Publikationen

Graphical interfaces and interactive visualisations are typical mediators between human users and data analytics systems. HCI researchers and developers have to be able to understand both human needs and back-end data analytics. Participants of our tutorial will learn how visualisation and interface design can be combined with data analytics to provide better visualisations. In the first of three parts, the participants will learn about visualisations and how to appropriately select them. In the second part, restrictions and opportunities associated with different data analytics systems will be discussed. In the final part, the participants will have the opportunity to develop visualisations and interface designs under given scenarios of data and system settings.

Faceted search is a well known and broadly imple- mented paradigm for filtering information with various types of structured information. In this paper we introduce a multiple-view faceted interface, consisting of one main visualisation for exploring the data and multiple minia- turised visualisations showing the filters. The Recommen- dation Dashboard tool provides several interactive visual- isations for analysing recommender results along various faceted dimensions specific to cultural heritage and scien- tific content. As our aim is to reduce the user load and opti- mise the use of screen area, we permit only one main visu- alisation to be visible at a time, and introduce the concept of micro-visualisations – small, simplified views conveying only the necessary information – to provide natural, easy to understand representation of the the active filter set.

Whenever users engage in gathering and organizing new information, searching and browsing activities emerge at the core of the exploration process. As the process unfolds and new knowledge is acquired, interest drifts occur inevitably and need to be accounted for. Despite the advances in retrieval and recommender algorithms, real-world interfaces have remained largely unchanged: results are delivered in a relevance-ranked list. However, it quickly becomes cumbersome to reorganize resources along new interests, as any new search brings new results. We introduce uRank and investigate interactive methods for understanding, refining and reorganizing documents on-the-fly as information needs evolve. uRank includes views summarizing the contents of a recommendation set and interactive methods conveying the role of users' interests through a recommendation ranking. A formal evaluation showed that gathering items relevant to a particular topic of interest with uRank incurs in lower cognitive load compared to a traditional ranked list. A second study consisting in an ecological validation reports on usage patterns and usability of the various interaction techniques within a free, more natural setting.