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This paper discusses a goal-based approach for the engineering of capacity-driven Web services. In this approach, goals are established to first, define the roles that these Web services will play in implementing business applications, second, frame the requirements that will be put on these Web services, and third, identify the processes in terms of business logics that these Web services will carry out. Because of the nature of capacity-driven Web services compared with regular (i.e. mono-capacity) Web services, their engineering in terms of design, development, and deployment takes place in a different way. A Web service that is empowered with several capacities, which are basically separate groups of operations to execute, has to choose one capacity for triggering at run-time. To this end, the Web service takes into account different types of requirements like data and privacy that are put on each capacity that empowers this Web service.

The number of mobile devices (smartphone, tablet, laptop, etc.) and Internet users are continually increasing. Due to the accessibility provided by cloud computing, Internet and Internet of Things (IoT), users use more and more software applications which cause an increasing effect on gas emission. Thus, ICT (Information and Communication Technologies) is responsible of around 2% worldwide greenhouse gas emissions which is equivalent of that emitted by the airline industry. According to recent reports, the Intergovernmental Panel on Climate Change (IPCC), CO2 emissions due to ICT are increasing widely. Nevertheless, ICT, in allowing to solve complex problems in other sectors, can greatly and easily participate to reduce significant portion of the remaining 98% of global CO2 emissions. The use of software implies hardware operations which are physically responsible of energy consumption. Consequently, software is indirectly involved in the energy consumption. Thus, we need to reduce software energy consumption while maintaining the same functionalities for the software in order to build sustainable and green software. Firstly, in this thesis work, we define the terms sustainable and green in the area of software development. To build a software product, we need to follow a software engineering process. Hence, we define and describe sustainable and green criteria to be respected after each step of this process in order to establish a sustainable and green software engineering process. Then, we focus on the software energy consumption estimation. Many research works tried to propose various tools to estimate the energy consumption due to software in order to reduce carbon footprint. Unfortunately, these studies, in the majority of cases, consider only the CPU and neglects all others components. Existing power consumption methodologies need to be improved by taking into account more components susceptible to consume energy during runtime of an application. Writing sustainable, power efficient and green software necessitates to understand the power consumption behavior of a computer program. One of the benefits is the fact that developers, by improving their source code implementations, will optimize software power consumption. Moreover, there is a lack of analyzing tool to dynamically monitor source code energy consumption of several components. Thus, we propose GMTEEC (Generic Methodology of a Tool to Estimate Energy Consumption) which is composed of four layers assisting developers to build a tool estimating the software power consumption. Hence, in our work, respecting the layers of GMTEEC, we develop TEEC (Tool to Estimate Energy Consumption) which is based on mathematical formula established for each component (CPU, memory, hard disk, network) in order to estimate the total software energy consumption. Moreover, we add in TEEC the capacity to locate dynamically the hotpoints which are the parts of source code consuming the greater amount of energy in order to help and guide developers to optimize their source code and build efficient, sustainable and green software. We performed a variety of experiments to validate the accuracy and quality of the sustainable and green software engineering process and TEEC. The results demonstrate the possibility to save significant quantity of energy and time at limited costs with an important positive impact on environment, Le nombre de périphériques mobiles (smartphone, tablette, ordinateur portable, etc.) et les internautes augmentent continuellement. En raison de l'accessibilité du cloud computing, de l'Internet et de l'Internet des Objets (IdO), les utilisateurs utilisent de plus en plus d'applications logicielles qui provoquent un effet croissant sur les émissions de gaz à effet de serre. Ainsi, les TIC (Technologies de l'Information et de la Communication) sont responsables d'environ 2% des émissions mondiales de gaz à effet de serre qui sont équivalentes à celles émises par l'industrie aérienne. Selon des rapports récents, le Groupe d'experts Intergouvernemental sur l'Evolution du Climat (GIEC), les émissions de CO2 dus aux TIC augmentent rapidement. Néanmoins, les TIC, en permettant de résoudre des problèmes complexes dans d'autres secteurs, peuvent grandement et facilement participer pour réduire une partie importante des 98% restants des émissions mondiales de CO2. L'utilisation du logiciel implique des opérations matérielles qui sont physiquement responsables de la consommation d'énergie. Par conséquent, le logiciel est indirectement impliqué dans la consommation d'énergie. Ainsi, nous devons réduire la consommation d'énergie du logiciel tout en conservant les mêmes fonctionnalités pour le logiciel afin de créer des logiciels durables et verts. Premièrement, dans ce travail de thèse, nous définissons les termes «durable et vert» dans le domaine du logiciel afin de créer des logiciels respectant les critères de ces termes. Pour créer un produit logiciel, nous devons suivre un processus d'ingénierie logicielle. Par conséquent, nous décrivons des critères durables et verts à respecter après chaque étape de ce processus afin d'établir un processus d'ingénierie logicielle durable et écologique. En particulier, nous nous concentrons sur l'estimation de la consommation d'énergie du logiciel. De nombreux travaux ont essayé de proposer divers outils pour estimer la consommation d'énergie due aux logiciels afin de réduire l'empreinte carbone. Pendant longtemps, les solutions proposées se sont concentrées uniquement sur la conception du matériel, mais ces dernières années, les aspects logiciels sont également devenus importants. Malheureusement, ces études, dans la plupart des cas, ne considèrent que le CPU et négligent tous les autres composants. Les modèles de consommation d'énergie existants doivent être améliorés en tenant compte de plus de composants susceptibles de consommer de l'énergie pendant l'exécution d'une application. L'écriture d'un logiciel durable, performant et vert nécessite de comprendre le comportement de consommation d'énergie d'un programme informatique. L'un des avantages est que les développeurs, en améliorant leurs implémentations du code source, optimiseront la consommation d'énergie du logiciel. De plus, il existe un manque d'outil d'analyse pour surveiller dynamiquement la consommation d'énergie du code source de plusieurs composants. Ainsi, nous proposons GMTEEC (Méthodologie Générique d'Outil pour Estimer la Consommation Energétique) qui se compose de quatre couches aidant et guidant la construction d'un outil permettant d'estimer la consommation énergétique d'un logiciel. Ainsi, dans notre travail, en respectant les couches de GMTEEC, nous créons TEEC (Outil pour Estimer la Consommation Energétique) qui repose sur une formule mathématique établie pour chaque composant (CPU, mémoire, disque dur, réseau) susceptible de consommer de l'énergie afin d'estimer la consommation totale d'énergie du logiciel composée de la somme de chaque consommation d'énergie par composant. De plus, nous ajoutons à TEEC la capacité de localiser dynamiquement les points chauds qui sont les parties du code source consommant la plus grande quantité d'énergie afin d'aider et guider les développeurs à optimiser leur code source et à créer des logiciels efficaces, durables et verts... [etc]

Gavin Kemp, Genoveva Vargas-Solar, Catarina Ferreira da Silva, Parisa Ghodous, Christine Collet, et al.. Cloud big data application for transport. In Int. J. Agile Systems and Management, 2016, X, ; International audience; This paper presents a cloud service oriented approach for managing and analysing big data required by transport applications. Big data analyticsbrings new insights and useful correlations of large data collections providing undiscovered knowledge. Applying it to transport systems brings betterunderstanding to the transport networks revealing unexpected choking points in cities. This facility is still largely inaccessible to small companies due to their limited access to computational resources. A cloud-oriented architecture opens new perspectives for providing efficient and personalised big data management and analytics services to (small) companies.

International audience; There is an ongoing interest in examining users' experiences made available through social media. Unfortunately these experiences like reviews on products and/or services are sometimes conflicting and thus, do not help develop a concise opinion on these products and/or services. This paper presents a multi-stage approach that extracts and consolidates reviews after addressing specific issues such as user multi-identity and user limited credibility. A system along with a set of experiments demonstrate the feasibility of the approach.

As a class of context-aware systems, context-aware service recommendation 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 constructed context-aware service knowledge graph (C-SKG) is, then, transformed into a low-dimentional 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 CASR approach.

In this thesis, we focus on data integration of raw data coming from heterogeneous and multi-origin data sources on the Web. The global objective is to provide a generic and adaptive architecture able to analyze and combine this heterogeneous, informal, and sometimes meaningless data into a coherent smart data set. We define smart data as significant, semantically explicit data, ready to be used to fulfill the stakeholders' objective. This work is motivated by a live scenario from the French {\em Audience Labs} company. In this report, we propose new models and techniques to adapt the combination and integration process to the diversity of data sources. We focus on transparency and dynamicity in data source management, scalability and responsivity according to the number of data sources, adaptability to data source characteristics, and finally consistency of produced data (coherent data, without errors and duplicates). In order to address these challenges, we first propose a meta-models in order to represent the variety of data source characteristics, related to access (URI, authentication) extraction (request format), or physical characteristics (volume, latency). By relying on this coherent formalization of data sources, we define different data access strategies in order to adapt access and processing to data source capabilities. With help form these models and strategies, we propose a distributed resource oriented software architecture, where each component is freely accessible through REST via its URI. The orchestration of the different tasks of the integration process can be done in an optimized way, regarding data source and data characteristics. This data allows us to generate an adapted workflow, where tasks are prioritized amongst other in order to fasten the process, and by limiting the quantity of data transfered. In order to improve the data quality of our approach, we then focus on the data uncertainty that could appear in a Web context, and propose a model to represent uncertainty in a Web context. We introduce the concept of Web resource, based on a probabilistic model where each resource can have different possible representations, each with a probability. This approach will be the basis of a new architecture optimization allowing to take uncertainty into account during our combination process, Cette thèse porte sur l'intégration de données brutes provenant de sources hétérogènes sur le Web. L'objectif global est de fournir une architecture générique et modulable capable de combiner, de façon sémantique et intelligente, ces données hétérogènes dans le but de les rendre réutilisables. Ce travail est motivé par un scenario réel de l'entreprise Audience Labs permettant une mise à l'échelle de cette architecture. Dans ce rapport, nous proposons de nouveaux modèles et techniques permettant d'adapter le processus de combinaison et d'intégration à la diversité des sources de données impliquées. Les problématiques sont une gestion transparente et dynamique des sources de données, passage à l'échelle et responsivité par rapport au nombre de sources, adaptabilité au caractéristiques de sources, et finalement, consistance des données produites(données cohérentes, sans erreurs ni doublons). Pour répondre à ces problématiques, nous proposons un méta-modèle pour représenter ces sources selon leurs caractéristiques, liées à l'accès (URI) ou à l'extraction (format) des données, mais aussi au capacités physiques des sources (latence, volume). En s'appuyant sur cette formalisation, nous proposent différentes stratégies d'accès aux données, afin d'adapter les traitements aux spécificités des sources. En se basant sur ces modèles et stratégies, nous proposons une architecture orientée ressource, ou tout les composants sont accessibles par HTTP via leurs URI. En se basant sur les caractéristiques des sources, sont générés des workflows d'exécution spécifiques et adapté, permettant d'orchestrer les différentes taches du processus d'intégration de façon optimale, en donnant différentes priorités à chacune des tâches. Ainsi, les temps de traitements sont diminuées, ainsi que les volumes des données échangées. Afin d'améliorer la qualité des données produites par notre approches, l'accent est mis sur l'incertitude qui peut apparaître dans les données sur le Web. Nous proposons un modèle, permettant de représenter cette incertitude, au travers du concept de ressource Web incertaines, basé sur un modèle probabiliste ou chaque ressource peut avoir plusieurs représentation possibles, avec une certaine probabilité. Cette approche sera à l'origine d'une nouvelle optimisation de l'architecture pour permettre de prendre en compte l'incertitude pendant la combinaison des données

International audience; Currently, a good portion of datasets on Internet are accessed through data services, where user’s queries are answered as a composition of multiple data services. 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 returned data are too complex. In such case, a data service is associated with several possible semantic views. In addition, complex correlations may be present among these possible semantic views, mainly when data services encapsulate the same data sources. In this paper, we propose a probabilistic approach to model the semantic uncertainty of data services. Services along with their possible semantic views are represented in probabilistic service registry. The correlations among service semantics are modeled through a directed probabilistic graphical model (Bayesian network). Based on our modeling, we study the problem of compositing correlated data services to answer a user query, and propose an efficient method to compute the different possible compositions and their probabilities.

In this thesis we focus on the data web services composition problem and study the impact of the uncertainty that may be associated with the output of a service on the service selection and composition processes. This work is motivated by the increasing number of application domains where data web services may return uncertain data, including the e-commerce, scientific data exploration, open web data, etc. We call such services that return uncertain data as uncertain services. In this dissertation, we propose new models and techniques for the selection and the composition of uncertain data web services. Our techniques are based on well established fuzzy and probabilistic database theories and can handle the uncertainty efficiently. First, we proposed a composition model that takes into account the user preferences. In our model, user preferences are modelled as fuzzy constraints, and services are described with fuzzy constraints to better characterize their accessed data. The composition model features also a composition algebra that allows us to rank the returned results based on their relevance to user's preferences. Second, we proposed a probabilistic approach to model the uncertainty of the data returned by uncertain data services. Specifically, we extended the web service description standards (e.g., WSDL) to represent the outputs' probabilities. We also extended the service invocation process to take into account the uncertainty of input data. This extension is based on the possible worlds theory used in the probabilistic databases. We proposed also a set of probability-aware composition operators that are necessary to orchestrate uncertain data services. Since a composition may accept multiple orchestration plans and not all of them compute the correct probabilities of outputs, we defined a set of conditions to check if a plan is safe (i.e., computes the probabilities correctly) or not. We implemented our different techniques and applied them to the real-estate and e-commerce domains. We provide a performance study of our different composition techniques, Cette thèse porte sur la composition des services de données et l'étude de l'impact de l'incertitude qui peut être associée à leurs données accessibles sur le processus de composition et de sélection de service. En effet, dans un contexte tel que l'Internet, il est de plus en plus reconnu que les données et les services d'accès aux données sont sujettes à des valeurs d'incertitude tout en exigeant des techniques de gestion plus sophistiquées. Dans cette thèse, nous enrichissons la description sémantique des services Web afin de refléter l'incertitude, et nous proposons de nouveaux mécanismes et modèles pour la sélection et la composition des services. Nos mécanismes sont basés sur les ensembles flous et les théories probabilistes. Tout d'abord, nous étendons notre modélisation précédente basée sur les vues RDF afin d'inclure les contraintes floues qui caractérisent les données accédées par les services. Nous proposons une algèbre de composition qui permet de classer les résultats retournés en fonction de leur pertinence par rapport aux préférences de l'utilisateur. Notre algèbre proposée repose sur les fondations de bases de données floues. En outre, nous optons pour l'approche probabiliste pour modéliser l'incertitude des données renvoyées par les services incertains. Nous étendons la description du service Web standard pour représenter les probabilités de sortie. L'invocation des services est également étendue pour tenir compte de l'incertitude. Cette extension est basée sur la théorie des mondes possibles utilisée dans les bases de données probabiliste. Nous définissons un ensemble d'opérateurs de composition qui sont nécessaires pour orchestrer les services de données. Pour chaque composition, plusieurs plans d'orchestration peuvent être possibles mais qui sont pas tous corrects, donc, nous définissons un ensemble de conditions pour vérifier si le plan est correct (Safe) ou pas. Nous fournissons une implémentation de nos différentes techniques et les appliquer aux domaines de l'immobilier et du commerce électronique. Nous implémentons ces services et nous fournissons également une étude de la performance de notre prototype de composition

International audience; With Cloud/Fog Computing being a paradigm combination of IoT context and Edge Computing extended with Cloud/Fog, business process in it involves dataflows among multilayers and multi-nodes, possibly provided by multi-organizations. Achieving end-to-end trustworthiness over the whole dataflow in such a Cloud/Fog Computing context is a challenging issue, nonetheless a necessary pre-condition for a successful business process on intra-/inter- organizational level. This paper investigates technical conundrums related to this target and proposes a policy-based approach for trustworthiness governance. An architectural layout is proposed with according modules, by carrying out two methodologies. One resides in tracing data derivation and maintaining security-level over the whole dataflow, handling data aggregation with several protocols. The other is to express data owner trustworthiness requirements with an enhanced attribute-based access control policy model and to evaluate data accessing nodes’ trustworthiness-related properties. Experiments show that processing time per attribute pair drops as the scales of policies increase, suggesting good scaling property of the system.

International audience; Privacy is among the key challenges to data integration in many sectors, including healthcare, e-government, etc. The PAIRSE project aims at providing a flexible, looselycoupled and privacy-preserving data integration system in P2P environments. The project exploits recent Web standards and technologies such as Web services and ontologies to export data from autonomous data providers as reusable services, and proposes the use of service composition as a viable solution to answer data integration needs on the fly. The project proposed new composition algorithms and service/composition execution models that preserve privacy of data manipulated by services and compositions. The proposed integration system was demonstrated at EDBT 2013 and VLDB 2011.

Web services have emerged as a support for development and integration of applications and information systems. In this context, the interactions between two consumer and supplier applications, encapsulated by Web services are done by exchanging messages. These exchanges are based on the concept of interfaces, which describe the interactions in which a service can handle and dependencies between these interactions. In the current Web, it is very often that many services meet the same set of functional requirements. These services are often delivered through different interfaces. For many reasons, such as failure of the service provider, the consumer has to replace his usual supplier with another supplier that offers the same functionality. This substitution leads to incompatibilities between the interfaces of the service consumer and new service provider. This is because the customer service was not been developed according to the new service provider. The research we conduct in this thesis aims to solve the problem of incompatibility in the interaction between two services; consumer and new provider. In particular, our contribution extends both in theory and in practice. It is a part of a framework for the automatic generation of adapters for interactions between two services. On the other hand, we propose a multi-layer software architecture providing a framework for transparent and flexible substitution of a service provider by another with respect to an existed consumer. In our framework for automatic generation of adapters, services interfaces modeling using automata has been adopted. Then, a step of detecting incompatibilities between them is achieved. After that, an adapter of the interactions between the two services is generated automatically based on the detected incompatibilities. The generation of the adapter is based on the automata model. The generated adapter automaton contains a sufficient detail for the projected technology CEP (Complex Event Processing). This projection is performed using components templates that are implemented by us. Each template is designed to generate executable code (in terms of continuous requests) for the CEP technology. Our proposed architecture for the substitution of integrated Web services is an approach for deploying communities of Web services. A community promotes the dynamic binding of Web services through software named OSC (Open Service Connectivity). A community service is seen as a way to expose common descriptions of a desired functionality without explicitly referring to a specific service. OSC is a software component whose objective is to apply the principle of ODBC and JDBC in an environment based on Web services. Specifically, it is responsible for managing interactions between communities of consumers on the one hand and communities on the other hand, providing functions for the selection and substitution of services., Les services Web ont émergé comme un support de développement et d'intégration d'applications ou de systèmes d'information. Dans ce cadre, les interactions entre deux applications consommateur et fournisseur, encapsulées par des services Web se font par échanges de messages. Ces échanges s'appuient sur la notion d'interfaces, qui décrivent les interactions dans lesquelles un service peut s'engager et les dépendances entre ces interactions. Dans le Web actuel, il arrive très fréquemment que de nombreux services répondent à un même ensemble de besoins fonctionnels. Ces services sont souvent offerts par le biais d'interfaces différentes. Des nombreuses raisons, telles que la panne du service fournisseur, peuvent amener un consommateur à substituer son fournisseur habituel par un autre fournisseur qui offre la même fonctionnalité. Cette substitution provoque des incompatibilités entre l' interface du service consommateur et celle du service fournisseur substitut. Cela est dû au fait que le service consommateur n'a pas été fait en fonction de ce nouveau service fournisseur. Les recherches que nous menons dans cette thèse visent à résoudre le problème des incompatibilités dans des interactions entre deux services consommateur et fournisseur substitut. En particulier, notre contribution s'étend, tant sur le plan théorique que sur le plan pratique. Il s'agit d'une part d'un canevas pour la génération automatique des adaptateurs des interactions entre deux services. D'autre part, nous proposons une architecture logicielle multicouche fournissant un cadre permettant une substitution transparente et flexible d'un service fournisseur par un autre service vis-à-vis du consommateur du premier. Dans notre canevas pour la génération automatique des adaptateurs, une modélisation des interfaces de services en des automates a été adoptée. Puis, une étape de détection des incompatibilités entre ceux-ci est réalisée. Ensuite, un adaptateur des interactions entre les deux services est généré automatiquement sur la base d'incompatibilités détectées. La génération de l'adaptateur est guidée par le modèle d' automates. Cela permet de modéliser l' adaptateur indépendamment de son implémentation cible, permettant ainsi une bonne réutilisation des modèles. Une fois généré, l'automate de l'adaptateur suffisamment détaillé est projeté sur la technologie CEP (Complexe Event Processing). Cette projection est réalisée à l'aide des composants cartouches (en anglais : Templates) que nous avons mis en œuvre. Chaque cartouche étant conçue pour générer du code exécutable (en termes de requêtes continues) pour la technologie CEP. Notre architecture proposée pour la substitution de services Web s'intègre d'une part la notion de communauté de services, et d'autre part un progiciel nommé OSC (Open Service Connectivity). Une communauté de service est perçue comme un moyen d'exposer des descriptions communes d'un caractère fonctionnel désiré sans explicitement se référer à un service spécifique. L'OSC est un composant logiciel dont l'objectif est d'appliquer le principe de pilotes ODBC et JDBC dans un environnement à base de services Web. Plus précisément, il est responsable de gérer les interactions entre les consommateurs des communautés d'une part et les communautés d'autre part, en fournissant des fonctions permettant la sélection et la substitution de services.

This thesis studies how a context-based approach could improve interoperability between heterogeneous ontologies developed and evolving autonomously. The hypothesis is that during collaborations, organizations exchange their data whose meaning is described by concepts defined by ontologies. The objective is to propose a context-based evaluation of such concepts. We propose a methodology to determine, model and use the context. Applying this methodology, we uncover three usages of context that may be used together to improve ontology reconciliation: We propose to disambiguate among the possible pragmatic meanings of the concepts by comparing the “perspectives” with or for which the concepts have been developed; to personalize this comparison by considering the agent's context, made of a relevant selection of the agent's company domains and tasks; to evaluate the pertinence of the data associated with the concept for the task that triggered the interoperability need., Cette thèse étudie l'intérêt du contexte pour améliorer l'interopérabilité entre ontologies hétérogènes, d'une manière qui permette leur évolution indépendante. Lors de collaborations, les organisations échangent leurs données, qui sont décrites par des concepts définis dans des ontologies. L'objectif est d'obtenir un service d'évaluation de tels concepts, basé sur le contexte.Nous proposons une méthodologie pour déterminer, modeler et utiliser le contexte. En l'appliquant, nous découvrons trois usages du contexte qui contribuent à améliorer la réconciliation d'ontologies : Nous proposons de désambiguïser les sens pragmatiques possibles des concepts en comparant les "perspectives" avec lesquelles les concepts ont été développés ; de personnaliser en considérant le contexte des agents, constitué d'une sélection pertinente parmi les domaines et tâches de l'organisation ; d'évaluer la pertinence des données associées au concept pour la tâche qui a suscité le besoin en interopérabilité.

International audience; In recent years, the rapid development of blockchain technology and cryptocurrencies has influenced the financial industry by creating a new crypto-economy. Then, next-generation decentralized applications without involving a trusted third-party have emerged thanks to the appearance of smart contracts, which are computer protocols designed to facilitate, verify, and enforce automatically the negotiation and agreement among multiple untrustworthy parties. Despite the bright side of smart contracts, several concerns continue to undermine their adoption, such as security threats, vulnerabilities, and legal issues. In this paper, we present a comprehensive survey of blockchain-enabled smart contracts from both technical and usage points of view. To do so, we present a taxonomy of existing blockchain-enabled smart contract solutions, categorize the included research papers, and discuss the existing smart contract-based studies. Based on the findings from the survey, we identify a set of challenges and open issues that need to be addressed in future studies. Finally, we identify future trends.

International audience; APIs 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.

International audience

The fast development of the so-called shared economy as well as collaborative networked organization increases the call for responsive and strongly interconnected production and delivery systems. The closer connection of information systems and control systems has to be achieved, as “Industry 4.0” developed by leading industries in Europe, USA and Asia requires to set new IT models in order to support agile collaborative business processes and production control processes. Whereas the service-based organizations and technologies, such as Service-Oriented Architecture, Web 2.0, Cloud and Everything as a Service technologies as well as Business models, have been successfully deployed to support agile systems relying on service selection, composition and orchestration, only few works have addressed cloud-based control systems. Despite the flexibility provided by the Internet of Thing, the integration of Cyber-Physical systems (CPS) in production control system is limited due to their lack of interoperability. To overcome this limit and provide a smooth integration of CPS systems in collaborative production process control, we propose a service-based control model integrating sensors, controllers and actuators. To support the Industry 4.0 digital transformation, including collaborative production process design and management, our Control as a Service model relies on control ontology. Our control ontology gathers IoT devices (sensors and actuators $$\ldots $$ ) and services, including their functional and non-functional properties description. Then, a Control as a Service architecture is designed to facilitate the development of cloud control system via two steps: service pre-selection/pre-composition, event-driven and context-aware service orchestration. The measurement of three core components, data manager, event manager and context manager, shows the feasibility of application of our Control as a Service model/architecture in a supply chain temperature control use case.

Supervised deep learning has achieved remarkable success in various applications. Successful machine learning application however depends on the availability of sufficiently large amount of data. In the absence of data from the target domain, representative data collection from multiple sources is often needed. However, a model trained on existing multi-source data might generalize poorly on the unseen target domain. This problem is referred to as domain shift. In this paper, we explore the suitability of multi-source training data selection to tackle the domain shift challenge in the context of domain generalization. We also propose a microservice-oriented methodology for supporting this solution. We perform our experimental study on the use case of building energy consumption prediction. Experimental results suggest that minimal building description is capable of improving cross-building generalization performances when used to select energy consumption data., Service Oriented Computing and Applications, Springer, 2020

International audience; Handling very large data, in order to make the best decision, is only possible through an extraction of knowledge. Data mining has become a widely used process in data analytics to extract the most important knowledge for predictive decision making. One of the important types of data mining is clustering mechanism ; its purpose is dividing data into a set of clusters with very large data, the numbers of parameters are very high, and the clustering problem is more difficult. Metaheuristics have been widely used in clustering; they can provide satisfactory solutions for complex problems. The main objective of this paper is to propose a new clustering algorithm based on a metaheuristic technique called Symbiotic Organisms Search (SOS), it was inspired from a biological process, and it simulates the symbiotic interaction between organisms of the same population. The SOS method is used to find the optimal centers of a number of clusters, as a supervised data mining technique. Experimental results have been performed through two phases. Firstly, the SOS technique is benchmarked with six well-known test functions. Secondly, different medical datasets have been used to test our proposed clustering method based on SOS, and show its credibility of treatment. ARTICLE HISTORY

Countless discussions around security challenges affecting cloud computing are often large textual accounts, which can be cumbersome to read and prone to misinterpretation. The growing reliance on cloud computing means that not only should we focus on evaluating its security challenges but devote greater attention towards how challenges are viewed and communicated. With many cloud computing implementations in use and a growing evolution of the cloud paradigm (including fog, edge and cloudlets), comprehending, correlating and classifying diverse perspectives to security challenges increasingly becomes critical. Current classifications are only suited for limited use; both as effective tools for research and countermeasures design. The taxonomic approach has been used as a modeling technique towards classifying concepts across many domains. This paper surveys multiple perspectives of cloud security challenges and systematically develops corresponding graphical taxonomy based upon meta-synthesis of important cloud security concepts in literature. The contributions and significance of this work are as follows: (1) a holistic view simplifies visualization for the reader by providing illustrative graphics of existing textual perspectives, highlighting entity relationships among cloud entities/players thereby exposing security areas at every layer of the cloud. (2) a holistic taxonomy that facilitates the design of enforcement or corrective countermeasures based upon the source or origin of a security incident. (3) a holistic taxonomy highlights security boundary and identifies apt areas to implement security countermeasures.

Internet of Things (IoT), one of the fastest growing Information and Communication Technologies (ICT), is playing a major role in provisioning contextualized, smart services to end-users and organizations. To sustain this role, many challenges must be tackled with focus in this paper on the design and development of thing composition. The complex nature of today’s needs requires groups of things, and not separate things, to work together to satisfy these needs. By analogy with other ICTs like Web services, thing composition is specified with a model that uses dependencies to decide upon things that will do what, where, when, and why. Two types of dependencies are adopted, regular that schedule the execution chronology of things and special that coordinate the operations of things when they run into obstacles like unavailability of resources to use. Both resource use and resource availability are specified in compliance with Allen’s time intervals upon which reasoning takes place. This reasoning is technically demonstrated through a system extending EdgeCloudSim and backed with a set of experiments.

International audience

The ubiquitous use of Internet-of-Things (IoT) is enabling a new era of wireless Sensor Nodes ( SN s) that can be subject to attacks like any other piece of hardware and software. Unfortunately, an open and challenging issue is to what extent legitimate SN s can be trusted. This paper presents an energy-efficient, software-defined-network-based Mobile Code-driven Trust Mechanism ( MCTM ) for addressing this issue by assessing trust of SN s based on their forwarding behaviors. MCTM uses mobile code to visit the SN s based on pre-defined itineraries while collecting necessary details about these SN s in preparation for assessing their trust. The results gained from the experiments demonstrate a superior performance over a state-of-art technique that is energy-efficient management based on Software-Defined Network ( SDN ) for SN s. Message overhead is reduced by approximately 50%, which results in consuming less energy when detecting malicious SN s.

International audience; The European Telecommunications Standards Institute (ETSI) established the key reference architectural framework for Network Function Virtualization (NFV). The NFV framework supports several phases (e.g., design, deploy, and manage) that make up the life-cycle of Virtual Network Functions (VNFs) along with their connectivity through the Network Service (NS) concept. Furthermore, coupled with Software-Defined Networking (SDN), NFV provides a dynamic reshaping of the network control plane. For a proper NS provisioning, network providers should anticipate and handle all the possible and practical VNFs composition scenarios, even those that are either rarely or never used. This results in costly, tedious, and time-consuming procedures when designing, deploying, and managing NSs. To overcome these limitations, NSs can be further improved by using techniques that have not been deeply explored yet. In this paper, we advocate for routing VNFs with re-configurable wiring capabilities as a novel concept that would allow agile and dynamic VNFs wiring from design-time to run-time. To validate this work, we developed a proof-of-concept as a plugin on top of ETSI Open Source MANO, as well as, a running vehicle platooning prototype. The performed experiments show that our approach: (i) performs better than the SDN-based solution in terms of latency and Mean Time-To-Operation (MTTO), and (ii) shows that the associated cost, with regard to SDN-based solution. remains acceptable.

This paper proposes a QoS-based trust evaluation model for black box data services. Under the black-box model, data services neither export (meta)-data about conditions in which they are deployed and collect and process data nor the quality of data they deliver. Therefore, the black-box model creates blind spots about the extent to which data providers can be trusted to be used to build target applications. The trust evaluation model for black box data services introduced in this paper originally combines QoS indicators, like service performance and data quality, to determine services trustworthiness. The paper also introduces DETECT: a Data sErvice as a black box Trust Evaluation arChitecTure, that validates our model. The trust model and its associated monitoring strategies have been assessed in experiments with representative case studies. The results demonstrate the feasibility and effectiveness of our solution., Comment: Long Version, short version ICWS 2021

Data curation deals with managing the data by applying different tasks such as extraction, enrichment, cleaning to fit the purpose of use. Indeed, nowadays, there is an increasing need to implement such tasks in the big data era to maintain data management. Big data is involved in decision processes to perform analysis, visualization, prediction, etc. Thus, there is a dependency between the generated outcomes and the input data of such a process. Therefore, decision process features (e.g., decision context, user constraints, and requirements) need to be taken into account during the data management process, including the data curation phase. Although the proposed curation approaches in the literature are diverse, most of them are static and do not consider the decision process features. Moreover, most of the proposals are dedicated to curating a specific data source format (e.g., structured/unstructured data source). To overcome these limitations, we propose a new approach ACUSEC (Adaptive CUration SErvice Composition) that ensures adaptive curation services composition by considering different features: the source type, the user constraints and preferences, and the decision context. To do so, we rely on AI and machine learning mechanisms such as reinforcement learning. Following the approach's definition, we conducted experiments that show encouraging results in overall execution time and adaptation to the above features.

Blockchain is at the core of the EU strategy to advance digital transformation, benefitting society/businesses and stimulating sustainable growth. The competitiveness of blockchain development and applications largely relies on the availability of competent/versatile workforce. Whereas the demand for blockchain skills is steadily increasing, employers are facing a shortfall of skilled professionals. The sector is challenged by talents shortage, global competitive pressures, as well as a limited connection between education and the labor market. The aim of CHAISE initiative presented in this paper is to set forward an open, inclusive blockchain skills governance system, to address skills mismatches and deliver appropriate training, intelligence gathering & mobility solutions tailored to sectoral needs.

International audience; Network Function Virtualization (NFV) has increasingly gained importance to address some emerging networking challenges like agility and cost-effectiveness. NFV enables to run Virtualized Network Functions (VNF) on top of any generic, Commercial-Off-The-Shelf (COTS) hardware, anytime and anywhere in the network. Specific service providers offer VNFs to prospective network providers. Service providers publish VNFs in dedicated marketplaces where network providers search VNFs and instantiate them according to a pre-established service-level agreement. On top of being proprietary and specific to the service providers, the existing VNF description models include details on VNF deployment but fail to fit VNF functional and non-functional specifications. This description alters an efficient selection of the most relevant VNFs and prevents full automation of the VNFs provisioning. This paper introduces a novel domain-independent VIrtualized networK functIoN ontol-oGy (VIKING for short) for VNF description and publication in federated repositories. It also proposes a semantic-based matchmaking algorithm to discover and select the most relevant VNFs that satisfy prospective VNF consumers' requests. As for validation, a prototype called Mastermyr Chest, including VIKING's instantiation along with the matchmaker in Content Delivery Networks (CDN) domain was implemented. This prototype illustrates a new way to contribute to the redesign of the CDN's traditional architecture by enabling value-added CDN service provisioning in an agile and dynamic manner. A set of experiments was run to (i) evaluate the matchmaker performances and (ii) demonstrate its accuracy and precision.

The computer science community is paying more and more attention to data due to its crucial role in performing analysis and prediction. Researchers have proposed many data containers such as files, databases, data warehouses, cloud systems, and recently data lakes in the last decade. The latter enables holding data in its native format, making it suitable for performing massive data prediction, particularly for real-time application development. Although data lake is well adopted in the computer science industry, its acceptance by the research community is still in its infancy stage. This paper sheds light on existing works for performing analysis and predictions on data placed in data lakes. Our study reveals the necessary data management steps, which need to be followed in a decision process, and the requirements to be respected, namely curation, quality evaluation, privacy-preservation, and prediction. This study aims to categorize and analyze proposals related to each step mentioned above.

International audience; Blockchain technology enables several untrustworthy parties to execute inter-organizational business processes in a tamper-proof manner. Existing approaches are based on smart contract code generation. Although the smart contract immutability guarantees that the process model is followed by each participant, any change in the process model makes the existing process instance smart contract not compliant with the process model and therefore, the deployment of a new smart contract becomes a requirement. In this paper, we propose CoBuP, a decentralized Collaborative Business Process execution architecture using blockchain. To address the inflexibility issue, this paper presents an interpreter of BPMN process models, supporting the instantiating, execution, monitoring of process instances. For this reason, one generic smart contract is deployed once, which helps generate a process instance, create BPMN elements by invoking the process instance predefined functions, and dynamically update the adaptation of a process at run time. For validation purposes, two use cases have permitted to carry out different experiments that demonstrate the technical feasibility of our approach. The obtained results of the experimental evaluation shows that our approach cost is similar or lower than that of the existing approaches.

By analogy to cells that grow, split, merge, and die, this paper applies same operations to things residing in an open Internet-of-Things (IoT) ecosystem. Despite the growing interest in IoT, things are mainly “busy” with sensing and (some) actuating, which prevents them from being responsive to changes in this ecosystem. To address this limitation, this paper proposes mutation as a novel mechanism for making things responsive and hence, capable of either satisfying “unseen” needs or seizing “unexpected” opportunities. To approve/deny mutation, a decision-making process is initiated progressing over 4 stopovers (obligation, permission, prohibition, and dispensation) along with 4 stages (awareness, pre-mutation, mutation-itself, and post-mutation) that would allow to answer questions such as why to approve/deny mutation, how to prepare mutation, and how to evaluate mutation. A testbed demonstrating the technical doability of thing mutation along with the conducted experiments are also discussed in the paper.

Wildfire prediction from Earth Observation (EO) data has gained much attention in the past years, through the development of connected sensors and weather satellites. Nowadays, it is possible to extract knowledge from collected EO data and to learn from this knowledge without human intervention to trigger wildfire alerts. However, exploiting knowledge extracted from multiple EO data sources at run-time and predicting wildfire raise multiple challenges. One major challenge is to provide dynamic construction of service composition plans, according to the data obtained from sensors. In this paper, we present a knowledge-driven Machine Learning approach that relies on historical data related to wildfire observations to guide the collection of EO data and to automatically and dynamically compose services for triggering wildfire alerts.

International audience; Service-Oriented Computing (SOC) and SOC systems have been invented, studied, and developed in response to the problems generated by heterogeneity and poor latency, and a need for modularization and standardization. Wide application of SOC, practically in all areas of human endeavors, interconnectivity provided by wireless networks, huge volumes and diversity of data, increased frequency of security attacks, and reliability expectations have increased research, development, and deployment of SOC systems. These circumstances and requirements could only be dealt with by a future generation of SOC systems. This implies a need for fast and high-quality research and wide and efficient dissemination of achieved innovative results. Therefore, a Special Issue (SI) was proposed, and its relevant scope defined. This SI involves a set of high-quality and cross-community scientific papers from various disciplines (e.g., cloud computing, edge computing, business process management). The accepted papers focus on research leading toward future generation of service-oriented computing systems (e.g., dynamic quality of service management in fog computing, time-aware resources allocation in cloud computing, resilient composition in the Internet of Things) with emphasis on advanced results that solve open research problems and have significant impact on the field of service-oriented computing.

Data analytics based on the produced data from the Internet of Things (IoT) devices is expected to improve the individuals’ quality of life. However, ensuring security and privacy in the IoT data aggregation process is a non-trivial task. Generally, the IoT data aggregation process is based on centralized servers. Yet, in the case of distributed approaches, it is difficult to coordinate several untrustworthy parties. Fortunately, the blockchain may provide decentralization while overcoming the trust problem. Consequently, blockchain-based IoT data aggregation may become a reasonable choice for the design of a privacy-preserving system. To this end, we propose PrivDA, a Privacy-preserving IoT Data Aggregation scheme based on the blockchain and homomorphic encryption technologies. In the proposed system, each data consumer can create a smart contract and publish both terms of service and requested IoT data. Thus, the smart contract puts together into one group potential data producers that can answer the consumer’s request and chooses one aggregator, the role of which is to compute the group requested result using homomorphic computations. Therefore, group-level aggregation obfuscates IoT data, which complicates sensitive information inference from a single IoT device. Finally, we deploy the proposal on a private Ethereum blockchain and give the performance evaluation.

International audience; The development of Decentralised Blockchain Applications (DBA) is becoming more and more complex. We formalise the development of DBA based on Business Process Modelling and ontologies. This formalisation permits to represent a set of specific constraints that characterise a DBA. These constraints concern data management, the Blockchain storage cost, the number of transactions allowed in a given time and the execution of the application logic. Based on this formalism, we propose a generic and adaptive methodology for the development of DBAs. This methodology uses as input the constraints related to each DBA and as output recommends a set of appropriate services necessary for the development of each DBA. For example, our methodology can recommend a specific distributed storage service such as InterPlanetary File System (IPFS) for a DBA that needs to handle a large amount of data. We apply our methodology to a Blockchain Video Game application.

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.

International audience

Protecting Information Systems (IS) relies traditionally on security risk analysis methods. Designed for well-perimetrised environments, these methods rely on a systematic identification of threats and vulnerabilities to identify efficient control-centered protection countermeasures. Unfortunately, this does not fit security challenges carried out by the opened and agile organizations provided by the Social, Mobile, big data Analytics, Cloud and Internet of Things (SMACIT) environment. Due to their inherently collaborative and distributed organization, such multi-tenancy systems require the integration of contextual vulnerabilities, depending on the a priori unknown way of using, storing and exchanging data in opened cloud environment. Moreover, as data can be associated to multiple copies, different protection requirements can be set for each of these copies, which may lead the initial data owner lose control on the data protection. To overcome these limits, we propose a Data centered Usage based Protection model relying on an IS description model to set a consistent protection for data assets. Protection means are defined according to both organizational and technical risks. To this end, we propose a GDPR compliant security and extended usage ontology which is used to define usage-control assertions coupling usage rights to security countermeasures so that data assets can be efficiently protected according to both organizational and technical dimensions. Thanks to a Blockchain-based usage control, our Data centered and Usage based Protection architecture also allows tracking the way assets are used so their life-long protection can be checked.; Protecting Information Systems (IS) relies traditionally on security risk analysis methods. Designed for well-perimetrised environments, these methods rely on a systematic identification of threats and vulnerabilities to identify efficient control-centered protection countermeasures. Unfortunately, this does not fit security challenges carried out by the opened and agile organizations provided by the Social, Mobile, big data Analytics, Cloud and Internet of Things (SMACIT) environment. Due to their inherently collaborative and distributed organization, such multi-tenancy systems require the integration of contextual vulnerabilities, depending on the a priori unknown way of using, storing and exchanging data in opened cloud environment. Moreover, as data can be associated to multiple copies, different protection requirements can be set for each of these copies, which may lead the initial data owner lose control on the data protection. To overcome these limits, we propose a Data centered Usage based Protection model relying on an IS description model to set a consistent protection for data assets. Protection means are defined according to both organizational and technical risks. To this end, we propose a GDPR compliant security and extended usage ontology which is used to define usage-control assertions coupling usage rights to security countermeasures so that data assets can be efficiently protected according to both organizational and technical dimensions. Thanks to a Blockchain-based usage control, our Data centered and Usage based Protection architecture also allows tracking the way assets are used so their life-long protection can be checked.

International audience; Microservices are hailed for their capabilities to tackle the challenge of breaking monolithic business systems down into small, cohesive, and loosely-coupled services. Indeed, these systems are neither easy to maintain nor to replace undermining organizations' efforts to cope with user's changing needs and governments' complex regulations. Microservices constitute an architectural style for developing a new generation of systems as a suite of services that, although they are separate, engage in collaborative execution and communication sessions. However, microservices success depends, among many other things, on the existence of an approach that would automatically identify the necessary microservices according to organizations' requirements. In this paper, we present such an approach and demonstrate its technical doability in the context of a case study, Bicing, for renting bikes. Some salient features of this approach are business processes as input for the identification needs, three models known as control, data, and semantic to capture dependencies between these processes' activities, and, finally, a collaborative clustering technique that recommends potential microservices. Conducted experiments in the context of Bicing clearly indicate that our approach outperforms similar ones for microservices identification and reinforce the important role of business processes in this identification. The approach constitutes a major milestone towards a better architectural style for future microservices systems.

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.

International audience; Protecting Information Systems (IS) relies traditionally on security risk anal-ysis methods. Designed for well-perimetrised environments, these methods rely on a systematic identification of threats and vulnarabilities to identify efficient control-centered protection countermeasures. Unfortunately, this does not fit security challenges carried out by the opened and agile organiza-tions provided by the Social, Mobile, big data Analytics, Cloud and Internet of Things (SMACIT) environment. Due to their inherently collaborative and distributed organization, such multi-tenancy systems require the integration of contextual vulnerabilities, depending on the a priori unknown way of us-ing, storing and exchanging data in opened cloud environment. Moreover, as data can be associated to multiple copies, different protection requirements can be set for each of these copies, which may lead the initial data owner lose control on the data protection. This involves (1) turning the traditional control-centered security vision to a dynamic data-centered protection and even (2) considering that the way a data is used can be a potential threat that may corrupt data protection efficiency. To fit these challenges, we propose a Data-centric Usage-based Protection service (DUP). This service is based on an information system meta-model, used to identify formally data assets and store the processes using copies of these assets. To define a usage-entered protection, we extend the Usage Based Access Control model, which is mostly focused on managing CRUD operations, to more complex operation fitting the SMACIT context. These usage rules are used to generate smart contracts, storing usage consents and managing usage control for cloud ser-vices.

The efficient evaluation of top- k queries is crucial for many applications where a huge quantity of data should be ranked and sorted to return the best answers to users in a reasonable time. Examples include, e-commerce platforms (e.g., amazon.com), multimedia sharing platforms, web databases, etc. Most often, these applications need to retrieve data from autonomous data sources. The access to these data sources is carried out through popular Web APIs, such as data web services, to provide a standard way to interact with data. In this context, users’ queries often require the composition of multiple data services to be answered. Most of existing solutions for the evaluation of top- k queries assume data services to provide both sorted and random accesses to data or only a sorted access. In practice, however, some services may provide only a random access to data, which could impact the performance of the solutions. In this paper, we propose an approach to optimize the evaluation of top- k queries over data services. We consider the worst case scenario when services provide only a random access to data. Our approach defines two strategies: Pipeline Parallel Strategy and Necessary Invocation Principle to reduce the composition processing time and the number of unnecessary service invocations. Conducted experiments showcased the scalability and efficiency of our solution.

Data provenance is a fundamental concept in scientific experimentation in general and complex event processing (CEP) in particular. For accurate determination and visualization of data provenance, efficient and user-friendly mechanisms are needed. Research in CEP optimization and visual notations can help in this process. This paper presents the extension of an optimized CEP framework to respond to data provenance requests. The extension consists in enriching the formal representation of execution plans of CEP queries to make them provenance-aware. These provenance-aware execution plans are then queried to generate a visual representation of the provenance data. We present the implementation of this framework and then its deployment and the associated evaluation in the context of an industrial use case.

This paper presents a novel way to address the “silo” obstacle that is making things play a limited role in the Internet-of-Things (IoT) arena since they mainly act as data suppliers. This novel way capitalizes on the concept of storytelling to connect things together, which leads to the formation of Process-of-Things (PoT). In compliance with the movie industry’s practices, a PoT has a script that identifies the necessary characters (actors) along with what they are expected to do, when, and where. Things, specialized into living and non-living, embody the characters during the shooting (filming) of scripts. 5 phases ensure the completion of this embodiment with emphasis on rehearsal and evaluation stages, in this paper, to guarantee character readiness and appropriateness, respectively. A system implementing PoT’s 5 phases is presented in this paper along with a usability study summarizing how end-users, namely farmers, experienced the system.

International audience; In service-oriented environments, abstract business processes can be implemented by concrete services to build complex applications. Given global user constraints, service selection allows to identify the best combination of services with respect to the business constraints. Generally, the selection problem is challenging, but it is particularly complex when dealing with QoS (quality of service) values, which can change during the time associated with temporal constraints. Indeed, these constraints make the selection problem heavily constrained, which can present a barrier for enabling effective service selection. Unlike static QoS values which have been deeply studied in the existing service selection approaches, time-dependent QoS associated with temporal constraints are insufficiently taken into consideration. Moreover, existing approaches cannot handle heavily constrained problems and usually do not provide strategies to detect the source of failure in order to enhance the selection problem in case there is no solution. In this paper, we introduce a new service selection approach, while considering time-dependent QoS values associated with temporal constraints. First, pruning techniques are proposed. The aim of the pruning process is twofold: (1) it allows for reducing the search space and thus, enhancing the efficiency of the selection process; (2) it allows for improving the selection problem by detecting at earlier stages the possible causes of failure, even before the selection process. Second, based on the pruning phase, improvement techniques are proposed to identify possible actions for finding a solution. Third, an exact and an approximate service selection algorithms under several constraints are given. Finally, we demonstrate the effectiveness of our approach through experimental results.

International audience; 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 automaticidentification 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.

To fit the renewed globalized economic environment, enterprises, and mostly SMEs, have to develop new networked and collaborative strategies, focusing on networked value creation (instead of the classical value chain vision), fitting the blue ocean context for innovative products and service development. Even if collaborative organizations have been studied for decades, the closer connection of information systems involved by the so-called “Industry 4.0” developed by leading industries in Europe, US and Asia requires to set new IT models to support agile and evolving collaborative Business Process (BP) enactment, integrating both traditional Information Systems (IS) and production control processes. By now, these product/service ecosystems are mostly supported by software services, which span multiple organizations and providers, and on multiple cloud-based execution environments, increasing the call for openness, agility, interoperability and trust for both production and Information System organization. These requirements are well supported by SOA, Web 2.0 and XaaS technologies for Information Systems. Taking advantage of IoT, services and Cloud technologies, the development of Cloud of Things (CoT) changes the way control application are engineered and developed moving from a dedicated design and development of control applications to a Control as a Service vision. This vision requires developing a new architecture to connect physical and logical objects as well as integrating basic control patterns to organize a consistent control service orchestration. To fit this challenge, we propose a multi-layer Control as a Service architecture to describe control systems in a holistic way. Our Control service model is built according to an event-driven orchestration strategy. Thanks to the integration of a context manager, analyzing continuously the system environment as well as the control system behavior, these context-aware control services can be deployed; Pour s’adapter au contexte de l’économie globalisée, les entreprises, et principalement les PMEs doivent développer de nouvelles stratégies de collaboration. Ces stratégies sont axées sur la création de valeur en réseau en remplacement de l’organisation classique de chaîne de valeur, s’adaptant ainsi au modèle dit de « Blue Ocean » qui conduit au développement de produits et services innovants. Bien que les organisations collaboratives aient été étudiées depuis des décennies, l’Industrie 4.0, actuellement largement développée par les principales industries en Europe, Amérique ou Asie, impose une intégration plus poussée des Systèmes d’Information pour y inclure des processus opérationnels collaboratifs, tant pour les activités administratives que pour la production. En outre, ces processus doivent être adaptatifs pour s’adapter au contexte. Actuellement, ces écosystèmes de produits/services sont principalement implémentés par des services logiciels, déployés sur le Cloud et utilisables par différentes organisations. Pour répondre aux besoins d’agilité, d’ouverture, d’interopérabilité et de confiance, ces services utilisent largement les architectures orientées services, les technologies Web2.0 et XaaS. Tirant parti de l’IoT, des technologies de services et du Cloud, le Cloud des objets (Cloud of Things ou CoT) change la manière de concevoir des applications de contrôle, passant d’une ingénierie traditionnelle à une vision de composition de services. Cette vision suppose de définir une nouvelle architecture pour connecter les objets physiques, leur double virtuel et intégrer des patrons de contrôle pour composer et orchestrer ces services pour répondre aux besoins. Pour répondre à ce défi, nous proposons uns architecture multi-niveaux de Control as a Service permettant de décrire les systèmes de contrôle selon une vision holistique. Notre modèle de service de contrôle est construit pour permettre une exécution pilotée par les événements. L’intégration d’un gestionnaire de contexte, analysant continuellement l’environnement d’exécution et le comportement du système, permet d’assurer le déploiement de services de contrôle contextualisables.

To fit the renewed globalized economic environment, enterprises, and mostly SMEs, have to develop new networked and collaborative strategies, focusing on networked value creation (instead of the classical value chain vision), fitting the blue ocean context for innovative products and service development. Even if collaborative organizations have been studied for decades, the closer connection of information systems involved by the so-called “Industry 4.0” developed by leading industries in Europe, US and Asia requires to set new IT models to support agile and evolving collaborative Business Process (BP) enactment, integrating both traditional Information Systems (IS) and production control processes. By now, these product/service ecosystems are mostly supported by software services, which span multiple organizations and providers, and on multiple cloud-based execution environments, increasing the call for openness, agility, interoperability and trust for both production and Information System organization. These requirements are well supported by SOA, Web 2.0 and XaaS technologies for Information Systems. Taking advantage of IoT, services and Cloud technologies, the development of Cloud of Things (CoT) changes the way control application are engineered and developed moving from a dedicated design and development of control applications to a Control as a Service vision. This vision requires developing a new architecture to connect physical and logical objects as well as integrating basic control patterns to organize a consistent control service orchestration. To fit this challenge, we propose a multi-layer Control as a Service architecture to describe control systems in a holistic way. Our Control service model is built according to an event-driven orchestration strategy. Thanks to the integration of a context manager, analyzing continuously the system environment as well as the control system behavior, these context-aware control services can be deployed; Pour s’adapter au contexte de l’économie globalisée, les entreprises, et principalement les PMEs doivent développer de nouvelles stratégies de collaboration. Ces stratégies sont axées sur la création de valeur en réseau en remplacement de l’organisation classique de chaîne de valeur, s’adaptant ainsi au modèle dit de « Blue Ocean » qui conduit au développement de produits et services innovants. Bien que les organisations collaboratives aient été étudiées depuis des décennies, l’Industrie 4.0, actuellement largement développée par les principales industries en Europe, Amérique ou Asie, impose une intégration plus poussée des Systèmes d’Information pour y inclure des processus opérationnels collaboratifs, tant pour les activités administratives que pour la production. En outre, ces processus doivent être adaptatifs pour s’adapter au contexte. Actuellement, ces écosystèmes de produits/services sont principalement implémentés par des services logiciels, déployés sur le Cloud et utilisables par différentes organisations. Pour répondre aux besoins d’agilité, d’ouverture, d’interopérabilité et de confiance, ces services utilisent largement les architectures orientées services, les technologies Web2.0 et XaaS. Tirant parti de l’IoT, des technologies de services et du Cloud, le Cloud des objets (Cloud of Things ou CoT) change la manière de concevoir des applications de contrôle, passant d’une ingénierie traditionnelle à une vision de composition de services. Cette vision suppose de définir une nouvelle architecture pour connecter les objets physiques, leur double virtuel et intégrer des patrons de contrôle pour composer et orchestrer ces services pour répondre aux besoins. Pour répondre à ce défi, nous proposons uns architecture multi-niveaux de Control as a Service permettant de décrire les systèmes de contrôle selon une vision holistique. Notre modèle de service de contrôle est construit pour permettre une exécution pilotée par les événements. L’intégration d’un gestionnaire de contexte, analysant continuellement l’environnement d’exécution et le comportement du système, permet d’assurer le déploiement de services de contrôle contextualisables.

International audience; The Internet-of-things, which designates the interconnection of physical objects, is a growing research area with many challenges. One of these challenges is the management of failures and unforeseen situations when using connected devices in real world applications. To this end, the authors propose a self-adaptation framework to deal with changes and take into account storage, computational and communication constraints. Their self-adaptation framework relies on constraints expressed in terms of quality of service (QoS) and event-driven rules to specify control objectives. Internally, the framework generates labelled transitions systems and builds on the fly synchronous controllers to guarantee QoS properties. Moreover, their framework has capabilities to concurrently deal with dynamic control objectives, monitoring and self-adaptation. To prove the practicality of their framework, they present a healthcare case-study to remotely monitor a patient at risk of myocardial infarction recurrence. The objective control rules easily specify how wearable devices should coordinate their behaviours to ensure safety, resilience and health-awareness factors. The implementation of their framework is fully distributed and scalable and include their development of a wearable, remotely configurable, QoS aware cardiac activity sensor.

International audience