Conceptual data modeling for realizing context-aware services

Recently, context-aware applications, which provide customized services using personalized contextual information, have gained much attention. However, core IT technologies for realizing context-aware services are not sufficient to support the actual development of context-aware applications. Especially, it is very hard to find studies on context-aware data modeling that can provide different responses even for the same information according to users’ situation. In practice, most context-aware services are managing contextual information in the phase of application development in a rather ad-hoc way. Unfortunately, this approach has an obvious limitation of expansibility when the amount of rapidly increasing contextual information is considered. In this paper, we first define the concept of contextual dependency among data objects. Second, we provide a context-aware data modeling methodology for modeling contextual information into the Entity-Relationship Diagram (ERD, for short) based on contextual dependency. Finally, we design a simple scenario for context-aware services, construct an ERD from the scenario based on the discovered design rules, and implement a prototype of the service.     

Toward context-aware computing: experiences and lessons

To minimize distractions, a pervasive-computing environment must be context-aware. The authors define an activity-attention framework for context-aware computing, discuss the spatial and temporal aspects of applications they developed, and introduce a pervasive-computing architecture.     

Toward an OSGi-based infrastructure for context-aware applications

Applications and services must adapt to changing contexts in dynamic environments. However, building context-aware applications is still complex and time-consuming due to inadequate infrastructure support. We propose a context-aware infrastructure for building and rapidly prototyping such applications in a smart-home environment. This OSGi-based infrastructure manages context-aware services reliably and securely and efficiently supports context acquisition, discovery, and reasoning. A formal, ontology-based context model enables semantic context representation, reasoning, and knowledge sharing. We propose an ontology-based context model that leverages Semantic Web technology and OWL (Web Ontology Language). OWL is an ontology markup language that enables context sharing and context reasoning. Based on our context model, we also propose a service-oriented context-aware middleware (SOCAM) architecture, including a set of independent services that perform context discovery, acquisition, and interpretation.     

The ECORA framework: A hybrid architecture for context-oriented pervasive computing

An infrastructure approach to support context-aware pervasive computing is advantageous for rapid prototyping of context-aware distributed applications and beneficial for unifying modelling of context and reasoning in uncertain conditions. This paper presents the ECORA framework for context-aware computing, which is designed with a focus on reasoning about context under uncertainty and addressing issues of heterogeneity, scalability, communication and usability. The framework follows an agent-oriented hybrid approach, combining centralized reasoning services with context-aware, reasoning capable mobile software agents. The use of a centralized reasoning engine provides powerful reasoning capabilities and deploying context-aware mobile agents enables agility and robustness of components in the pervasive system. The design and implementation of the framework at different levels, as well as three case studies, are presented.     

情境计算研究综述

作为一种新的计算模式,情境计算得到了学术界和产业界越来越多的关注.随着物联网、云计算、大数据、社会计算等相关技术的不断发展成熟,情境计算进入了快速发展阶段.情境计算是一种通过对获取到的情境信息进行处理、从而得出用户所需服务并主动向用户提供相应情境感知服务的计算模式.这一新的计算模式为使用者的工作、生活带来了舒适和便利.对情境计算的诞生背景进行阐述,介绍情境、情境计算、情境感知、情境感知系统和情境感知服务等关键概念,总结情境数据获取、情境模型与建模、情境推理、主动服务提供、情境感知中间件和安全与隐私等重要研究内容以及其中使用到的关键技术,最后依托情境计算的一般性架构分析得出情境计算的未来发展挑战.     

Context-awareness in wearable and ubiquitous computing

A common focus shared by researchers in mobile, ubiquitous and wearable computing is the attempt to break away from the traditional desktop computing paradigm. Computational services need to become as mobile as their users. Whether that service mobility is achieved by equipping the user with computational power or by instrumenting the environment, all services need to be extended to take advantage of the constantly changing context in which they are accessed. This paper will report on work carried out by the Future Computing Environments Group at Georgia Tech to provide infrastructure for context-aware computing. We will describe some of the fundamental issues involved in contextaware computing, solutions we have generated to provide a flexible infrastructure and several applications that take advantage of context awareness to allow freedom from traditional desktop computing.     

Usability study of ME2.0

Mobile context-aware applications execute in the background of hosts mobile devices. The applications source process and aggregate hosts’ contextual and personal information. This information is disclosed to ubiquitously pervasive services that adapt their offerings to individual preferences. Unfortunately, many developers continue to ignore the user perspective in context-aware application designs as they complicate their overall task and generate exponential requirements. The additional incorporation of privacy mechanisms in context-aware applications to safeguard context and personal information disclosures also complicates users’ tasks resulting to misconfigured or completely abandoned applications. Misconfigured applications give end-users a false assurance of privacy exposing them to comprising services. We present a usability study on Mobile Electronic Personality Version 2 a privacy enhanced context-aware mobile application for personalising ubiquitous services and adapting pervasive smart-spaces. We draw conclusions on key issues related to user needs, based on user interviews, surveys, prototypes and field evaluations. Users’ needs are evaluated against five themes, learn-ability, efficiency, memorability, errors, satisfaction and privacy contention. In addition, design layout preferences, privacy manageability and consensus design comprehension are also evaluated. Clarity of priorities in context-aware mobile applications shaped by usability studies effectively increases the acceptance of levels of potential users.     

MobiPADS: a reflective middleware for context-aware mobile computing

Traditionally, middleware technologies, such as CORBA, Java RMI, and Microsoft's DCOM, have provided a set of distributed computing services that essentially abstract the underlying network services to a monolithic "black box." In a mobile operating environment, the fundamental assumption of middleware abstracting a unified distributed service for all types of applications operating over a static network infrastructure is no longer valid. In particular, mobile applications are not able to leverage the benefits of adaptive computing to optimize its computation based on current contextual situations. In this paper, we introduce the Mobile Platform for Actively Deployable Service (MobiPADS) system. MobiPADS is designed to support context-aware processing by providing an executing platform to enable active service deployment and reconfiguration of the service composition in response to environments of varying contexts. Unlike most mobile middleware, MobiPADS supports dynamic adaptation at both the middleware and application layers to provide flexible configuration of resources to optimize the operations of mobile applications. Within the MobiPADS system, services (known as mobilets) are configured as chained service objects to provide augmented services to the underlying mobile applications so as to alleviate the adverse conditions of a wireless environment.     

An Adaptive Middleware for Context-Sensitive Communications for Real-Time Applications in Ubiquitous Computing Environments

Context-sensitivity is an important expected capability in applications in ubiquitous computing (ubicomp) environments. These applications need to use different contextual information from the user, host device, on board sensors, network, and the ambient environments to systematically adapt their actions. In addition, some context-sensitive applications may use specific contextual conditions to trigger impromptu and possibly short-lived interactions with applications in other devices. This property, referred to as context-sensitive or context-aware communications, allows applications to form short-range mobile ad hoc networks consisting of mobile and stationary devices, sensors, and other computing resources. Real-time applications, especially those having reactive behavior, running on embedded devices and requiring context-sensitive communications support, pose new challenges related to systematic representation of specific contexts, associations of contexts with real-time actions, timely context data collection and propagation, and transparent context-sensitive connection establishment. An object-based middleware can be effective to meet these challenges if such a middleware can provide a well-defined development framework as well as lightweight runtime services. In this paper, an adaptive and object-based middleware, called reconfigurable context-sensitive middleware (RCSM) is presented to facilitate context-sensitive communications in ubicomp environments. To facilitates context-sensitive communications, RCSM provides a context-aware interface definition language for specifying context-sensitive interfaces of real-time objects, an object container framework for generating interfaces-specific context-analyzers, and a context-sensitive object request broker for context-sensitive object discovery and impromptu connection management. RCSM is adaptive in the sense that depending on the context-sensitive behavior of the applications, it adapts its object discovery and connection management mechanisms.     

Modeling and generating context-aware agent-based applications with amended colored Petri nets

Context-awareness is become more crucial in mobile distributed computing systems. However, sophisticated modeling methods to analyze context-aware systems are still very few. Among those, the Colored Petri Net (CPN) is promising because it is proven to be useful for modeling system dynamics and concurrency control in more efficient ways. However, to support managing multiple configurations of components of context-aware applications, some features need to be added to specialize the CPNs. To address these challenges, our research has two idea: (a) to decompose a system into several meaningful subsystems, each of which we will call a pattern, and (b) to separate context from the patterns to realize context-pattern independence. Hence, we propose a modeling methodology to represent and analyze a context-aware agent-based system, which tends to be highly complex. We introduce CPNs as a method of capturing the dynamics of this contextual change. We define CPNs and a way to apply them in context-aware agent-based systems. We also describe a prototype system that we have developed called CPN Generator, which translates CPN specification into Java programs.     

基于主动推理的情境感知系统框架

情境感知计算是普及计算引发的新的研究领域。在分析情境感知计算系统模型的基础上，研究并改进了系统开发模型CTK，提出基于主动推理的系统框架ACTK，以智能家庭原型的实现证明了它对情境感知应用的开发和研究更好的支持。     

以数据为中心的智慧城市研究综述

在城市信息化浪潮与数据科学崛起的共同推动下,智慧城市开始在全球范围内成为未来城市发展的新理念与新实践.大数据、数据活化、数据挖掘等数据管理、应用与分析技术在智慧城市建设当中具有核心作用.站在信息科学的视角之上,围绕以数据为中心这一主题,对当前智慧城市研究工作的最新动态进行了综述.梳理了当前智慧城市相关研究中广泛采用的城市数据类型及其特点,并从相关研究工作和技术与研究特点两个大的方面对该领域的研究工作现状进行了介绍.其中相关研究涵盖了技术体系研究、数据驱动的智能交通、城市计算技术和城市人类活动的统计力学等方面.而技术与研究特点的介绍包括核心技术与理论,以及领域研究的学科交叉、城市数据为中心、区域特性等方面.最后对该研究领域未来可能的发展方向进行了总结和展望.     

普适计算环境下基于构件化中间件的上下文管理技术研究

上下文相关技术已经成为普适计算环境中用于应用服务的不可或缺的核心技术之一。对于基于构件的中间件来说,对高性能的上下文管理的需求更是必不可少,因为它构成了普适计算环境中构件适配和部署的重要基础。因此,在已有构件中间件的基础上,我们针对普适计算环境的特点提出了一种整体的上下文管理方法,将上下文管理集成在已有的构件化中间件中。应用表明,该方法能够更好地支持普适计算环境下上下文相关的构件化应用开发。     

Contextual music information retrieval and recommendation: State of the art and challenges

Increasing amount of online music content has opened new opportunities for implementing new effective information access services–commonly known as music recommender systems–that support music navigation, discovery, sharing, and formation of user communities. In the recent years a new research area of contextual (or situational) music recommendation and retrieval has emerged. The basic idea is to retrieve and suggest music depending on the user’s actual situation, for instance emotional state, or any other contextual conditions that might influence the user’s perception of music. Despite the high potential of such idea, the development of real-world applications that retrieve or recommend music depending on the user’s context is still in its early stages. This survey illustrates various tools and techniques that can be used for addressing the research challenges posed by context-aware music retrieval and recommendation. This survey covers a broad range of topics, starting from classical music information retrieval (MIR) and recommender system (RS) techniques, and then focusing on context-aware music applications as well as the newer trends of affective and social computing applied to the music domain.     

A comprehensive approach to model and use context for adapting applications in pervasive environments

With an increasing diversity of pervasive computing devices integrated in our surroundings and an increasing mobility of users, it will be important for computer systems and applications to be context-aware. Lots of works have already been done in this direction on how to capture context data and how to carry it to the application. Among the remaining challenges are to create the intelligence to analyze the context information and deduce the meaning out of it, and to integrate it into adaptable applications. Our work focuses on these challenges by defining generic context storage and processing model and by studying its impact on the application core. We propose a reusable context ontology model that is based on two levels: a generic level and a domain specific level. We propose a generic adaptation framework to guarantee adaptation of applications to the context in a pervasive computing environment. We also introduce a comprehensive adaptation approach that involves content adaptation and presentation adaptation inline with the adaptation of the core services of applications. Our case study shows that the context model and the application adaptation strategies provide promising service architecture.     

Service-oriented middleware for distributed data mining on the grid

Distribution of data and computation allows for solving larger problems and executing applications that are distributed in nature. The grid is a distributed computing infrastructure that enables coordinated resource sharing within dynamic organizations consisting of individuals, institutions, and resources. The grid extends the distributed and parallel computing paradigms allowing for resource negotiation and dynamical allocation, heterogeneity, open protocols, and services. Grid environments can be used both for compute-intensive tasks and data intensive applications by exploiting their resources, services, and data access mechanisms. Data mining algorithms and knowledge discovery processes are both compute and data intensive, therefore the grid can offer a computing and data management infrastructure for supporting decentralized and parallel data analysis. This paper discusses how grid computing can be used to support distributed data mining. Research activities in grid-based data mining and some challenges in this area are presented along with some promising future directions for developing grid-based distributed data mining.     

Distributed data mining patterns and services: an architecture and experiments

Distributed data mining implements techniques for analyzing data on distributed computing systems by exploiting data distribution and parallel algorithms. The grid is a computing infrastructure for implementing distributed high‐performance applications and solving complex problems, offering effective support to the implementation and use of data mining and knowledge discovery systems. The Web Services Resource Framework has become the standard for the implementation of grid services and applications, and it can be exploited for developing high‐level services for distributed data mining applications. This paper describes how distributed data mining patterns, such as collective learning, ensemble learning, and meta‐learning models, can be implemented as Web Services Resource Framework mining services by exploiting the grid infrastructure. The goal of this work was to design a distributed architectural model that can be exploited for different distributed mining patterns deployed as grid services for the analysis of dispersed data sources. In order to validate such an approach, we presented also the implementation of two clustering algorithms on the developed architecture. In particular, the distributed k‐means and distributed expectation maximization were exploited as pilot examples to show the suitability of the implemented service‐oriented framework. An extensive evaluation of its performance was provided. Copyright © 2011 John Wiley & Sons, Ltd.     

A portable toolkit for supporting end-user personalization and control in context-aware applications

A context-aware application in the pervasive computing environment provides intuitive user centric services using implicit context cues. Personalization and control are important issues for this class of application as they enable end-users to understand and configure the behavior of an application. However most development efforts for building context-aware applications focus on the sensor fusion and machine learning algorithms to generate and distribute context cues that drive the application with little emphasis on user-centric issues. We argue that, to elevate user experiences with context-aware applications, it is very important to address these personalization and control issues at the system interface level in parallel to context centric design. Towards this direction, we present Persona, a toolkit that provides support for extending context-aware applications with end-user personalization and control features. Specifically, Persona exposes a few application programming interfaces that abstract end-user customization and control mechanisms and enables developers to integrate these user-centric aspects with rest of the application seamlessly. There are two primary advantages of Persona. First, it can be used with various existing middlewares as a ready-to-use plug-in to build customizable and controllable context-aware applications. Second, existing context-aware applications can easily be augmented to provide end-user personalization and control support. In this paper, we discuss the design and implementation of Persona and demonstrate its usefulness through the development and augmentation of a range of common context-aware applications.