Designing context-sensitive systems: An integrated approach

Vieira, Vaninha; Tedesco, Patrícia; Salgado, Ana Carolina

doi:10.1016/j.eswa.2010.05.006

Cited by 67 publications

(69 citation statements)

References 43 publications

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“…Notable among them are those of Schilit [18], Dey [5], Viera [20], Zimmermann [22] and the work of Patrick Brezillon, a summary of which can be found in [13]. One of the problems with the term Context is the overloaded use of it.…”

Section: Related Workmentioning

confidence: 99%

Discovering Context for Real-World Events

Satish¹,

Jain²,

Gupta³

2018

MUC

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ABSTRACT. Specifying the search space is an important step in designing multimedia annotation systems. With the large amount of data available from sensors and web services, context-aware approaches for pruning search spaces are becoming increasingly common. In these approaches, the search space is limited by the contextual information obtained from a fixed set of sources. For example, a system for tagging faces in photos might rely on a static list of candidates obtained from the photo owner's Facebook profile. These contextual sources can become extremely large, which leads to lower accuracy in the annotation problem. We present Context Discovery Algorithm, a technique to progressively discover the most relevant search space from a dynamic set of context sources. This allows us to reap the benefits of context, while keeping the size of the search space within limits. As a concrete application for our approach, we present a simple photo management application, which tags faces of people in a user's personal photos. We empirically study the role of our framework in the face tagging application to tag photos taken at real-world events, such as conferences, weddings or social gatherings. Our results show that the availability of event context, and its dynamic discovery, can produce 97.5% smaller search spaces with at least 93% correct tags. RÉSUMÉ. Spécifier un espace de recherche représente une étape importante dans la conception de systèmes d'annotation multi-média. Avec une grande quantité de données provenant de capteurs et de services Web, les approches sensible au contexte deviennent de plus en plus usuelles pour élaguer les espaces de recherche. Dans ces approches, l'espace de recherche est limité par les informations contextuelles qui sont obtenues à partir d'un ensemble donné de sources. Par exemple, un système pour marquer les visages dans des photos pourrait reposer sur une liste statique de candidats obtenus à partir de photos de personnes sur leur profil FaceBook. Ces sources contextuelles peuvent devenir très volumineuses, ce qui peut conduire à une précision plus faible dans le problème des annotations. Nous présentons un nouvel algorithme de découverte du contexte, une technique pour découvrir progressivement l'espace de recherche le plus pertinent pour un ensemble dynamique de sources contextuelles. Ceci nous permet de recueillir les bénéfices du contexte tout en gardant la taille de l'espace de recherche à une taille raisonnable. Comme concrète application de notre approche, nous présentons une application simple de management de photos, où les visages de personnes sont marqués à partir de photos privées d'un utilisateur. Nous étudions empiriquement le rôle de notre cadre de travail dans l'application de marquage de visages pour marquer des photos prises lors d'événements sociaux comme des conférences, des mariages ou de rassemblements sociaux. Nos résultats montrent que la disponibilité du contexte des événements et sa découverte dynamique peut produire des espaces de recherche plus petits de 97...

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Section: Related Workmentioning

confidence: 99%

Discovering Context for Real-World Events

Satish¹,

Jain²,

Gupta³

2018

MUC

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“…Moreover, a contextual element is stable and can be set at designated time, whereas context is dynamic and must be constructed at runtime, when the interaction occurs. [26] metamodel defines the concept of context, but it was not specifically suited for business processes; therefore, it does not make explicit the relationships between the relevant concepts and the domain of a business. Furthermore, in order to use the metamodel in a real domain, it is necessary to extend the concept of contextual element to the specific domain concepts.…”

Section: Model Contextmentioning

confidence: 99%

“…Our proposal is based on [26], who adopted the definition of context proposed by [4] and distinguished between the concepts of contextual element and context: (i) a contextual element is any piece of data or information that allows an entity to be characterized within a domain; and (ii) context is the set of instantiated contextual elements that are needed to support a task performed by an agent (human or software). Additionally, the elements that compose context have a relevant relationship with the task that an agent is performing.…”

Section: Model Contextmentioning

confidence: 99%

Modeling and Using Context in Business Process Management: A Research Agenda

Santoro¹,

Baião²,

Revoredo³

et al. 2017

MUC

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ABSTRACT. Business Process Management (BPM) is the art and science of monitoring how the work is performed within an organization to ensure consistent results and opportunities for improvement. One important research topic on BPM relates to the issue of flexibility. Unplanned conditions may occur at any time during process execution. In dynamic environments, changes must be performed more frequently and systematically and considering not only pre-established rules, but also contextual information. The literature indicates context as a source of information that should be considered in the modeling of business processes in order to contribute to their flexibility when in the execution phase. In this paper, we present the results from our studies about context in the whole cycle of BPM. The goal is to describe an approach that comprises solutions for modeling, gathering, using and evolving context in a BPM oriented environment.

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“…Accordingly, context guides the variations in application's behavior, enriching the user interaction either by influencing recommendations or by enabling adaptations of any kind (Vieira et al, 2011).…”

Section: Context-sensitive Applicationsmentioning

confidence: 99%