concepts from a domain ontology. Annotations are then exploited within an inference phase to identify the messages exhibiting a radicalization indicator. We conducted a set of experiments on a sample extracted from a public dataset that contains radicalized individuals along with their social messages (i.e. Tweets). Obtained results show the effectiveness of our approach compared to a baseline method.

Real-time bidding (RTB) has recently become the predominant technique in online advertising. Although, RTB is very effective, compared to classical approaches, a lot can be done to improve the accuracy of display advertising. In fact, a major drawback of existing RTB systems is the use of the bidding price and the current user profile as the primary features to display advertising. However, in doing so, the user may not always get the appropriate ad, and the same ad may be presented several times to the same user, which leads to its frustration. To overcome this limitation, we propose in this paper, an approach to serve the right ad to the right user at the right time. Our approach consists of incorporating the notion of recommender systems into the RTB architecture. Specifically, we design a two-layer approach. The first layer implements the item-based collaboratif filtering technique, while the second layer implements the factorization machines model. This allows to capture and use the information of other users (more specifically, those who are similar to the current user) to enhance the accuracy of display advertising. We show how these two layers collaborate to reach our goal, and validate our approach through an experimental study.

In conjunction with the rapid growth and adoption of social media, people are more and more willing to share their personal experiences and opinions about products and/or services with the community. Opinions could be the basis of developing systems that would advise future users on how to proceed with any purchase without risking any disappointment. Unfortunately, opinions are not always genuine due to for instance, biased users as well as mixed feedback coming from the same users (i.e., multi-identity). This paper presents an approach for opinion analysis using credibility as a decisive criterion for supporting future users make sound decisions. The effectiveness of this approach has been tested using opinions posted on Twitter.

We conduct an experimental study of four algorithms for the Weighted Graph Matching Problem (WGMP) for directed graphs. The first algorithm is based on Umeyama's Eigen-decomposition approach and provides nearly an optimum solution by means of Hermitian matrices deduced from the adjacency matrices of pairs of directed weighted graphs. The second algorithm is based on Almohamad's Symmetric ploynomial transform approach. The Symmetric polynomial transform is applied to map input data representing polynomial roots into a set of coefficients that are invariant under permutation of the roots. Assuming this transformation, the weights of two nodes can be compared one to one via their resulted invariant coefficients. The third algorithm is inspired from Almohamad's Linear programming approach where the WGMP is initially formulated in a non linear problem and then transformed into a Linear one formulated in L1 norm. In the two first algorithms, the optimum match is given by the Hungarian method when a permutation exists between each two nodes. The fourth algorithm is an improved version of the first one that gives exact results only for graphs satisfying certain conditions.

In this paper we present our ongoing work to build an approach to empower users of IoT-based cyber physical systems to protect their privacy by themselves. Our approach allows users to identify the privacy risks involved in sharing private data with a data consumer, assess the value of their private data based on identified risks and take a pragmatic data sharing decision balancing the risks with the benefits generated by the sharing. Our approach features a knowledgebase, called the Privacy Oracle, that exploits the power of the Semantic Web to determine how raw metadata can be combined by data consumers to infer privacy-sensitive information as well as the privacy risks associated with the disclosure of inferred information.

This paper presents a Configurable Cloud Service Discovery and Selection System (C2SDS2) that aims to guide Cloud users in retrieving configurations of IaaS Cloud resources over the Internet. The C2SDS2 takes into account both user functional and non-functional requirements in the retrieval and selection process. In this work, configurable services are designed as directed Cloud extended feature graphs inspired by graph structures and feature models. The discovery-based matching is performed in two steps. In the first step, the structural matching is performed by adapting two heuristics: (1) Hungarian and (2) VG (Volgenant-Jonker) which is an improved Hungarian algorithm. In the second step, the QoS matching and ranking are achieved using three different methods of directed weighted graph matching namely the Eigen-decomposition, the Symmetric polynomial transform and the Linear programming methods. We show the efficiency and effectiveness of our system through an experimental study conducted on a configurable IaaS services collection. The experiment results show the performance and the efficiency of the algorithms combinations.

Nowadays, due to the growth of technology, there is a mass production of data (of large volume), available in a digital form. These currently available data are not unified but appear in different formats and types. The diversity of the data is based on the type of information, they contain, such as text, image, video and audio documents and also on their sources, such as data from sensors (high variety). In addition, with the expansion of the Internet and the World Wide Web, the majority of these data become the publicity available for a wide range of users (at high speed). The main objective of this work is to propose an efficient Big Uncertainty Web Data Services Indexing Model able to reasoning in uncertain data environment. More concretely, the proposed approach is based on two main phases: the first one consists on processing uncertain data in the syntactic indexing phase and the second one consists on the semantic indexing phase. These two phases are presented as two algorithms syntactic and semantic.

A configurable Business Process (BP) is an abstract BP that engineers customize with respect to specific requirements. To keep track of the multiple and recurrent customizations that lead to a set of derived BPs, this paper proposes a knowledge-based approach that uses a new Process Structure Tree called configurable PST (cPST). A cPST abstracts a separate variability option of the configurable BP. All cPSTs should be equivalent to the set of all PSTs associated with the derived BPs that could originate from the same configurable BP. This paper also proposes a logic-based configuration model for capturing configuration details on the cBP and describing the cPST computing.

The multi-tenancy aware discovery of configurable Cloud services is one of the most important and difficult issues, because of multiplicity and non-standardization of their description in the Cloud. In this paper, relying on a feature model based specification of configurable WSDL services, we develop a multi-tenancy aware approach for their discovery. Our approach empowers multiple tenants to discover their desired service configured variants, considering individual variations. To do so, we reduce the problem of configurable matching to a tree matching problem and we adapt existing algorithms for this aim. The experimental results show the feasibility of our approach.

Numerous studies have discussed the benefits of using social networks, even companies started to exploit the usefulness of this valuable information sources. Collecting social data then integrating them into a CRM (Customer Relationship Management) has led companies to understand the customer needs and therefore to improve the development process of their products or their services quality. In this work, we propose a multi-agent framework for analyzing extracted opinions from social media. In the development process, we were brought to consider the huge volumes of data (Big Data) and the response time. To do so, an architecture based on Map/Reduce analysis using Hadoop was made in order to perform the data refinement (classify or remove special words or delete the unvaluable reviews) and sentiment analysis (Sentigem). Finally, a study case using Twitter (Twitter4J API) as a data source, was made to verify the effectiveness of the proposed framework.

International audience

The increasing number of people using social media to express their personal experiences has led to an emerging interest in supporting social media analysis for marketing, opinion analysis and understanding community cohesion. Social customer relationship management (SCRM) systems have become an interesting research area. Generally analysis needs to be done on large volumes of data (Big Data) in an efficient and timely manner. In this research, we propose a social intelligence framework that can extract and consolidate the reviews expressed via social media to help enterprises to know more about the customers' opinion toward the target products. This goal can be achieved by analyzing the reviewers' knowledge and authority and their opinion, sentiment (SentiGem) toward the target products, after filtering the extracted tweets from Twitter using Twitter4J. Additionally we present an architecture based on Map/Reduce analysis using Hadoop.

Over the years, different development waves have shaped the Web. Introduced as a tool to browse Web sites, the Web now is a dynamic and robust platform upon which enterprises conduct e-Business. One of these waves known as Web 2.0 (or social Web) is putting pressure on how enterprises should ensure a productive use of Web 2.0 applications such as Facebook and Twitter. Misuse cases of these applications are on the rise and the lack of guidelines and awareness is a main reason. This paper presents an approach for "controlling" the social actions that Web 2.0 applications allow users to execute. These actions are identified after analyzing some representative Web 2.0 applications and then defined in terms of stakeholders, content, and tools. The control over these actions is defined with UML Object Constraint Language (OCL) and then demonstrated through a prototype system.

Organizations, companies and Web platforms hold large amounts of unused data. These data are trapped in separate data sources, locked up in legacy formats and only reachable through several different protocols, making usage difficult. It is therefore necessary to manage this multiplicity of data sources in order to build a solution able to combine this multi-origin data into a coherent smart data set. We define a meta-model and models to describe data source diversity in a flexible way. We therefore propose an adaptive architecture that generates data integration workflows at runtime. We evaluate our approach to offer scalability, responsiveness, and dynamic and transparent data source management. We apply our approach in a live scenario from a French company to show how it adapts to industrial needs and facilitates smart data production and reuse. This paper describes our models and strategies and presents our resource-oriented architecture.

With the emergence of the open data movement, hundreds of thousands of datasets from various concerns are now freely available on the Internet. The access to a good number of these datasets is carried out through Web services which provide a standard way to interact with data. In this context, user's queries often require the composition of multiple data Web services to be answered. Defining the semantics of data services is the first step towards automating their composition. An interesting approach to define the semantics of data services is by describing them as semantic views over a domain ontology. However, defining such semantic views cannot always be done with certainty, especially when the service's outputs are too complex. In this paper, we propose a probabilistic approach to model the semantics uncertainty of data services. In our approach, a data service with an uncertain semantics is described by several possible semantic views, each one is associated with a probability. Services along with their possible semantic views are represented in a Block-Independent-Disjoint (noted BID) probabilistic service registry, and interpreted based on the Possible Worlds Semantics. Based on our modeling, we study the problem of interpreting an existing composition involving services with uncertain semantics. We also study the problem of compositing uncertain data services to answer a user query, and propose an efficient method to compute the different possible compositions and their probabilities.

This paper presents a credibility model to assess trust of Web services. The model relies on consumers' ratings whose accuracy can be questioned due to different biases. A category of consumers known as strict are usually excluded from the process of reaching a majority consensus. We demonstrated that this exclusion should not be. The proposed model reduces the gap between these consumers' ratings and the current majority rating. Fuzzy clustering is used to compute consumers' credibility. To validate this model a set of experiments are carried out.

Data-driven Web services build on service-oriented technologies to provide an interoperable method of interacting with data sources on top of the Web. Data Web services composition has emerged as a flexible solution to answer users’ complex queries on the fly. However, as the number of Web services on the Web grows quickly, a large number of candidate compositions that would use different (most likely competing) services may be used to answer the same query. User preferences are a key factor that can be used to rank candidate services/compositions and retain only the best ones. In this article, we present a novel approach for computing the top- k data service compositions based on user preferences. In our approach, we model user preferences using fuzzy sets and incorporate them into the composition query. We use an efficient RDF query rewriting algorithm to determine the relevant services that may be used to answer the composition query. We match the (fuzzy) constraints of the relevant services to those of the query and determine their matching degrees using a set of matching methods. We then rank-order the candidate services based on a fuzzification of Pareto dominance and compute the top- k data service compositions. In addition, we introduce a new method for increasing the diversity of returned top- k compositions while maintaining as much as possible the compositions with the highest scores. Finally, we describe the architecture of our system and present a thorough experimental study of our proposed techniques and algorithms. The experimental study demonstrates the efficiency and the effectiveness of our techniques in different settings.

In this demo paper, we present a new privacy preserving composition execution system. Our system allows to execute queries over multiple data services without revealing any extra information to any of the involved services. None of involved services (and their providers) is able to infer any information about the data the other services provide beyond what is permitted

Privacy is still among the key challenges that keep hampering DaaS service composition solution. Indeed services may follow different, conflicting privacy specifications with respect to the data they use and provide within a composition. In this paper, we propose an approach for privacy-aware composition of DaaS services. Our approach allows verifying the compatibility of privacy specifications of services involved in a composition. In the case when any composition will be incompatible in terms of privacy, we introduce a novel approach based on negotiation to reach compatibility of concerned services. The negotiation approach is cautiously operated with without any privacy damaging of services. We validate the applicability of our proposal through a set of experiments.

This paper discusses the value-added of diversity to support fault tolerant Web Services operation. Web Services offering the same functionality are gathering into the same virtual space referred to as diversity group. Besides the group additional components are used including: composite manager, group manager, and Web service manager. The first component invokes the diversity groups and either resumes or aborts the overall execution in case of failures. The second component oversees Web services execution and the interactions between them. Finally, the last component monitors a Web service execution and reports either failure or success to the group manager. An architecture and a set of experiments showing the use of diversity to design and deploy fault tolerant Web services is presented in this paper.

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.

Data Mashup is a special class of mashup application that combines information on the fly from multiple data sources to respond to transient business needs. Mashing up data requires an important programming skill on the side of mashups' creators, and involves handling many challenging privacy and security concerns raised by data providers. This situation prevents non-expert users from mashing up data at large. In this paper, we propose a declarative approach for mashing-up data. The approach allows data mashup creators to create data mashups without any programming involved, they just need to specify "declaratively" their data needs. The approach will then build the mashups automatically while taking into account the data's privacy and security concerns.

Data Mashup is a special class of mashup application that combines information on the fly from multiple data sources to respond to transient business needs. Data mashup is a difficult task that would require an important programming skill on the side of mashups' creators, and involves handling many challenging privacy and security concerns raised by data providers. This situation prevents non-expert users from mashing up data at large. In this paper, we present a declarative approach for mashing-up data. The approach allows data mashup creators to build data mashups without any programming involved. The approach builds the mashups automatically and takes into account the data's privacy concerns. We evaluate the efficiency of the approach via a thorough set of experiments. The results show that handling data privacy introduces only a negligible cost in the mashup building time.

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.

This paper presents an approach to developing Composite Web Services Marts (CWS-Marts). A CWS-Mart gathers ready-to-use composite Web services that users can invoke any time without going from scratch through the regular steps of discovering, selecting, and composing the necessary component Web services. In addition, this paper presents an architecture that encompasses four levels namely component, community, composite, and mart. The component and composite levels set respectively the ground upon which the community and mart levels are deployed. Similar to communities that gather similarly-functional Web services, CWS-Marts gather similarly-functional composite Web services. Some experimental details are, also, discussed in the paper.

International audience

Data-Providing (DP) services allow query-like access to organizations' data via web services. The invocation of a DP service results in the execution of a query over data sources. In most cases, users' queries require the composition of several services. In this paper, we propose a novel approach for querying and automatically composing DP services. The proposed approach largely draws from the experiences and lessons learned in the areas of service composition, ontology, and answering queries over views. First, we introduce a model for the description of DP services and specification of service-oriented queries. We model DP services as RDF views over a mediated (domain) ontology. Each RDF view contains concepts and relations from the mediated ontology to capture the semantic relationships between input and output parameters. Second, we propose query rewriting algorithms for processing queries over DP services. The query mediator automatically transforms a user's query (during the query rewriting stage) into a composition of DP services. Finally, we describe an implementation and provide a performance evaluation of the proposed approach.

Interactions between two applications encapsulated into Web services consist in series of message exchanges that must conform to service interfaces. The study reported in this text aims at dealing with the issues that arise when interactions between two services (a client and a provider) fail because their interfaces are incompatible. This may happen because the provider has evolved and its interface has been modified. It may also happen because the client decided to change for another provider which addresses the same needs but offers a different interface. The contribution of the proposed approach is twofold. First, given two services, all incompatibilities that may exist between their interfaces are automatically detected and classified into patterns. Second, according to the patterns that have been recognized, an adapter is then automatically generated. This latter is intended to act as an intermediate between the client and the provider, therefore seamlessly reconciling interactions between them.

This paper discusses how communities of Web services sustain the high availability of Web services engaged in composition scenarios. Availability is the proportion of time that a system is in a functioning condition. Web services offering the same functionality like HotelBooking are gathered into a single community regardless of who developed them, where they are located, and how they function. The current practice to achieve Web services high-availability advocates the use of replicas, which are managed according to specific replication strategies and come into play when the original Web service to back-up fails. Replacing Web services replicas with similarly functional Web services offers solutions to some of the limitations that feature these strategies, but at the same time raises other issues that are examined in this paper.

This paper presents a policy-based approach to compose Web services with emphasis on exceptions. Three types of policies denoted as engagement, mediation, and deployment regulate composition and are specified with WSPL. To support the exception handling strategy, context comes into play. Context oversees and keeps track of the execution outcomes of policies that took place and are now taking place.

The semantic web promises automates invocation, discovery and composition of web services by enhancing services with semantic descriptions. This paper describes a model driven approach to facilitate the construction of OWL-S specifications. The methodology is divided into three main steps. In the first step we reverse engineered WSDL documents into UML profile models that enable the use of high-level graphical models as an integration platform for semantic web services. In the second step, suitable domain ontologies are used for the semantic annotation of the UML models. Finally, in the third step a conversion tool will generate automatically the OWL-S description from these UML models. The UML profile provides flexibility as it can expresses multiple semantic web service concepts.

In this paper, we propose a new approach to automatically compose data providing Web services. Our approach exploits existing mature works done in data integration systems. Specifically, data providing services are modeled as RDF Parameterized views over mediated ontologies. Then, an RDF oriented algorithm to compose services based on query rewriting techniques was devised. We apply also an optimization algorithm on the generated composition to speed up its execution. The results of our experiments show that the algorithms scale up very well up to a large number of services, covering thus most realistic applications.

International audience

This paper discusses how interactions between Web services engaged in composition scenarios are analyzed, modeled, and finally managed. Interactions are primarily considered as a means for conveying messages of different natures between separate components, for instance, Web services. To achieve better coordination and hence, avoid conflicts between Web services, in this paper interactions are handled by two layers known as business logic and support. The business-logic layer is the host of two flows known as control and transactional, whereas the support layer is the host of two additional flows namely exception and message. On top of the proposed layers and flows, context and policies are used to oversee the progress of interactions between Web services and to constrain the behavior of these Web services, respectively.

Making Web services context-aware is a challenge. This is like making Web service expose appropriate behaviors in response to changes detected in the environment. Context awareness requires a review and extension of the current execution model of Web services. This paper discusses the seamless combination of context and policy to manage behaviors that Web services expose during composition and in response to changes in the environment. For this purpose, a four-layer approach is devised. These layers are denoted by policy, user, Web service, and resource. In this approach, behavior management and binding are subject to executing policies of types permission, obligation, restriction, and dispensation. A prototype that illustrates how context and policy are woven into Web services composition scenarios is presented as well.

This paper presents a Web services composition approach that is built upon three layers known as component, community, and composite. The contribution of each layer towards this approach is assessed from two perspectives known as organization and management. Furthermore this paper discusses how Web services in a community are specialized into abstract and concrete. Interactions between abstract/- concrete Web services and composite Web services happen through a driver known as Open Service Connectivity. This driver permits first, binding any abstract Web service to any composite Web service and second, triggering any concrete Web service from any composite Web service.

This paper discusses the compatibility issue between policies of Web services, which could refrain the automatic composition of these Web services from smoothly progressing. Policies regulate/constrain the functioning of Web services at three levels identified by business, behavior, and privacy. Because independent Web services collaborate with one another in order to satisfy users' needs, their respective policies could be in contradiction. This could lead to conflicts and exceptions at run-time unless these contradictions are as a first step, detected and then, fixed. In this paper, WSPL is the language to specify policies of Web services.

Web services composition is a keystone in the development of interoperable systems. However, despite the widespread adoption of Web services, several obstacles still hinder their smooth automatic semantic reconciliation when being composed. Consistent understanding of data exchanged between composed Web services is hampered by various implicit modeling assumptions and representations. Our contribution in this article revolves around context and how it enriches data exchange between Web services. In particular, a context-based mediation approach to solve semantic heterogeneities between composed Web services is presented.

Interoperability is a crucial issue in the health care domain. It must be conducted along two tightly-coupled levels: "data" and "application" levels. The former is about bridging together heterogeneous medical data sources. The later is concerned with enabling medical applications to understand each other when they interact through Web services composition. In this paper we present a peer-to-peer framework for ensuring the interoperability along its two levels. Then we present a semantic mediation model dealing with data's "context heterogeneity" between Web services within a composition process

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This paper presents an approach whose objective is to support Web services substitution. Substitution means replacing a component with another component, as long as the replacing component produces the same output and satisfies the same requirements as the replaced component. Motives for substitution include Web service's non-responsiveness to client requests and better arrangement with another, competitor Web service. To perform Web services substitution with less impact on the ongoing, and sometimes critical, business processes, the approach proposes deploying communities of Web services. A community promotes the dynamic binding of Web services through a common interface, known as open service connectivity. The open service connectivity directs requests to and responses from Web services regardless of how these latter are specified, implemented, and located

System evolution most often implies the integration of legacy components, such as databases, with newly developed ones, leading to mixed architectures that suffer from severe heterogeneity problems. For instance, incorporating a new program in a legacy database application can create an integrity mismatch, since the database model and the program data view can be quite different (e.g. standard file model versus OO model). In addition, neither the legacy DBMS (too weak to address integrity issues correctly) nor the new program (that relies on data server responsibility) correctly cope with data integrity management. The component that can reconciliate these mismatched subsystems is the R/W wrapper , which allows any client program to read, but also to update the legacy data, while controlling the integrity constraints that are ignored by the legacy DBMS.This article describes a generic, technology-independent, R/W wrapper architecture, a methodology for specifying them in a disciplined way, and a CASE tool for generating most of the corresponding code.The key concept is that of implicit construct , which is a structure or a constraint that has not been declared in the database, but which is controlled by the legacy application code. The implicit constructs are elicited through reverse engineering techniques, and then translated into validation code in the wrapper. For instance, a wrapper can be generated for a collection of COBOL files in order to allow external programs to access them through a relational, object-oriented or XML interface, while offering referential integrity control. The methodology is based on a transformational approach that provides a formal way to build the wrapper schema and to specify inter-schema mappings.

Summary form only given. Centralized architectures based on the client server paradigm show their limits each time the Internet extends its capabilities. They present problems in terms of security, robustness, performance, high needs for administration. Short time solutions like load balancing and replication, or purchasing of more powerful hardware tend to cope with these limits for a while, but do not provide a definitive answer to these problems. Currently, different proprietary approaches hinder attempts to enabling communication between heterogeneous systems, while the Internet is developing toward a better interoperability with service-oriented architectures. Open source and standardization efforts allow building a framework providing an original answer to interoperability and scalability over the Internet. In this paper, we consider that the distributed nature of P2P platforms allied with the standardized WS architecture, based on SOAP and WSDL represent a powerful framework for building dynamic interoperable and decentralized information systems. The aim of our work is to allow P2P participants to dynamically access and compose semantic WS outside of the P2P network, and to improve existing WS publication and discovery by using the decentralization of P2P architecture. In order to achieve this goal, we propose a mediation framework built around the JXTA P2P architecture. This framework is based on the OWL-S language as a means for a semantic description of WS interfaces, in order to solve heterogeneity problems.

The number of Web-based information systems has been increasing since Internet became the global open network accessible for all. The recent semantic Web that provides supplementary meaningful information (meta-data) about Web resources facilitates automatic processing of machines and interoperability between different systems. In this paper, we focus on an integration of heterogeneous data sources in the semantic Web context using a semantic mediation approach based on ontology. We use the ontology description language OWL to formalize ontologies of different resources and to describe their relations and correspondences allowing the semantic interoperability between them. We propose an architecture adopting mediator-wrapper approach for a mediator based on OWL. Some illustrations of semantic mediation using OWL are also presented in the paper.

The diversity of spatial information systems promotes the need to integrate heterogeneous spatial or geographic information systems (GIS) in a cooperative environment. The paper describes the research project ISIS (Interoperable Spatial Information System) which is a semantic mediation approach to support GIS interoperability. Its key characteristic is a dynamic resolution of semantic conflicts which is adequate for achieving autonomy, flexibility and extensibility. We propose a spatial OO data model and a mediation architecture based on multi-agent paradigm to support GIS interoperability.