Facilitating dynamic web service composition with fine-granularity context management

Context is an important factor for the success of dynamic service composition. Although many contextbased AI or workflow approaches have been proposed to support dynamic service composition, there is still an unaddressed issue of the support of fine-granularity context management. In this paper, we propose a granularity-based context model together with an approach to supporting the intelligent context-aware service composing problem. The corresponding case study is provided to show the validity of our approach.<br /

Dynamic Model-based Management of Service-Oriented Infrastructure.

Models are an effective tool for systems and software design. They allow software architects to abstract from the non-relevant details. Those qualities are also useful for the technical management of networks, systems and software, such as those that compose service oriented architectures. Models can provide a set of well-defined abstractions over the distributed heterogeneous service infrastructure that enable its automated management. We propose to use the managed system as a source of dynamically generated runtime models, and decompose management processes into a composition of model transformations. We have created an autonomic service deployment and configuration architecture that obtains, analyzes, and transforms system models to apply the required actions, while being oblivious to the low-level details. An instrumentation layer automatically builds these models and interprets the planned management actions to the system. We illustrate these concepts with a distributed service update operation

Building a Context World for Dynamic Service Composition

Dynamic service composition requires responding and adapting to changes in the computing environment when orchestrating existing services into one or more new services that fit better to a composite application. This paper abstracts the changes of the environment as a context world to store the physical contexts of the computing environment, user profiles and computed results of services as well. We use ontology techniques to model the domain concepts of application contexts. Context Condition/Effect Description Language is designed to describe the dynamic semantics of the requirements and capabilities of goals and services in a concise and editable manner. Goal-driven and planning techniques are used to dynamically implement the service composition according to the domain knowledge and facts in the context world. ?2010 IEEE.EI

A Context Model for Service Composition Based on Dynamic Description Logic

Abstract: A service composition task for service broker is to discovery and compose provider&apos;s services to satisfy user&apos;s request. Many researchers model the context utilizing ontology-based or attribute-based method to assist service composition. We propose a new context model by combining the context logic with the dynamic description logic (DDL), where user&apos; context, provider&apos;s context and broker&apos;s context are described by DDL separately and reasoned under the context logic. The reasoning results finally can be used to discovery and compose services intelligently. We evaluate this model on a simple, yet realistic example, and the results show that our context model provides a practical solution

日本のEFL教室におけるデータ駆動型学習利用に関するメタ分析

In this study, a meta-analysis was conducted targeting the studies that employ data-driven learning (DDL) approach in the Japanese EFL classroom context. After a thorough literature search, 32 effect sizes from 14 primary studies that took place in the Japanese EFL classroom were retrieved, coded, and calculated. The synthesized results, based on the classification of the outcome measures, showed that the DDL approach worked well particularly for learning vocabulary items (Level 1: lemma). It also worked positively for basic grammar items (Level 2:category) and noun and verb phrases (Level 3: phrase). For a proficiency measure, the combined effect size was small. Accordingly, the results of the current metaanalysis would provide further support for the use of DDL approach in the classroom, which could be an alternative methodology for facilitating the learning of lexico-grammatical items. Suggestions for further research and pedagogical implications are provided.This study was supported by JSPS KAKENHI Grant Numbers 26704006 and 25284108

Delivering IoT Services in Smart Cities and Environmental Monitoring through Collective Awareness, Mobile Crowdsensing and Open Data

The Internet of Things (IoT) is the paradigm that allows us to interact with the real world by means of networking-enabled devices and convert physical phenomena into valuable digital knowledge. Such a rapidly evolving field leveraged the explosion of a number of technologies, standards and platforms. Consequently, different IoT ecosystems behave as closed islands and do not interoperate with each other, thus the potential of the number of connected objects in the world is far from being totally unleashed. Typically, research efforts in tackling such challenge tend to propose a new IoT platforms or standards, however, such solutions find obstacles in keeping up the pace at which the field is evolving. Our work is different, in that it originates from the following observation: in use cases that depend on common phenomena such as Smart Cities or environmental monitoring a lot of useful data for applications is already in place somewhere or devices capable of collecting such data are already deployed. For such scenarios, we propose and study the use of Collective Awareness Paradigms (CAP), which offload data collection to a crowd of participants. We bring three main contributions: we study the feasibility of using Open Data coming from heterogeneous sources, focusing particularly on crowdsourced and user-contributed data that has the drawback of being incomplete and we then propose a State-of-the-Art algorith that automatically classifies raw crowdsourced sensor data; we design a data collection framework that uses Mobile Crowdsensing (MCS) and puts the participants and the stakeholders in a coordinated interaction together with a distributed data collection algorithm that prevents the users from collecting too much or too less data; (3) we design a Service Oriented Architecture that constitutes a unique interface to the raw data collected through CAPs through their aggregation into ad-hoc services, moreover, we provide a prototype implementation