Supporting the active learning of collaborative database browsing techniques

We describe the implications of a study of database browsing behaviour for the development of a system to support more effective browsing. In particular we consider the importance of collaborative working, both in learning browsing skills and in co‐operating on a shared information‐retrieval task. From our study, we believe that an interface to support collaboration should promote the awareness of the activities of others, better visualization of the information data structures being browsed, and effective communication of the browsing process

Supporting collaborative information retrieval in the virtual library

The advent of the virtual library is usually presented as a welcome development for library users. Unfortunately, this tends to reinforce the perception of the use of information resources as a solitary activity. In fact, as many studies have emphasised, information retrieval (IR) in the conventional library is often a highly collaborative activity, involving users' peers and experts such as librarians. Failure to take this into account in the design of virtual library services may result in its users being disadvantaged and denied timely and effective access to sources of assistance. Our focus here is on collaboration between users and librarians. We report an investigation of collaboration issues as seen from the perspective of librarians' and users' contexts and encapsulated in the notion of genre. Finally, we describe the design of a pilot multimedia-based system intended to support collaboration between librarians and IR system users

Supporting collaboration and engagement using a whiteboard-like display

Large interactive display surfaces have the potential to combine the simplicity, spontaneity and presence of a conventional whiteboard with the convenience, clarity, and archiving and retrieval capabilities of a computer display. Recent developments in display projection and large surface digitising have brought the cost of such displays to a level where they can be utilised to support a range of everyday activities. This paper describes the LIDS (Large Interactive Display Surfaces) project, recently commenced at the University of Waikato. LIDS focuses on the use of low-cost whiteboard-like shared interactive displays, and is exploring whiteboard metaphors and lightweight interaction techniques to support group collaboration and engagement. Three closely related application areas are being studied: (i) support for single and multiple site meetings and informal discussions, (ii) the use of such displays in teaching, and (iii) their use in personal information management

Collaborative searching: social searching, together

Information Retrieval (IR) is typically an individual pursuit where an individual searcher will engage with a search system, working alone, until their information need is satisfied. Yet in the real world there are many scenarios, both work-related and related to leisure, entertainment or hobbies, where we want to search as part of a team, maybe even a group of only two people. Collaborative Information Retrieval (CIR) refers to technologies which support collaboration in the retrieval process.  In this presentation we will present both synchronous and asynchronous CIR as well as covering remote and co-located search, and the various combinations of these. In our work we are particularly interested in synchronous collaborative IR (SCIR) where a group of users work collectively to address some shared information need. We describe two systems we have developed to demonstrate SCIR, one on a gesture-based tabletop computer and the other on touch-based mobile devices (iPODs). We believe SCIR to be an important kind of social search even though the tools to support this are neither widespread nor reliable and are limited by the technology we currently use. Despite this we expect the importance of SCIR to grow as a consequential fallout of growth in social networks and the trend towards social networks now acting as platforms for applications, like search

Recommendation, collaboration and social search

This chapter considers the social component of interactive information retrieval: what is the role of other people in searching and browsing? For simplicity we begin by considering situations without computers. After all, you can interactively retrieve information without a computer; you just have to interact with someone or something else. Such an analysis can then help us think about the new forms of collaborative interactions that extend our conceptions of information search, made possible by the growth of networked ubiquitous computing technology. Information searching and browsing have often been conceptualized as a solitary activity, however they always have a social component. We may talk about 'the' searcher or 'the' user of a database or information resource. Our focus may be on individual uses and our research may look at individual users. Our experiments may be designed to observe the behaviors of individual subjects. Our models and theories derived from our empirical analyses may focus substantially or exclusively on an individual's evolving goals, thoughts, beliefs, emotions and actions. Nevertheless there are always social aspects of information seeking and use present, both implicitly and explicitly. We start by summarizing some of the history of information access with an emphasis on social and collaborative interactions. Then we look at the nature of recommendations, social search and interfaces to support collaboration between information seekers. Following this we consider how the design of interactive information systems is influenced by their social elements

Analyzing the Strengths, Weaknesses, Opportunities, and Threats of AI in Libraries

This article provides a comprehensive analysis of the strengths, weaknesses, opportunities, and threats (SWOT) associated with the integration of Artificial Intelligence (AI) in libraries. AI has the potential to transform library and information science, revolutionizing processes, and services. The strengths of AI in libraries include efficient information retrieval and management, enhanced user experiences through personalization, automation of routine tasks, and improved decision-making through data analysis. However, the weaknesses of AI in libraries encompass ethical considerations and biases, the potential lack of human touch and personalized assistance, technical challenges, and concerns about job displacement. The article also explores the opportunities presented by AI, such as advanced search capabilities, expanded accessibility of digital collections, support for diverse user needs, and collaboration among libraries. On the other hand, the threats and challenges of AI in libraries involve privacy and security risks, dependence on technology and potential system failures, user acceptance and trust issues, and the impact on traditional library services and roles. By considering these factors, libraries can make informed decisions and strategically implement AI to maximize its benefits while addressing the associated challenges. The findings of this analysis emphasize the importance of thoughtful implementation and human-AI collaboration to ensure the best outcomes for library users and stakeholders in the future

A Web-based multimedia collaboratory. Empirical work studies in film archives

This report represents the latest study in the activity on Ecological Information Systems conducted in the Center for Human Machine Interaction situated at Ris National Laboratory and the University of Aarhus. The purpose of this activity is to give a description of the characteristics of work domains that will serve to outline the general context of concern to design of collaboratories. In addition, a set of preliminary implications for the design of a collaboratory are derived from the cognitive work analysis. To anticipate, further research on this approach to the design of collaboratories will show how the preceding analysis is likely to lead to a novel theoretical framework, called Ecological Collaborative Information Systems (ECIS), required for the design of collaboratories. The intention is to illustrate how the general principles of ECIS can be instantiated to develop a concrete design product: A crossdisciplinary and cross-cultural collaboratory to support customer service and professional research in archives. A web based Collaboratory Numerous valuable historic and cultural films and their sources are scattered in various national archives. Knowledge and usage of the multinational film material are severely impeded by access problems. To fully exploit the cultural film heritage internationally, a high degree of cross-disciplinary and international collaboration among professionals working with the film media is required. The Collaboratory for Annotation, Indexing and Retrieval of Digitized Historical Archive Material (Collate) is intended to foster and support collaboration on research, cultural mediation and preservation of films through a distributed multimedia repository. The collaboratory will provide webbased tools and interfac..

Enhancing Clinical Decision Support Systems with Public Knowledge Bases

With vast amount of biomedical literature available online, doctors have the benefits of consulting the literature before making clinical decisions, but they are facing the daunting task of finding needles in haystacks. In this situation, it would help doctors if an effective clinical decision support system could generate accurate queries and return a manageable size of highly useful articles. Existing studies showed the useful-ness of patients’ diagnosis information in such scenario, but diagnosis is often missing in most cases. Furthermore, existing diagnosis prediction systems mainly focus on predicting a small range of diseases with well-formatted features, and it is still a great challenge to perform large-scale automatic diagnosis predictions based on noisy pa-tient medical records. In this paper, we propose automatic diagnosis prediction meth-ods for enhancing the retrieval in a clinical decision support system, where the predic-tion is based on evidences automatically collected from publicly accessible online knowledge bases such as Wikipedia and Semantic MEDLINE Database (SemMedDB). The assumption is that relevant diseases and their corresponding symptoms co-occur more frequently in these knowledge bases. Our methods perfor-mance was evaluated using test collections from the Clinical Decision Support (CDS) track in TREC 2014, 2015 and 2016. The results show that our best method can au-tomatically predict diagnosis with about 65.56% usefulness, and such predictions can significantly improve the biomedical literatures retrieval. Our methods can generate comparable retrieval results to the state-of-art methods, which utilize much more complicated methods and some manually crafted medical knowledge. One possible future work is to apply these methods in collaboration with real doctors