{"id":5597,"date":"2018-08-06T19:08:06","date_gmt":"2018-08-06T18:08:06","guid":{"rendered":"https:\/\/irsg.bcs.org\/informer\/?p=5597"},"modified":"2018-08-06T19:08:06","modified_gmt":"2018-08-06T18:08:06","slug":"visualizing-search-strategies","status":"publish","type":"post","link":"https:\/\/archive-irsg.bcs.org\/informer\/?p=5597","title":{"rendered":"Visualizing search strategies"},"content":{"rendered":"
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According to the IDC whitepaper,\u00a0The High Cost of Not Finding Information<\/a>, knowledge workers spend 2.5 hours per day searching for information. Whether they eventually find what they are looking for or just stop and make a sub-optimal decision, there is a high cost to both outcomes. The recruitment industry, for example, relies on\u00a0Boolean search<\/a>\u00a0as the foundation of the candidate sourcing process, and yet finding candidates with appropriate skills and experience\u00a0remains an ongoing challenge<\/a>. Similarly, patent agents rely on accurate prior art search as the foundation of their due diligence process, and yet infringement suits are being filed at a rate of more than 10 a day due to the later\u00a0discovery of prior art which their original search tools missed<\/a>.<\/p>\n<\/div>\n<\/div>\n

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What these professions have in common is a need to develop search strategies that are accurate, repeatable and transparent. The traditional solution to this problem is to use line-by-line query builders which require the user to enter\u00a0Boolean strings<\/a>\u00a0that may then be combined to form a\u00a0multi-line search strategy<\/a>:<\/p>\n<\/div>\n<\/div>\n

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However, such query builders typically offer limited support for error checking or query optimization, and their output is often\u00a0compromised by errors and inefficiencies<\/a>. In this post, we review three early but highly original and influential alternatives, and discuss their contribution to contemporary issues and design challenges.<\/p>\n

Alternative approaches<\/h2>\n

The application of data visualization to search query formulation can offer\u00a0significant benefits<\/a>, such as fewer zero-hit queries, improved query comprehension, and better support for exploration of an unfamiliar database. An early example of such an approach is that of\u00a0Anick et al<\/a>. (1989), who developed a system that could parse natural language queries and represent them using a \u201cQuery Reformulation Workspace\u201d. Although early work, this system introduced a number of key design ideas:<\/p>\n