Digital traces of our lives are now constantly produced by various connected devices, internet services and interactions. Our actions result in a multitude of heterogeneous data objects, or traces, kept in various locations in the cloud or on local devices. Users have very few tools to organize, understand, and search the digital traces they produce. We propose a simple but flexible data model to aggregate, organize, and find personal information within a collection of a user's personal digital traces. Our model uses as basic dimensions the six questions: what, when, where, who, why, and how. These natural questions model universal aspects of a personal data collection and serve as unifying features of each personal data object, regardless of its source. We propose indexing and search techniques to aid users in searching for their past information in their unified personal digital data sets using our model. Experiments performed over real user data from a variety of data sources such as Facebook, Dropbox, and Gmail show that our approach significantly improves search accuracy when compared with traditional search tools.
Contextual information has been considered in various computer science applications. Context-aware applications dynamically adapt to changes in the environment in which they are running: location, time, user profile, history. provide a thorough survey of context-aware models in @cite_21 . Truong and Dustdar survey context-aware Web-Service systems in @cite_10 . Context-awareness has become increasingly popular with the wide adoption of mobile devices. While the types of context these systems consider overlap with ours, the overall approach is different from ours, for instance a contextually-aware Information Retrieval system will use the current context (e.g., user location and time of day) to adjust search results @cite_31 . In contrast, we consider context as information that can be queried and used to guide the search.
@cite_31 @cite_21 @cite_10
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Context-awareness is becoming an important foundation of adaptive mobile systems, however, techniques for discovering contextually relevant Web content and Smart Devices (i.e., Smart Resources) remain consigned to small-scale deployments. To address this limitation, this paper introduces Ambient Ocean, a Web search engine for context-aware Smart Resource discovery. Ocean provides scalable mechanisms for supplementing Resources with expressive contextual metadata as a means of facilitating in-situ discovery and composition. Ocean supports queries based on arbitrary contextual data, such as location, biometric details, telemetry data, situational cues, sensor information, etc. Ocean utilizes a combination of crowd-sourcing, context-enhanced query expansion and personalization techniques to continually optimize query results over time. This paper presents Ocean's conceptual foundations, its reference implementation, and a preliminary evaluation that demonstrates significantly improved Smart Resource discovery results in real-world environments. Computing devices and applications are now used beyond the desktop, in diverse environments, and this trend toward ubiquitous computing is accelerating. One challenge that remains in this emerging research field is the ability to enhance the behavior of any application by informing it of the context of its use. By context, we refer to any information that characterizes a situation related to the interaction between humans, applications, and the surrounding environment. Context-aware applications promise richer and easier interaction, but the current state of research in this field is still far removed from that vision. This is due to 3 main problems: (a) the notion of context is still ill defined, (b) there is a lack of conceptual models and methods to help drive the design of context-aware applications, and (c) no tools are available to jump-start the development of context-aware applications. In this anchor article, we address these 3 problems in turn. We first define context, identify categories of contextual information, and characterize context-aware application behavior. Though the full impact of context-aware computing requires understanding very subtle and high-level notions of context, we are focusing our efforts on the pieces of context that can be inferred automatically from sensors in a physical environment. We then present a conceptual framework that separates the acquisition and representation of context from the delivery and reaction to context by a context-aware application. We have built a toolkit, the Context Toolkit, that instantiates this conceptual framework and supports the rapid development of a rich space of context-aware applications. We illustrate the usefulness of the conceptual framework by describing a number of context-aware applications that have been prototyped using the Context Toolkit. We also demonstrate how such a framework can support the investigation of important research challenges in the area of context-aware computing. Context-awareness is a critical aspect of safe navigation especially for the blind and visually impaired in unfamiliar environments. Existing mobile devices for context-aware navigation fall short in many cases due to their dependence on specific infrastructure requirements as well as having limited access to resources that could provide a wealth of contextual clues. In this paper, we propose a mobile-cloud collaborative approach for context-aware navigation by exploiting the computational power of resources made available by Cloud Computing providers as well as the wealth of location-specific resources available on the Internet. We propose an extensible system architecture that minimizes reliance on infrastructure, thus allowing for wide usability. We present a traffic light detector that we developed as an initial application component of the proposed system. We present preliminary results of experiments performed to test the appropriateness for the real-time nature of the application. Development of context-aware applications is inherently complex. These applications adapt to changing context information: physical context, computational context, and user context tasks. Context information is gathered from a variety of sources that differ in the quality of information they produce and that are often failure prone. The pervasive computing community increasingly understands that developing context-aware applications should be supported by adequate context information modelling and reasoning techniques. These techniques reduce the complexity of context-aware applications and improve their maintainability and evolvability. In this paper we discuss the requirements that context modelling and reasoning techniques should meet, including the modelling of a variety of context information types and their relationships, of situations as abstractions of context information facts, of histories of context information, and of uncertainty of context information. This discussion is followed by a description and comparison of current context modelling and reasoning techniques.