mSpace Mobile: a UI Gestalt to Support On-the-Go Info-Interaction

mSpace Mobile Interaction presents a UI gestalt of 7 techniques for mobile/on-the-move information retrieval and assessment that enables multiple views of the information within a persistent focus+context viewer. It uses the web but breaks the web page paradigm to support effective rapid triage

Universal Mobile Information Retrieval

International audienceThe shift in human computer interaction from desktop computing to mobile interaction highly influences the needs for new designed interfaces. In this paper, we address the issue of searching for information on mobile devices, an area also known as Mobile Information Retrieval. In particular, we propose to summarize as much as possible the information retrieved by any search engine to allow universal access to information

An Investigation into Mobile Based Approach for Healthcare Activities, Occupational Therapy System

This research is to design and optimize the high quality of mobile apps, especially for iOS. The objective of this research is to develop a mobile system for Occupational therapy specialists to access and retrieval information. The investigation identifies the key points of using mobile-D agile methodology in mobile application development. It considers current applications within a different platform. It achieves new apps (OTS) for the health care activities

Mining user activity as a context source for search and retrieval

Nowadays in information retrieval it is generally accepted that if we can better understand the context of users then this could help the search process, either at indexing time by including more metadata or at retrieval time by better modelling the user context. In this work we explore how activity recognition from tri-axial accelerometers can be employed to model a user's activity as a means of enabling context-aware information retrieval. In this paper we discuss how we can gather user activity automatically as a context source from a wearable mobile device and we evaluate the accuracy of our proposed user activity recognition algorithm. Our technique can recognise four kinds of activities which can be used to model part of an individual's current context. We discuss promising experimental results, possible approaches to improve our algorithms, and the impact of this work in modelling user context toward enhanced search and retrieval

Context modelling for just-in-time mobile information retrieval (JIT-MobIR)

Mobile users have the capability of accessing information anywhere at any time with the introduction of mobile browsers and mobile web search. However, the current mobile browsers are implemented without considering the characteristics of mobile searches. As a result, mobile users need to devote time and effort in order to retrieve relevant information from the web in mobile devices. On the other hand, mobile users often request information related to their surroundings, which is also known as context. This recognizes the importance of including context in information retrieval. Besides, the availability of the embedded sensors in mobile devices has supported the recognition of context. In this study, the context acquisition and utilization for mobile information retrieval are proposed. The "just-in-time" approach is exploited in which the information that is relevant to a user is retrieved without the user requesting it. This will reduce the mobile user's effort, time and interaction when retrieving information in mobile devices. In this paper, the context dimensions and context model are presented. Simple experiments are shown where user context is predicted using the context model

Calendar based contextual information as an Internet content pre-caching tool

Motivated by the need to access internet content on mobile devices with expensive or non-existent network access, this paper discusses the possibility for contextual information extracted from electronic calendars to be used as sources for Internet content predictive retrieval (pre-caching). Our results show that calendar based contextual information is useful for this purpose and that calendar based information can produce web queries that are relevant to the users' task supportive information needs