Agent-based architectural framework enhancing configurability,autonomy and scalability of context-aware pervasive services

Multi-agent software architectures have gained in popularity due to their beneficial behavior in designing and implementing sophisticated applications. However, current approaches in implementing such architectures have led to application-specific, non-scalable implementations which limit the reusablity and improvement of the whole architecture. Moreover, these attempts lack features to enhance the user experience, thus slowing the adoption of the resulting services. In this paper we describe a fully-fledged multi-agent architecture covering a large variety of preferred features including capabilities of ‘plugging’ ubiquitous services, servicing mobile users, interconnecting remote similar architectures and interfacing with advanced software components such as knowledge bases. This framework exploits a wide-range of context-aware components making it essentially context-aware, allowing for the development of ubiquitous context-aware human-centric services, which are the focus of our research. To illustrate the flexibility of this architectural framework, we present four services which were built using this architectural paradigm by different development teams and elaborate on their overall behavior.     

一种面向情境的情境感知应用编程框架

随着信息技术的快速发展,各种各样的环境信息影响着我们生活中的各种决策,因此提供一种智能化的应用程序,能够根据用户当前的情境信息为用户提供所需要的最适合的服务变得越来越重要。许多遗产软件并不支持情境感知功能,目前的一些主要的编程语言和编程框架并没有提供一个很好的方式来支持这种情境感知应用程序的开发。提出了一个新的面向情境的编程框架,支持情境信息的定义、封装、发布,支持对应用行为的驱动和约束,允许应用快速响应非预期情境。通过该框架可以快速开发情境感知应用,也可以以非侵入的方式改造遗产软件,使之具有情境感知能力。     

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.     

Context modelling and a context-aware framework for pervasive service creation: A model-driven approach

Pervasive service creation entails a complex process that involves a diversity of development aspects. Context-awareness is an important facet of pervasive service creation, which deals with the acquisition, rendering, representation and utilisation of context information. In this paper we tackle context-awareness at the application level dealing with the representation and utilisation of context by services. We propose a model-driven approach that facilitates the creation of a context modelling framework and simplifies the design and implementation of pervasive services. To conclude, we demonstrate the benefits of our model-driven approach via the creation of a pervasive museum service and its evaluation using selected software metrics.     

A context-aware multimedia framework toward personal social network services

People use various Internet-based services including social networks to conduct tasks of diversified categories such as informational, professional, educational, hobbies, health, recreational, academic, and news. Many people not only maintain association with a number of different services but also share information with their social ties in their daily life. However, a person only consumes a subset of services and needs to communicate with a subset of social ties at any given context. In this paper, we present a framework called SenseFace that leverages sensory data coming from one’s body sensor network and multimedia information contained within Internet-based services to recommend context-aware services and community of interest. We present the detailed design and implementation of the framework and share our preliminary test results.     

Information dissemination framework for context-aware products

In recent years, some scholars claimed the usage of intelligent products to make systems more efficient throughout the Product Life Cycle (PLC). Integrating intelligence and information into products themselves is possible with, among others, auto-ID technologies (barcode, RFID, …). In this paper, a new kind of intelligent product is introduced, referred to as “communicating material” paradigm. Through this paradigm, a product is (i) capable of embedding information on all or parts of the material that it is made of and (ii) capable of undergoing physical transformations without losing its communication ability and the data that is stored on it. This new material is used in our study to convey information between the different actors of the PLC, thus improving data interoperability, availability and sustainability. Although “communicating materials” provide new abilities compared to conventional products, they still have low memory capacities compared to product databases that become larger and larger. An information dissemination framework is developed in this paper to select the appropriate information to be stored on the product, at different stages of the PLC. This appropriateness is based on a degree of data relevance, which is computed by taking into account the context of use of the product (actor’s expectations, environment, …). This framework also provides the tools to split information on all or parts of the material. A case study is presented, which aims at embedding context-sensitive information on “communicating textiles”.     

General factorization framework for context-aware recommendations

Context-aware recommendation algorithms focus on refining recommendations by considering additional information, available to the system. This topic has gained a lot of attention recently. Among others, several factorization methods were proposed to solve the problem, although most of them assume explicit feedback which strongly limits their real-world applicability. While these algorithms apply various loss functions and optimization strategies, the preference modeling under context is less explored due to the lack of tools allowing for easy experimentation with various models. As context dimensions are introduced beyond users and items, the space of possible preference models and the importance of proper modeling largely increases. In this paper we propose a general factorization framework (GFF), a single flexible algorithm that takes the preference model as an input and computes latent feature matrices for the input dimensions. GFF allows us to easily experiment with various linear models on any context-aware recommendation task, be it explicit or implicit feedback based. The scaling properties makes it usable under real life circumstances as well. We demonstrate the framework’s potential by exploring various preference models on a 4-dimensional context-aware problem with contexts that are available for almost any real life datasets. We show in our experiments—performed on five real life, implicit feedback datasets—that proper preference modelling significantly increases recommendation accuracy, and previously unused models outperform the traditional ones. Novel models in GFF also outperform state-of-the-art factorization algorithms. We also extend the method to be fully compliant to the Multidimensional Dataspace Model, one of the most extensive data models of context-enriched data. Extended GFF allows the seamless incorporation of information into the factorization framework beyond context, like item metadata, social networks, session information, etc. Preliminary experiments show great potential of this capability.     

An adaptive middleware framework for context-aware applications

We describe a middleware framework for the adaptive delivery of context information to context-aware applications. The framework abstracts the applications from the sensors that provide context. Further applications define utility functions on the quality of context attributes that describe the context providers. Then, given multiple alternatives for providing the same type of context, the middleware applies the utility function to each alternative and choose the one with maximum utility. By allowing applications to delegate the selection of context source to the middleware, our middleware can implement autonomic properties, such as self-configuration when new context providers appear and resilience to failures of context providers.

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Modular framework support for context-aware mobile cinema

In this paper we describe a novel approach for interactive cinema based on context-aware narration using handheld computers. The paper describes both the artistic approach and the ubiquitous computing framework developed to realize the scenario. This framework has been used in various projects, including the described video production course at the ISNM, where five interactive cinema concepts have been developed and shown during a public demonstration. In our approach, a new type of user experience has been established by placing the viewer inside the movie’s physical locations during playback. Moreover, the developed ubiquitous computing framework provides a foundation for future work in the area of ad-hoc, service-oriented Ubicomp scenarios.     

RecAm: a collaborative context-aware framework for multimedia recommendations in an ambient intelligence environment

With an ever-increasing accessibility to different multimedia contents in real-time, it is difficult for users to identify the proper resources from such a vast number of choices. By utilizing the user’s context while consuming diverse multimedia contents, we can identify different personal preferences and settings. However, there is a need to reinforce the recommendation process in a systematic way, with context-adaptive information. The contributions of this paper are twofold. First, we propose a framework, called RecAm, which enables the collection of contextual information and the delivery of resulted recommendation by adapting the user’s environment using Ambient Intelligent (AmI) Interfaces. Second, we propose a recommendation model that establishes a bridge between the multimedia resources, user joint preferences, and the detected contextual information. Hence, we obtain a comprehensive view of the user’s context, as well as provide a personalized environment to deliver the feedback. We demonstrate the feasibility of RecAm with two prototypes applications that use contextual information for recommendations. The offline experiment conducted shows the improvement of delivering personalized recommendations based on the user’s context on two real-world datasets.     

GAT: Platform for automatic context-aware mobile services for m-tourism

Despite the recent advances in mobile tourism systems, most of the wayfinding applications have still to deal with some problems: a huge amount of tourist information to manage, guidance for indoor and outdoor environments, and the need of users to have programming knowledge about many mobile phone platforms. In this study, we propose the GAT platform to overcome these problems. In GAT, users are able to generate wayfinding applications for indoor and outdoor environments through a web form without the need for programming skills, assisted by a system of automatic generation and update of points of interest.     

E-MIIM: an ensemble-learning-based context-aware mobile telephony model for intelligent interruption management

AI & SOCIETY - Nowadays, mobile telephony interruptions in our daily life activities are common because of the inappropriate ringing notifications of incoming phone calls in different contexts....     

A context-aware scheme for privacy-preserving location-based services

We address issues related to privacy protection in location-based services (LBSs). Most existing privacy-preserving LBS techniques either require a trusted third-party (anonymizer) or use cryptographic protocols that are computationally and communicationally expensive. Our design of privacy-preserving techniques is principled on not requiring a trusted third-party while being highly efficient in terms of time and space complexities. The problem has two interesting and challenging characteristics: First, the degree of privacy protection and LBS accuracy depends on the context, such as population and road density, around a user’s location. Second, an adversary may violate a user’s location privacy in two ways: (i) based on the user’s location information contained in the LBS query payload and (ii) by inferring a user’s geographical location based on the device’s IP address. To address these challenges, we introduce CAP, a context-aware privacy-preserving LBS system with integrated protection for both data privacy and communication anonymity. We have implemented CAP and integrated it with Google Maps, a popular LBS system. Theoretical analysis and experimental results validate CAP’s effectiveness on privacy protection, LBS accuracy, and communication QoS (Quality-of-Service).     

A web services framework for distributed model management

Distributed model management aims to support the wide-spread sharing and usage of decision support models. Web services is a promising technology for supporting distributed model management activities such as model creation and delivery, model composition, model execution and model maintenance to fulfill dynamic decision-support and problem solving requests. We propose a web services based framework for model management (called MM-WS) to support various activities of the model management life cycle. The framework is based on the recently proposed Integrated Service Planning and Execution (ISP & E) approach for web services integration. We discuss encoding of domain knowledge (as individual models) and utilize the MM-WS framework to interleave synthesis of composite models with their execution. A prototypical implementation with an example is used to illustrate the utility of the framework to enable distributed model management and knowledge integration. Benefits and issues of using the framework to support model-based decision-making in organizational contexts are outlined.

An application framework for mobile,context-aware trails

In this paper we describe the design, implementation and evaluation of a software framework that supports the development of mobile, context-aware trails-based applications. A trail is a contextually scheduled collection of activities and represents a generic model that can be used to satisfy the activity management requirements of a wide range of context-based time management applications. Trails overcome limitations with traditional time management techniques based on static to-do lists by dynamically reordering activities based on emergent context.     

Toward a cooperative programming framework for context-aware applications

OPEN is an ontology-based programming framework for rapid prototyping, sharing, and personalization of context-aware applications. Unlike previous systems that provide programming support for single group of users, OPEN provides different programming support for users with diverse technical skills. According to the programming requirements of different users, several cooperation patterns are identified, and the mechanisms to facilitate resource sharing and reuse are built into the framework. Three corresponding programming modes are elaborated by showing how a context-aware game has been developed with the support of the OPEN framework, and the usability of our system is validated through an initial user study.     

Towards a programming framework for activity-oriented context-aware applications

Context-aware system is an emerging research area in recent years. Context plays an important role in these systems. In most existing work, context is treated as all relative elements in the environment of an application, and the scope of context is predefined by the developers during the development. However, it is difficult to analyze, specify, and organize everything in the environment accurately and completely; and even when it is possible, the developed applications are difficult to extend or modify as the requests for environment may change over time. In this paper, we focus on activity-oriented context-aware (AOCA) applications where the requests for environment are highly dependent on user activities, and propose a programming framework for developing AOCA applications. In particular, we first present a concept model for describing the notions of activity-oriented context. Next, based on the concept model, we describe the details of the programming framework as well as a development tool. Moreover, we provide a platform to support the runtime of AOCA applications, and demonstrate the advantages of our programming framework through experimental evaluations.     

Hybreed: A software framework for developing context-aware hybrid recommender systems

This article introduces Hybreed, a software framework for building complex context-aware applications, together with a set of components that are specifically targeted at developing hybrid, context-aware recommender systems. Hybreed is based on a concept for processing context that we call dynamic contextualization. The underlying notion of context is very generic, enabling application developers to exploit sensor-based physical factors as well as factors derived from user models or user interaction. This approach is well aligned with context definitions that emphasize the dynamic and activity-oriented nature of context. As an extension of the generic framework, we describe Hybreed RecViews, a set of components facilitating the development of context-aware and hybrid recommender systems. With Hybreed and RecViews, developers can rapidly develop context-aware applications that generate recommendations for both individual users and groups. The framework provides a range of recommendation algorithms and strategies for producing group recommendations as well as templates for combining different methods into hybrid recommenders. Hybreed also provides means for integrating existing user or product data from external sources such as social networks. It combines aspects known from context processing frameworks with features of state-of-the-art recommender system frameworks, aspects that have been addressed only separately in previous research. To our knowledge, Hybreed is the first framework to cover all these aspects in an integrated manner. To evaluate the framework and its conceptual foundation, we verified its capabilities in three different use cases. The evaluation also comprises a comparative assessment of Hybreed’s functional features, a comparison to existing frameworks, and a user study assessing its usability for developers. The results of this study indicate that Hybreed is intuitive to use and extend by developers.