Search User Interface Design

Wilson, Max L.

doi:10.2200/s00371ed1v01y201111icr020

Cited by 48 publications

(57 citation statements)

References 178 publications

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“…There have been several recommendations about the functionality of search interfaces [95] and the consideration of human factors when displaying information [96]. These recommendations include details about the presentation of both query and retrieved data, the use of multiple views to provide a comprehensive perspective of the retrieved data, and the use of abstractions to provide an overview of complex data.…”

Section: Display and Feedbackmentioning

confidence: 99%

“…User interface guidelines for search applications should be followed to ensure that users are able to easily integrate the CBIR system into their clinical workflow [95].…”

Section: Interpretation and Visualisationmentioning

confidence: 99%

“…In addition, Wilson [95] Based on the characteristics of PET-CT, we combined the visualisation [96] and search UI [95] guidelines to elicit the following requirements for enabling effective user interpretation of retrieved images:…”

Section: Ui and Visualisation Requirementsmentioning

confidence: 99%

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A graph-based approach for the retrieval of multi-modality medical images

Kumar

Kim

Wen

et al. 2014

Medical Image Analysis

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Advances in sensors and imaging technologies are contributing to rapidly expanding data repositories that contain interrelated information from different modalities. The extraction and visualisation of knowledge from these repositories is a major challenge in the modern, digital world. In the medical domain, images are routinely acquired for a variety of tasks, including diagnosis and patient monitoring. Advances in imaging technologies have resulted in devices capable of acquiring images in multiple dimensions (volumetric and dynamic) as well as from multiple modalities. One example of a widely used volumetric and multimodality image is combined positron emission tomography and computed tomography (PET-CT), which presents physicians with complementary functional and anatomical features and spatial relationships. In clinical practice, PET-CT imaging has already proven its ability to improve cancer diagnosis, localisation, and staging compared to its single-modality counterparts.The clinical benefits provided by medical imaging have spurred increases in the data volume acquired in clinical environments. As such, massive medical imaging collections offer the opportunity for search-based applications in evidence-based diagnosis, physician training, and biomedical research. However, conventional search techniques that operate upon manually assigned textual annotations are not feasible for the volume of data acquired in modern hospitals. Qualitative text descriptions are also limited in their capacity to quantitatively describe the rich information inherent in medical images. Content-based image retrieval (CBIR) is an image search technique that utilises visual features as search criteria. CBIR has already demonstrated benefits forevidence-based diagnosis, physician training, and biomedical research by allowing clinical staff to consider relevant knowledge from retrieved cases. The majority of medical CBIR research has focused on single modality medical images leaving a clear deficiency in the retrieval of multi-modality images. In particular, images iii like PET-CT offer the ability for retrieval based upon the relationships between regions in different modalities, such as the location of tumour features (from PET) in relation to organ features (from CT). The challenge of multi-modality image retrieval for cancer patients lies in representing these complementary geometric and topologic attributes between tumours and organs. A secondary challenge lies in the human aspect of retrieval -effectively communicating the retrieved results to users and facilitating a better understanding of the similarity between the query and retrieved multi-modality images.As such, in this thesis we propose a new graph representation for multimodality images. Our representation preserves the spatial relationships between modalities by emphasising the inherent characteristics of these images that are used for disease staging and classification. This is done by structurally constraining the graph based on image features, e.g., spatia...

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Section: Display and Feedbackmentioning

confidence: 99%

“…User interface guidelines for search applications should be followed to ensure that users are able to easily integrate the CBIR system into their clinical workflow [95].…”

Section: Interpretation and Visualisationmentioning

confidence: 99%

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A graph-based approach for the retrieval of multi-modality medical images

Kumar

Kim

Wen

et al. 2014

Medical Image Analysis

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“…A review of existing literature [46,97,[101][102][103][104][105][106][107][108] in combination with findings derived from the primary data in this research has aided the development of a model for word co-occurrence in faceted search (Table 4). This consists of the seven user needs; Broad, Rich, Intriguing, Descriptive, General, Expert and Situational (BRIDGES).…”

Section: Development Of An Information Needs Modelmentioning

confidence: 99%

Retrieving haystacks: a data driven information needs model for faceted search

Cleverley

Burnett

2014

Journal of Information Science

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CopyrightItems in 'OpenAIR@RGU', Robert Gordon University Open Access Institutional Repository, are protected by copyright and intellectual property law. If you believe that any material held in 'OpenAIR@RGU' infringes copyright, please contact openair-help@rgu.ac.uk with details. The item will be removed from the repository while the claim is investigated. Article Simon BurnettRobert Gordon University AbstractThe research aim was to develop an understanding of information needs characteristics for word co-occurrence based search result filters (facets). No prior research has been identified into what enterprise searchers may find useful for exploratory search and why. Various word co-occurrence techniques were applied to results from sample queries performed on industry membership content. The results were used in an international survey of fifty four practicing petroleum engineers from thirty two organizations. Subject familiarity, job role, personality and query specificity are possible causes for survey response variation. An information needs model is presented: Broad, Rich, Intriguing, Descriptive, General, Expert and Situational (BRIDGES). This may help professionals more effectively meet their information needs and stimulate new needs, improving a systems capability to facilitate serendipity. This research has implications for faceted search in enterprise search and digital library deployments. KeywordsEnterprise search; digital library; exploratory search; interactive information retrieval; human computer interaction (HCI); information discovery; word co-occurrence; text analytics; oil and gas; information seeking behaviour; serendipity; user interface design.

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“…Although there are many studies on the usability of WSE (Thurow & Musica 2009) and guidelines for the general design for search user interfaces (Hearst 2009, Wilson 2012, Russell-Rose & Tate 2013, the area of DSE has not been the subject of extensive usability research so far. While there are many similarities between web and desktop search, there are also some substantial differences: On the web, we usually search for new, mostly unseen information, but on our private desktops we search for "stuff we've seen before" (Dumais et al 2003).…”

Section: Introduction: Desktop Search Engines and The Case For Usabilitymentioning

confidence: 99%

Usability Guidelines for Desktop Search Engines

Burghardt

Schneidermeier

Wolff

2013

Human-Computer Interaction. Human-Centred Design Approaches, Methods, Tools, and Environments

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Abstract. In this article we describe a usability evaluation of eight desktop search engines (DSEs). We used the heuristic walkthrough method to gather usability problems as well as individual strengths and weaknesses of the tested search engines. The results of the evaluation are integrated into a set of 30 design guidelines for user-friendly DSEs. Russell-Rose & Tate 2013), the area of DSE has not been the subject of extensive usability research so far. While there are many similarities between web and desktop search, there are also some substantial differences: On the web, we usually search for new, mostly unseen information, but on our private desktops we search for "stuff we've seen before" (Dumais et al. 2003). The different information behavior for WSEs and DSEs (Bergman 2008) has implications for the design of a desktop search system, i.e. existing guidelines for the design of user-friendly web search systems (cf. Leavitt et al. 2006) cannot be adopted to the area of DSEs without further ado. In this paper we propose a set of guidelines for the design of user-friendly DSEs which has been derived from an expert-based usability inspection of existing desktop search systems. Keywords

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Search User Interface Design

Cited by 48 publications

References 178 publications

A graph-based approach for the retrieval of multi-modality medical images

A graph-based approach for the retrieval of multi-modality medical images

Retrieving haystacks: a data driven information needs model for faceted search

Usability Guidelines for Desktop Search Engines

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