The context

Data repositories of complex organisations are often very large, and understanding which analytical queries are interesting for different kinds of decision makers, in the various possible situations, may be difficult. We propose to support the formulation of OLAP queries by using the knowledge of the context the decision maker is currently experiencing.

AbstractThe pervasive and ubiquitous computing is a computing paradigm that aims to integrate the real world with the virtual world so that is not perceived by users. One of the areas of study of this paradigm is the context-aware systems that are systems that perform some action after collecting information that characterizes a given context. The objective of this paper is to describe the specification of a framework that can be utilized for the development of new context-sensitive applications.KeywordsUbiquitous ComputingPervasive ComputingMobile ComputingAndroidiOSContext of UseCAMobile

With the development of Internet120, the information on the Web is more and more abundance. In this paper, combining Mobile Agent theory and Context-aware, a kind of Context-ware model based on Mobile Agent has been brought forward. The function of analyzing actual requirement of users which combines their context information, utilizing Mobile Agent to aware, process and manage context information, and providing precise knowledge to users are implemented. At last a Context-aware prototype system based on Mobile Agent which is on the Aglet software platform is realized, it can aware of user profile context and the experiment preferably educes the characteristic of Context-aware.

AbstractLately, recommendation systems have received significant attention. Most existing approaches though, recommend items of potential interest to users by completely ignoring the temporal aspects of ratings. In this paper, we argue that time-aware recommendations need to be pushed in the foreground. We introduce an extensive model for time-aware recommendations from two perspectives. From a fresh-based perspective, we propose using different aging schemes for decreasing the effect of historical ratings and increasing the influence of fresh and novel ratings. From a context-based perspective, we focus on providing different suggestions under different temporal specifications. To facilitate user browsing, we propose an effective presentation layer for time-aware recommendations based on user preferences and summaries for the suggested items. Our experiments with real movies ratings show that time plays an important role in the recommendation process.

Resolving non-deterministic choices of choreographies is a crucial task. We introduce a novel notion of realisability for choreographies ---called whole-spectrum implementation--- that rules out deterministic implementations of roles that, no matter which context they are placed in, will never follow one of the branches of a non-deterministic choice. We show that, under some conditions, it is decidable whether an implementation is whole-spectrum. As a case study, we analyse the POP protocol under the lens of whole-spectrum implementation.

Since the introduction of the ubiquitous computing vision by Mark Weiser, we observed quite some research from various R&D projects as well as groups that focused on different aspects in the area of context-awareness. However, there is yet no result which we can easily apply in a real environment. The context-aware service development and deployment process seldom focused on reusability and realistic implementation issues. We believe these issues can be tackled by proposing a complete context-aware system package. In this package both developers and users will be supported. This idea is realised at our department as a context-aware playground. This paper explains the observation on current work and the shortcomings, and proposes how the context-aware playground approach can be a solution.

Previously, we proposed two recommendation systems—context-aware information filtering (C-IF) and context-aware collaborative filtering (C-CF)—both of which are context-aware recommendation methods. We have also shown their effectiveness through of experiments using a restaurant recommendation system based on these methods. Furthermore, we need to rank the recommended contents to improve the performance of the C-IF and the C-CF. A ranking method ranks the recommended contents based on content parameters that the user regards as important. However, what parameter is important for the user is according to their contexts. For example, a user likes a reasonable restaurant when he is alone, but on the other hand, he likes an expensive and stylish restaurant when he is with his girlfriend. Therefore, it is important to consider his contexts when ranking recomended contents. In this study, we propose a context-aware ranking method. The system ranks recommended contents within users’ contexts. We also evaluate our proposal method from experimental results.

In this paper we present a context-sensing component that recognizes meetings in a typical office environment. Our prototype detects the meeting start and end by combining outputs from pressure and motion sensors installed on the chairs. We developed a telephone controller application that transfers incoming calls to voice-mail when the user is in a meeting. Our experiments show that it is feasible to detect high-level context changes with “good enough” accuracy, using low-cost, off-the-shelf hardware, and simple algorithms without complex training. We also note the need for better metrics to measure context detection performance, other than just accuracy. We propose several metrics appropriate for our application in this paper. It may be useful, however, for the community to define a set of general metrics as a basis to compare different approaches of context detection.

This paper proposes a context driven activity theory (CDAT) and reasoning approach for recognition of concurrent and interleaved complex activities of daily living (ADL) which involves no training and minimal annotation during the setup phase. We develop and validate our CDAT using the novel complex activity recognition algorithm on two users for three weeks. The algorithm accuracy reaches 88.5% for concurrent and interleaved activities. The inferencing of complex activities is performed online and mapped onto situations in near real-time mode. The developed systems performance is analyzed and its behavior evaluated.