{"id":5671,"date":"2018-10-26T18:03:34","date_gmt":"2018-10-26T17:03:34","guid":{"rendered":"https:\/\/irsg.bcs.org\/informer\/?p=5671"},"modified":"2018-10-26T18:03:34","modified_gmt":"2018-10-26T17:03:34","slug":"visualizing-search-strategies-part-2","status":"publish","type":"post","link":"https:\/\/archive-irsg.bcs.org\/informer\/?p=5671","title":{"rendered":"Visualizing search strategies (part 2)"},"content":{"rendered":"

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In\u00a0our last post<\/a>\u00a0we reviewed some of the issues involved in developing\u00a0effective solutions to complex search problems<\/a>, and explored some of the challenges involved in formulating and representing\u00a0Boolean strings and expressions<\/a>. In particular, we explored the\u00a0contribution of three experimental systems<\/a>\u00a0which aimed to offer an alternative to the conventional approach exemplified by line-by-line query builders and \u2018advanced search\u2019 forms. In this piece, we review some of the more recent examples, and reflect on the ways in which their ideas, insights and innovations may be productively applied to address contemporary search challenges.<\/p>\n

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Alternative approaches<\/h2>\n

Our first example is that of\u00a0Yi et al (2005)<\/a>, who were investigating ways in which information visualization techniques could be applied the exploration and querying of\u00a0multivariate data sets<\/a>. In particular, they developed a system based around a \u2018dust and magnet\u2019 metaphor, in which dimensions of interest within the data could be represented as \u2018magnets\u2019 on a visual canvas, and the relationships between points in the data could be understood by observing the effect of the \u2018magnetic forces\u2019 on individual \u2018data particles\u2019. For example, a database of cereal brands could be explored by selecting magnets corresponding to dimensions of interest in the data (e.g. protein, vitamin, sugar, fat content), and the layout would then represent the balance of competing forces between them:<\/p>\n

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Crucially, Yi el al recognised that the effectiveness of their approach relied not just on the choice of an appropriate metaphor, but also attention to detail in the execution which emphasized the use of\u00a0animation<\/em>\u00a0and\u00a0interaction<\/em>\u00a0to convey the nature of the relationships within the data. To them, animation was more than mere decoration: it was the means by which the user\u00a0understood how the visualization was actually generated<\/em>. This is a theme to which we return\u00a0in our own work<\/a>.<\/p>\n

Our second example concerns a more recent system, namely that of\u00a0Nitsche & Nurnberger (2013)<\/a>. They observed that when users search for information in unfamiliar domains, they often struggle to formulate effective queries, and can end up repeating reformulations without necessarily achieving their goals. In response, they developed a system based around a radial interface in which queries and results could be integrated and collectively manipulated.<\/p>\n

The concept utilized a pseudo-desktop metaphor in which objects of interest clustered toward the centre. \u00a0Query objects could be entered directly onto this canvas (shown as circular icons), and their proximity to the centre and to other objects was used as a relevance cue, influencing the selection and position of search results (shown as square icons). The radial canvas was combined with a \u2018scratch space\u2019 on the left for unused query terms, and a conventional results list on the right:<\/p>\n

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Significantly, Nitsche and Nurberger understood that the provision of immediate feedback was fundamental to effective exploratory search, so users could not only query the data in a conventional sense but also compose \u2018what if\u2019 questions to explore and optimise their own search queries. They also recognised that the exploratory nature of their system invited interaction via non-desktop (i.e.\u00a0 touch-based) platforms, and developed versions for native tablet devices (e.g. iPad) and also for mobile web (HTML5). These are also ideas which have influenced\u00a0our own work<\/a>.<\/p>\n

Our final example is much more modest in ambition, but offers perhaps the most compelling proposition by virtue of its simplicity.\u00a0Boolify<\/a>\u00a0was developed as an educational tool to teach users how to create Boolean searches in Google. Users are invited to drag coloured blocks onto a canvas, e.g. green for terms or phrases, and yellow or red for Boolean operators (OR and NOT respectively), and purple for platform-specific operators (e.g. \u2018intitle\u2019 \u2018inurl, etc.):<\/p>\n

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Crucially, by displaying the generated Boolean string below the canvas, Boolify promotes understanding of the query syntax and optimisation of its semantics, and facilitates portability of the output to other systems. These are key themes to which we return in\u00a0our own work<\/a>.<\/p>\n

Boolify\u2019s simplicity encourages playful interaction and exploration. However, its minimalism is also its principal shortcoming: a lack of support for structural delimitation (e.g. parentheses) means there is no inbuilt method to differentiate between \u2018A AND (B OR C)\u2019 and \u2018(A AND B) OR C\u2019. Boolify thus excels as a learning tool, but does not scale well to the rigour of\u00a0professional search<\/a>\u00a0practice.<\/p>\n

In summary<\/h2>\n

In this post and the\u00a0previous<\/a>\u00a0one we have reviewed some of the issues involved in developing effective solutions to complex search problems, and explored the contribution of a number of key alternatives. Each of these systems affords their own unique solution to the problem of\u00a0search strategy formulation<\/a>, and collectively they offer a number of key principles and insights, such as:<\/p>\n