Selecting one from many: the development of a scalable visualization tool

Abstract

This paper describes visualisation tools to support the task of selecting one object from a collection of many on the basis of its attribute values. For this frequently encountered task we identify a set of tools appropriate to a spectrum of collection sizes extending from hundreds of thousands to as few as ten or twenty. Although some of the tools have not previously been reported, and some have received only cursory attention in the literature, others are well known. This paper presents the tools in a coherent and consistent manner, showing relationships and progressions between them, identifying their principal attributes and relating them to the problem solver's cognitive task. We conclude with a proposal for integrating techniques within a single tool in order to deal with a continuum of working set sizes.