C-ANIS: a Contextual, Automatic and Dynamic Service-Oriented Integration Framework

International audienceUbiquitous computing environments are highly dynamic by nature. Services provided by different devices can appear and disappear as, for example, devices join and leave these environments. This article contributes to the handling of this dynamicity by discussing service integration in the context of service-oriented architectures. We propose CANIS: a Contextual, Automatic and dyNamic Integration framework of Services. C-ANIS distinguishes two different approaches to service integration. Automatic integration automatically extends the capabilities of an existing service, leaving the interface unchanged. On-demand integration builds a new service on request from a list of given services. We have implemented C-ANIS based on the OSGi/Felix framework and thereby demonstrated the feasibility of these two service integration concepts. We have also implemented a toolkit providing two different techniques to realize the automatic and on-demand service integration concepts: Redirection, i.e. calling interfaces and replication, i.e. copying implementations of service

Interface Matching and Combining Techniques for Services Integration

International audienceThe development of many highly dynamic environments, like pervasive environments, introduces the possibility to use geographically close-related services. Dynamically integrating and unintegrating these services in running applications is a key challenge for this use. In this article, we classify service integration issues according to interfaces exported by services and internal combining techniques. We also propose a contextual integration service, IntegServ, and an interface, Integrable, for developing services

Intelligent assistant system as a context-aware decision-making support for the workers of the future

International audienceThe key role of information and communication technologies (ICT) to improve manufacturing productivity within the paradigm of factory of the future is often proved. These tools are used in a wide range of product lifecycle activities, from the early design phase to product recycling. Generally, the assistance tools are mainly dedicated to the management board and fewer initiatives focus on the operational needs of the worker at the shop-floor level. This paper proposes a context-aware knowledge-based system dedicated to support the actors of the factory by the right information at the right time and in the appropriate format regarding their context of work and level of expertise. Particularly, specific assistance functionalities are dedicated to the workers in charge of the machine configuration and the realization of manufacturing operations. PGD-based (Proper Generalized Decomposition) algorithms are used for real time simulation of industrial processes and machine configuration. At the conceptual level, a semantic model is proposedas key enablersfor the structuration of the knowledge-based system

Dynamic variability support in context-aware workflow-based systems

Workflow-based systems are increasingly becomingmore complex and dynamic. Besides the large sets of process variants to be managed, process variants need to be context sensitive in order to accommodate new user requirements and intrinsic complexity. This paradigm shift forces us to defer decisions to run time where process variants must be customized and executed based on a recognized context. However, few efforts have been focused on dynamic variability for process families. This dissertation proposes an approach for variant-rich workflow-based systems that can comprise context data while deferring process configuration to run time. Whereas existing early process variability approaches, like Worklets, VxBPEL, or Provop handle run-time reconfiguration, ours lets us resolve variants at execution time and supports multiple binding required for dynamic environments. Finally, unlike the specialized reconfiguration solutions for some workflow-based systems, our approach allows an automated decision making, enabling different run-time resolution strategies that intermix constraint solving and feature models. We achieve these results through a simple extension to BPMN that adds primitives for process variability constructs. We show that this is enough to eficiently model process variability while preserving separation of concerns. We implemented our approach in the LateVa framework and evaluated it using both synthetic and realworld scenarios. LateVa achieves a reasonable performance over runtime resolution, which means that can facilitate practical adoption in context-aware and variant-rich work ow-based systems

Islamic view towards Bitcoin

This paper proposes to analyze the agent behavior by means of big data extracted from the search engine « Google trends » and Twitter API to visualize the emotions and the manner of thinking about « Bitcoin » in the Islamic context. Two kinds of sentiment measures are constructed. The first is based on the search query of the word « Bitcoin » with religious connotation all over the world from 14/04/2017 to 14/04/2018 in weekly frequency. The second is built on twitter data from 03/04/2018 to 13/04/2018, by using a Bayesian machine learning device exploiting deep natural language processing modules to assign emotions and sentiment orientations. In the next step, the Granger causality analysis is used to investigate the hypothesis that this sentiment causes the volatility and the returns of « Bitcoin ». The results show that, at a first-level that twitter users of the word « Islamic Bitcoin » improve positive sentiment. Secondly, the Twitter sentiment measure has a significant effect on lagged Bitcoin returns and volatility. Furthermore, this sentimental variable Granger causes Bitcoin returns and volatility.  This study contributes to the literature by studying the influence of the doctrinal view towards Bitcoin on his prices dynamics. Knowing that Bitcoin is a new financial asset and there is a large debate on his compliance with sharia

Web service composition: A survey of techniques and tools

Web services are a consolidated reality of the modern Web with tremendous, increasing impact on everyday computing tasks. They turned the Web into the largest, most accepted, and most vivid distributed computing platform ever. Yet, the use and integration of Web services into composite services or applications, which is a highly sensible and conceptually non-trivial task, is still not unleashing its full magnitude of power. A consolidated analysis framework that advances the fundamental understanding of Web service composition building blocks in terms of concepts, models, languages, productivity support techniques, and tools is required. This framework is necessary to enable effective exploration, understanding, assessing, comparing, and selecting service composition models, languages, techniques, platforms, and tools. This article establishes such a framework and reviews the state of the art in service composition from an unprecedented, holistic perspective

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse