Eoin McLoughlin

Bus catcher: a context sensitive prototype system for public transportation users

In this paper we describe the architectural andfunctional characteristics of Bus Catcher, a contextsensitive prototype system for public transportation users.Bus Catcher assists mobile users in planning their busrides by providing timely and accurate information aboutcurrent bus locations and estimated arrival times. Acomplete report on the implementation together with apreliminary evaluation of the system is provided in thispaper.

MEDIC: MobilE Diagnosis for Improved Care

Hospitals everywhere are taking advantage of the flexibility and speed of wireless computing to improve the quality and reduce the cost of healthcare. Caregivers equipped with mobile computers now have levels of interaction at the bedside not possible with traditional paper charts, and they can access accurate real-time information (patient records, medication and medical imagery) at the point-of-care to make decisions, diagnose and treat patients with greater speed and efficiency. Greater and more immediate information access, however, is giving rise to challenges in how to effectively select and present the most relevant aspects for given patient care tasks, as well as how to take advantage of the collaborative opportunities afforded by medical community connection. We propose a system that enables doctors to efficiently query, analyse and annotate patient information, in particular medical imagery, using current mobile technologies. The system allows entire profiles with known diagnoses to be retrieved and can be used to compare diagnosis and treatments for patients with similar symptoms or care records. The application can be employed by caregivers either working in the hospital setting or working remotely and off-site as well as a useful tool to facilitate education and training of medical staff.

Capturing task knowledge for geo-spatial imagery

Geo-spatial image databases are employed in a wide range of applications, such as intelligence operations, recreational and professional mapping, urban and industrial planning, and tourism systems. Effective retrieval of relevant images from such digital libraries can employ knowledge about what an image contains, why image contents are important in a particular domain, and how specific images have been used for particular domain tasks. Approaches to annotation for multimedia information retrieval have typically focused on the first two types of knowledge; however, managing the knowledge implicit in using geo-spatial imagery to address particular tasks can be crucial for capturing and making the most effective use of organisational knowledge assets. We are developing case-based knowledge-management support for large geo-spatial image repositories that scaffolds task-based knowledge capture about a content-based sketch query mechanism. This paper describes our task-centric approach to image annotation and retrieval, and it presents our initial implementation of the approach.

A Case-Based Approach to Managing Geo-spatial Imagery Tasks *

Advances in technology for digital image capture and storage have caused an information overload problem in the geo-sciences. This has compounded existing image retrieval problems whereby most image matching is performed using content-based image retrieval techniques. The biggest problem in this field is the so-called semantic gap – the mismatch between the capabilities of current content-based image retrieval systems and the user needs. One way of addressing this problem is to develop context-based image retrieval methods. Context-based retrieval relies on knowledge about why image contents are important in a particular area and how specific images have been used to address particular tasks. We are developing a case-based knowledge- management retrieval system that employs a task-centric approach to capturing and reusing user context. This is achieved through image annotation and adaptive content presentation. In this paper we present an extension of a previous implementation of our approach and a thorough evaluation of our application.

Using sketches and knowledge bases for geo-spatial image retrieval

Abstract This paper presents research in the field of knowledge management for geo-spatial imagery including scanned aerial photos and satellite images. We have developed a web-based system that allows users to query a database of images not only using metadata, but also drawing sketches of configurations of objects they are interested in as well as inputting textual descriptions of their intended task. Our system integrates case-based reasoning techniques to form a knowledge base from previously issued queries that can be exploited to improve future query processing and to build organizational memory through experience capture. The effective design and implementation of a user-friendly graphic user interface plays an important role for the system to provide improved human–computer interaction and decision support.

Context-Oriented image retrieval

In order to help address problems of information overload in digital imagery task domains, we have developed an interactive approach to the capture and reuse of image context information. Our framework models different aspects of the relationship between images and the domain tasks that they support by monitoring the interactive manipulation and annotation of task-relevant imagery. In particular, a strong focus on task context serves to ground image annotations in domain specific goals. This contrasts with prevalent annotation schemes that focus on what individual images contain but that provide no context for which, if any, of those aspects are important to users. Our work attempts to leverage a measure of the users intentions with regard to tasks that they address. We analyze human-computer interaction information that enables us to infer why image contents are important in a particular context and how specific images have been used to address particular domain goals.

Contextual media integration and recommendation for mobile medical diagnosis

Hospitals everywhere are taking advantage of the flexibility and speed of wireless computing to improve the quality and reduce the cost of healthcare. Caregivers equipped with portable computers now have levels of interaction at the bedside not possible with paper charts and can leverage accurate real-time patient information at the point of care to diagnose and treat patients with greater speed and efficiency. We present a mobile medical application that integrates heterogenous medical media (e.g. textual patient case descriptions, relevant medical imagery, physician dictations and endoscopies) into encapsulated patient profiles. This paper provides an overview and initial evaluation of the MEDIC mobile healthcare recommender system that facilitates decision support for expert diagnosis.

Knowledge capture and reuse for geo-spatial imagery tasks

The continuously increasing amount and availability of geospatial image data is giving rise to problems of information overload in organisations that rely on digital geo-spatial imagery. Intelligent support for relevant image retrieval is needed in order to help manage large geospatial image libraries. Moreover, managing the knowledge implicit in using geo-spatial imagery to address particular tasks is crucial for capturing and making the most effective use of organisational knowledge assets. We are developing case-based knowledge-management support for large geo-spatial image repositories, which incorporates sketch-based querying for image retrieval; image manipulation and annotation tools for highlighting and composing relevant aspects of task-relevant imagery; and automatic context-based querying for retrieving relevant previous task experiences. This paper describes our approach to knowledge capture and reuse through task-based image annotation, and it introduces the environment we are developing for capture and reuse of task knowledge involving geo-spatial imagery.

Case-Based Decision Support for Intelligent Patient Knowledge Management

The majority of healthcare workers in hospitals continue to record, access and update important patient information using paper charts. Disparate patient data (clinical information, laboratory results and medical imagery) is entered by different caregivers and stored at different locations around the hospital. This is a cumbersome, time consuming process that can result in critical medical errors such as documents being mislaid or prescriptions being misinterpreted due to illegible handwriting. Hospitals everywhere are moving to integrate health data sources using Electronic Health Record (EHR) systems as well as taking advantage of the flexibility and speed of wireless computing to improve the quality and reduce the cost of healthcare. We are developing a mobile application that allows doctors to efficiently access accurate real-time patient information at the point-of-care. The system can assist caregivers in automatically searching through very large repositories of previous patient cases as increasingly large hospital databases are making manual searches of such information unfeasible. The system performs computational prognosis by providing decision support for pre-screening of medical diagnosis. A presenting patients symptoms can be input to a portable device and the application can quickly retrieve the most similar profiles with known diagnoses from large databases which can be used to compare treatments, diagnosis, test results and other information.

A Knowledge Management System for Intelligent Retrieval of Geo-Spatial Imagery

Large repositories of geo-spatial images are employed to support tasks such as intelligence operations, recreational and professional mapping, urban planning and touristic systems. As imagery is retrieved to support a specific task, the interactions, analyses, and conclusions – based on relevant imagery – may be captured together with the images as an encapsulated experience. We are developing annotation-based image retrieval techniques and knowledge-management support for large geo-spatial image repositories incorporating sketch-based querying for image retrieval and manipulation. Leveraging interactive task knowledge to support current user goals requires a smart system that can perform knowledge capture. This paper describes our initial work in intelligent annotation-based image retrieval for geo-spatial imagery systems and presents the task-based knowledge management environment we have developed to support such retrieval.