Requirements for Context-aware MapReduce on Pervasive Grids

Deliverable 3.1PER-MARE project aims at adapting MapReduce distribution to pervasive grids. Such adaptation includes implementing context-awareness capabilities into Hadoop distribution. The main purpose of this deliverable is to analyze the requirements for including such support for context-awareness on Hadoop. Context-awareness can be defined as the ability of a system to adapt its operations to the current context, aiming at increasing usability and effectiveness by taking environmental context into account. Such environment corresponds, in the case of PER-MARE project, to a pervasive grid environment and its dynamic resources. It is then important to analyze such environment and its main characteristics, as well as the impact of observing such environment on Hadoop distribution. Indeed, performance of MapReduce applications should not be negatively impacted by context- awareness capabilities, which makes important to carefully consider context-awareness needs and implications of Hadoop distribution and on MapReduce application over pervasive grids. This deliverable intends to point out a set of requirements for context-aware support on PER-MARE project. These requirements will guide subsequent proposals on this domain

Strong Consistency for Shared Objects in Pervasive Grids

International audienceRecent advances in communication technology en- able the emergence of a new generation of applications that integrates mobile devices with classical high performance systems as part of a common computing environment. In such environ- ments, keeping the coherence of shared data (distributed objects, for example) represents a real challenge as communications are strongly influenced by the performance and the reliability of mobile devices (laptops, PDAs and cellular telephones) and wireless networks (WiFi, Bluetooth). Indeed, data incoherence may arise due to message losses or node volatility, which blocks the algorithms used to synchronize these data. In this paper, we analyze the main challenges concerning the manipulation of shared distributed objects in a pervasive environment. We demonstrate how a membership service can be enhanced to tolerate temporary disconnections and message losses without blocking, while reducing the number of exchanged message

Enriched Semantic Service Description for Service Discovery: Bringing Context to Intentional Services

International audienceIn service-orientation, the notion of service is studied from different point of views. On the one hand, several approaches have been proposing services that are able to adapt themselves according to the context in which they are used. On the other hand, some researches have been proposing to consider user intentions when proposing business services. We believe that these two views are complementary. An intention is only meaningful when considering the context in which it emerges. Conversely, context description is only meaningful when associated with a user intention. In order to take profit of both views, we propose to extend the Ontology Web Language for services description (OWL-S). We include on it both the specification of context associated with the service and the intention that characterize it. This extended description is experimented in a semantic registry that we built for service discovery purposes. Such registry considers a matching algorithm, which exploits the extended description. Then, we present experimental results of this matching algorithm that demonstrates the advantages one may have on using the proposed descriptor. Thus, we propose a new vision of service orientation taking into account the notion of intention and context. This new vision is based on the extended semantic descriptor, which is necessary in order to enhance transparency of the system by proposing to the user the most appropriate service

Bringing context to intentional services

International audienceIn service-orientation, the notion of service is used in different views. On the one hand, several approaches have been proposing services that are able to adapt themselves according to the context in which they are used. On the other hand, some researches have been proposing to consider user's goals when proposing business services. We believe that these two views are complementary. A goal is only meaningful when considering the context in which it emerges, and conversely, context description is only meaningful when associated with a user goal. In order to take profit of both views, we propose to extend the OWL-S service description by including on it both the specification of context associated with the service and the goal that characterize it

Service discovery and prediction on Pervasive Information System

International audienceRecent evolution of technology and its usages, such as BYOD (Bring Your Own Device) and IoT (Internet of Things), transformed the way we interact with Information Systems (IS), leading to a new generation of IS, called the Pervasive Information Systems (PIS). These systems have to face heterogeneous pervasive environments and hide the complexity of such environment end-user. In order to reach transparency and proactivity necessary for successful PIS, new discovery and prediction mechanisms are necessary. In this paper, we present a new user-centric approach for PIS and propose new service discovery and prediction based on both user's context and intentions. Intentions allow focusing on goals user wants to satisfy when requesting a service. Those intentions rise in a given context, which influence the service implementation. We propose a service discovery mechanism that observes user's context and intention in order to offer him/her the most appropriate service satisfying her/his intention on the current context. We also propose a prediction mechanism that tries to anticipate user's intentions considering the user's history and the observed context. We evaluate both mechanisms and discuss advanced features future PIS will have to deal with

A context-aware intentional service prediction mechanism in PIS

International audiencePervasive Information System (PIS) represents a new generation of Information Systems (IS) available anytime, anywhere in a pervasive environment. In this paper, we propose to enhance PIS transparency and efficiency through a context-aware intentional service prediction approach. This approach allows anticipating user's future needs, offering and recommending him the most suitable service in a transparent and discrete way. We detail in this paper our service prediction mechanism and present encouraging experimental results demonstrating our proposition

The influence of context on intentional service

International audienceSeveral service-oriented approaches promote the intention concept as a way to describe and document services based on user's requirements. However, these approaches have two main limitations: (1) they don't take into account the fact that a user evolves in a context that can influence his intentions, and (2) at the software service level, the corresponding intentional description of these software services is missing. Such a description should be a high level one, which is not directly connected to the software services. The objective of the paper is to propose a semantic service description that considers both intention corresponding to the service and context in which it is supposed to emerge. In addition, the variability embedded in the intentional description can be also affected by the user context. Such influence is also considered in our proposition

Context-Aware Service Selection with Uncertain Context Information

The current evolution of Service-Oriented Computing in ubiquitous systems is leading to the development of context-aware services. These are services whose description is enriched with context information related to the service execution environment and adaptation capabilities. This information is often used for discovery and adaptation purposes. However, in real-life systems context information is naturally dynamic, uncertain and incomplete, which represents an important issue when comparing service description and user requirements. Uncertainty of context information may lead to an inexact match between provided and required service capabilities, and consequently to the non-selection of services. In order to handle uncertain and incomplete context information, we propose a mechanism inspired by graph-comparison for matching contextual service descriptions using similarity measures that allow inexact matching. Service description and requirements are compared using two kinds of similarity measures: local measures, which compare individually required and provided properties, and global measures, which take into account the context description as a whole. We show how the proposed mechanism is integrated in MUSIC, an existing adaptation middleware, and how it enables more optimal adaptation decision making

Refinement Strategies for Correlating Context and User Behavior in Pervasive Information Systems

International audienceLarge amounts of traces can be collected by Pervasive Information Systems, reflecting user's actions and the context in which these actions have been performed (location, date, time, network connection, etc.). This article proposes refinement strategies with different frequency measurements on contextual elements in order to better analyze the impact of these elements on the user's behavior. These strategies are based on data mining and Formal Concept Analysis and used to refine input data in order to identify the context elements that have a strong impact on user behaviors. We go further on context analysis by cognizing FCA with semantic distance measures calculated based on a context ontology. The proposed context analysis is further on evaluated in experiments with real data. The novelties of this work lies on these refinement strategies which can lead to a better understanding of context impact. Such understanding represents an important step towards personalization and recommendation features

Unified and Conceptual Context Analysis in Ubiquitous Environments

International audienceThis article presents an original approach for the analysis of context information in ubiquitous environments. Large volumes of heterogeneous data are now collected, such as location, temperature, etc. This "environmental" context may be enriched by data related to users, e.g., their activities or applications. We propose a unified analysis and correlation of all these dimensions of context in order to measure their impact on user activities. Formal Concept Analysis and association rules are used to discover non-trivial relationships between context elements and activities, which, otherwise, could seem independent. Our goal is to make an optimal use of available data in order to understand user behavior and eventually make recommendations. In this paper, we describe our general methodology for context analysis and we illustrate it on an experiment conducted on real data collected by a capture system. Thanks to this methodology, it is possible to identify correlation between context elements and user applications, making possible to recommend such applications for user in similar situations