Search (5 results, page 1 of 1)

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Abstract

Granularity is a novel concept for presenting information in search result interfaces of hierarchical query-driven information retrieval systems in a manner that can support understanding and exploration of the context of the retrieved information (e.g., by highlighting its position in the granular hierarchy and exposing its relationship with relatives in the hierarchy). Little research, however, has been conducted on the effects of granularity of search results on the relevance judgment behavior of engineers. Engineers are highly motivated information users who are particularly interested in understanding the context of the retrieved information. Therefore, it is hypothesized that the design of systems with careful regard for granularity would improve engineers' relevance judgment behavior. To test this hypothesis, a prototype system was developed and evaluated in terms of the time needed for users to find relevant information, the accuracy of their relevance judgment, and their subjective satisfaction. To evaluate the prototype, a user study was conducted where participants were asked to complete tasks, complete a satisfaction questionnaire, and be interviewed. The findings showed that participants performed better and were more satisfied when the prototype system presented only relevant information in context. Although this study presents some novel findings about the effects of granularity and context on user relevance judgment behavior, the results should be interpreted with caution. For example, participants in this research were recruited by convenience and performed a set of simulated tasks as opposed to real ones. However, suggestions for further research are presented.

Type

a

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Abstract

The authors report on the findings of a usability test conducted to evaluate the usability of the VeriaGrid online system. The VeriaGrid (www.theveriagrid.org) is a prototype virtual map that focuses on the provision of information related to the cultural heritage of the city of Veria (Greece). It has been developed under the Light Project by the Central Public Library of Veria (www.libver.gr). It is an interactive application that includes various functional or thematic areas such as an interactive digital map of Veria, image gallery, videoclips, panoramic site photos, and general information about the city of Veria. The findings of the usability test revealed that users had some difficulties in using novel features of the digital map (such as the Recommended Points and the Routes functions) and finding textual information about cultural heritage of the city of Veria. Users, however, were satisfied with the overall usability of the system. In light of these findings, some recommendations for improving the usability of the system are made.

Type

a

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Abstract

Research Data repositories are growing in terms of volume rapidly and exponentially. Their main goal is to provide scientists the essential mechanism to store, share, and re-use datasets generated at various stages of the research process. Despite the fact that metadata play an important role for research data management in the context of these repositories, several factors - such as the big volume of data and its complex lifecycles, as well as operational constraints related to financial resources and human factors - may impede the effectiveness of several metadata elements. The aim of the research reported in this paper was to perform a descriptive analysis of the DC.Subject metadata element and to identify its data quality problems in the context of the Dryad research data repository. In order to address this aim a total of 4.557 packages and 13.638 data files were analysed following a data-preprocessing method. The findings showed emerging trends about the subject coverage of the repository (e.g. the most popular subjects and the authors that contributed the most for these subjects). Also, quality problems related to the lack of controlled vocabulary and standardisation were very common. This study has implications for the evaluation of metadata and the improvement of the quality of the research data annotation process.

Type

a

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Abstract

There is a consensus among scientists that the analysis of Big Data in health care (such as electronic health records, patient reported outcomes or in-motion data) can improve clinical research and the quality of care provided to patients. Yet there is little knowledge about the actual effectiveness of Big Data in the health care sector. The aim of this study was to perform a systematic review of the literature in order to determine the extent to which Big Data applications in health care systems have managed to improve patient experiences and clinicians' behavior as well as the quality of care provided to patients. All searches for relevant articles were performed in the PubMed database. From the 108 potentially relevant articles 12 satisfied the inclusion criteria for this study. The findings showed that in the case of nine articles the researchers reported positive effect of Big Data. However, some negative results were recorded in the case of three articles. The main benefits of Big Data application involved positive behavior change, improved usability and efficient decision support. However, problems were identified for technology acceptance. Most problems occurred in the case of systems processing heterogeneous datasets, patient reported outcomes and in motion data, as opposed to electronic health record systems. The paper concludes by highlighting some areas of investigation where further research is needed to understand the use of Big Data in health care and improve its effectiveness.

Type

a

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Abstract

Eye movement data can provide an in-depth view of human reasoning and the decision-making process, and modern information retrieval (IR) research can benefit from the analysis of this type of data. The aim of this research was to examine the relationship between relevance criteria use and visual behavior in the context of predictive relevance judgments. To address this objective, a multimethod research design was employed that involved observation of participants' eye movements, talk-aloud protocols, and postsearch interviews. Specifically, the results reported in this article came from the analysis of 281 predictive relevance judgments made by 24 participants using the Google search engine. We present a novel stepwise methodological framework for the analysis of relevance judgments and eye movements on the Web and show new patterns of relevance criteria use during predictive relevance judgment. For example, the findings showed an effect of ranking order and surrogate components (Title, Summary, and URL) on the use of relevance criteria. Also, differences were observed in the cognitive effort spent between very relevant and not relevant judgments. We conclude with the implications of this study for IR research.

Type

a