HyperGrid – Accessing Complex Information Spaces - Semantic Scholar

Published in: People and Computers XIX - The Bigger Picture, Vol. 1, Proceedings of HCI 2005 UK Edinburgh, Springer Verlag, Sep 2005

HyperGrid – Accessing Complex Information Spaces Hans-Christian Jetter, Jens Gerken, Werner König, Christian Grün & Harald Reiterer Department of Computer & Information Science, Workgroup HCI, University of Konstanz, 78457 Konstanz, Germany Email:{Hans-Christian.Jetter, Jens.Gerken, Werner.Koenig, Christian.Gruen, Harald.Reiterer}@uni-konstanz.de URL: http://hci.uni-konstanz.de

In this paper, we describe a new interaction and visualization concept for non-expert users to easily access complex heterogeneous information spaces. The HyperGrid combines well-known table visualizations with zoomable user interface concepts to provide a two-dimensional grid as an access tool to multi-dimensional and relational data. It allows the presentation of attributes of multiple data types, various modalities and various levels of detail in a compact and consistent visual structure. Our concept is illustrated by a use-case based on our experiences and evaluations from preceding projects in the field of visual information seeking systems. Keywords: HyperGrid, visualization, zoomable user interfaces, interaction techniques, heterogeneous information space, multi-dimensional data, information retrieval, evaluation.

1 Motivation Many everyday tasks in our life are centred around decision-making based on accessing, sorting, comparing and relating multi-dimensional data. Imagine a salesman has to decide for one of several suggested flight connections at his local travel agent. To choose a connection which suits his requirements he needs to focus on multiple dependent variables simultaneously (e.g. travel time, number of necessary changes, ticket price). A highly efficient way to perform such

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Hans-Christian Jetter, Jens Gerken, Werner König, Christian Grün & Harald Reiterer

comparisons is collecting and structuring information in a table with the individual candidates in rows and the individual properties (or attributes) in columns. The orientation and sorting direction of the table allow a quick insight into similarities and differences among the candidates and reasonably increase the efficiency of decision-making. Using information technology requires a large number of decisions based on multi-dimensional data as well: Everyday we decide for or against documents or web sites found by search engines or we select media objects like images or songs from large catalogues. These decisions are normally based on the given metadata sets for each candidate or object. In most cases this metadata is textual (e.g. name, URL), numeric (e.g. file size, date), and in some cases visual (e.g. thumbnails). As data formats and display sizes are well-defined, a traditional table with the metadata for each candidate displayed in the columns of the candidate’s row can be used for visualization. The possibility to arbitrarily rearrange rows, columns or sorting orders has made the table to be a simple but very popular visual structure – even for navigation tasks in hierarchical data. Accordingly tables are not only used in classical spreadsheet or business applications but also as interfaces for presenting, selecting and manipulating file system or email objects (e.g. in Microsoft Windows Explorer or Outlook). In our preceding projects INSYDER and INVISIP evaluations of table-based visualizations [Gerken 2004] have shown that tables are never inferior and for certain tasks superior to lists in effectiveness and efficiency. Furthermore we have recently concluded a summative usability study with a non-expert user group (n = 24) to compare the web interface of the search engine for the library of the University of Konstanz (KOALA) with our table-based information seeking system MedioVis. It revealed significant (p