47,329 research outputs found
Towards Proactive Context-aware Computing and Systems
A primary goal of context-aware systems is delivering the right information at the
right place and right time to users in order to enable them to make effective decisions and improve their quality of life. There are three key requirements for achieving this goal:
determining what information is relevant, personalizing it based on the usersā context (location, preferences, behavioral history etc.), and delivering it to them in a timely manner without an explicit request from them. These requirements create a paradigm that we term as āProactive Context-aware Computingā.
Most of the existing context-aware systems fulfill only a subset of these requirements.
Many of these systems focus only on personalization of the requested information
based on usersā current context. Moreover, they are often designed for specific domains.
In addition, most of the existing systems are reactive - the users request for some information and the system delivers it to them. These systems are not proactive i.e. they cannot anticipate usersā intent and behavior and act proactively without an explicit request from them. In order to overcome these limitations, we need to conduct a deeper analysis and enhance our understanding of context-aware systems that are generic, universal, proactive and applicable to a wide variety of domains.
To support this dissertation, we explore several directions. Clearly the most significant
sources of information about users today are smartphones. A large amount of usersā context can be acquired through them and they can be used as an effective means
to deliver information to users. In addition, social media such as Facebook, Flickr and
Foursquare provide a rich and powerful platform to mine usersā interests, preferences and behavioral history. We employ the ubiquity of smartphones and the wealth of information available from social media to address the challenge of building proactive context-aware systems. We have implemented and evaluated a few approaches, including some as part of the Rover framework, to achieve the paradigm of Proactive Context-aware Computing. Rover is a context-aware research platform which has been evolving for the last 6 years.
Since location is one of the most important context for users, we have developed
āLocusā, an indoor localization, tracking and navigation system for multi-story buildings.
Other important dimensions of usersā context include the activities that they are engaged
in. To this end, we have developed āSenseMeā, a system that leverages the smartphone and its multiple sensors in order to perform multidimensional context and activity recognition for users. As part of the āSenseMeā project, we also conducted an exploratory study of privacy, trust, risks and other concerns of users with smart phone based personal sensing systems and applications.
To determine what information would be relevant to usersā situations, we have developed āTellMeā - a system that employs a new, flexible and scalable approach based on Natural Language Processing techniques to perform bootstrapped discovery and ranking of relevant information in context-aware systems. In order to personalize the relevant information, we have also developed an algorithm and system for mining a broad range of usersā preferences from their social network profiles and activities. For recommending new information to the users based on their past behavior and context history (such as visited locations, activities and time), we have developed a recommender system and approach for performing multi-dimensional collaborative recommendations using tensor factorization.
For timely delivery of personalized and relevant information, it is essential to anticipate
and predict usersā behavior. To this end, we have developed a unified infrastructure,
within the Rover framework, and implemented several novel approaches and algorithms
that employ various contextual features and state of the art machine learning techniques
for building diverse behavioral models of users. Examples of generated models include
classifying usersā semantic places and mobility states, predicting their availability for accepting calls on smartphones and inferring their device charging behavior. Finally, to
enable proactivity in context-aware systems, we have also developed a planning framework based on HTN planning. Together, these works provide a major push in the direction of proactive context-aware computing
Moving Towards a Distributed Network of Proactive, Self-Adaptive and Context-Aware Systems
Instead of being static and waiting passively for instructions, software systems are required to take a more proactive approach in their behavior in order to anticipate and to adapt to the needs of their users. To design and develop such systems in an affordable, predictable and timely manner is a great engineering challenge. Even though there have been notable steps towards distributed self-adaptive and context-aware systems, there is still a lack of methodologies on how to model and implement applications which have to distribute and to manage large amounts of information. In this work-in-progress, we address this issue by proposing a self-adaptive and context-aware model with a structure that allows the system to learn from the userās behavior by using Proactive Computing. The novelty comes from the possibility of having a distributed network of Proactive Engines in which the exchange of contextual information would help each system to take smart decisions
Context for Ubiquitous Data Management
In response to the advance of ubiquitous computing technologies, we believe that for computer systems to be ubiquitous, they must be context-aware. In this paper, we address the impact of context-awareness on ubiquitous data management. To do this, we overview different characteristics of context in order to develop a clear understanding of context, as well as its implications and requirements for context-aware data management. References to recent research activities and applicable techniques are also provided
CAMMD: Context Aware Mobile Medical Devices
Telemedicine applications on a medical practitioners mobile device should be context-aware. This can vastly improve the effectiveness of mobile applications and is a step towards realising the vision of a ubiquitous telemedicine environment. The nomadic nature of a medical practitioner emphasises location, activity and time as key context-aware elements. An intelligent middleware is needed to effectively interpret and exploit these contextual elements. This paper proposes an agent-based architectural solution called Context-Aware Mobile Medical Devices (CAMMD). This framework can proactively communicate patient records to a portable device based upon the active context of its medical practitioner. An expert system is utilised to cross-reference the context-aware data of location and time against a practitioners work schedule. This proactive distribution of medical data enhances the usability and portability of mobile medical devices. The proposed methodology alleviates constraints on memory storage and enhances user interaction with the handheld device. The framework also improves utilisation of network bandwidth resources. An experimental prototype is presented highlighting the potential of this approach
A Model for Using Physiological Conditions for Proactive Tourist Recommendations
Mobile proactive tourist recommender systems can support tourists by
recommending the best choice depending on different contexts related to herself
and the environment. In this paper, we propose to utilize wearable sensors to
gather health information about a tourist and use them for recommending tourist
activities. We discuss a range of wearable devices, sensors to infer
physiological conditions of the users, and exemplify the feasibility using a
popular self-quantification mobile app. Our main contribution then comprises a
data model to derive relations between the parameters measured by the wearable
sensors, such as heart rate, body temperature, blood pressure, and use them to
infer the physiological condition of a user. This model can then be used to
derive classes of tourist activities that determine which items should be
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A monitoring approach for runtime service discovery
Effective runtime service discovery requires identification of services based on different service characteristics such as structural, behavioural, quality, and contextual characteristics. However, current service registries guarantee services described in terms of structural and sometimes quality characteristics and, therefore, it is not always possible to assume that services in them will have all the characteristics required for effective service discovery. In this paper, we describe a monitor-based runtime service discovery framework called MoRSeD. The framework supports service discovery in both push and pull modes of query execution. The push mode of query execution is performed in parallel to the execution of a service-based system, in a proactive way. Both types of queries are specified in a query language called SerDiQueL that allows the representation of structural, behavioral, quality, and contextual conditions of services to be identified. The framework uses a monitor component to verify if behavioral and contextual conditions in the queries can be satisfied by services, based on translations of these conditions into properties represented in event calculus, and verification of the satisfiability of these properties against services. The monitor is also used to support identification that services participating in a service-based system are unavailable, and identification of changes in the behavioral and contextual characteristics of the services. A prototype implementation of the framework has been developed. The framework has been evaluated in terms of comparison of its performance when using and when not using the monitor component
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