Stefan Haun

Data exploration for bisociative knowledge discovery: a brief overview of tools and evaluation methods

In this chapter we explain the definition of the term (data) exploration. We refine this definition in the context of browsing, navigating and searching. We provide a definition of bisociative exploration and derive requirements on user interfaces, which are designed to support bisociative knowledge discovery. We discuss how to support subtasks of bisociative data exploration with appropriate user interface elements. We also present a set of exploratory tools, which are currently available or in development. Finally, we discuss the problem of usability evaluation in the context of exploratory search. Two main issues - complexity and comparability - are explained and possible solutions proposed.

On the integration of graph exploration and data analysis: the creative exploration toolkit

To enable discovery in large, heterogenious information networks a tool is needed that allows exploration in changing graph structures and integrates advanced graph mining methods in an interactive visualization framework. We present the Creative Exploration Toolkit (CET), which consists of a state-of-the-art user interface for graph visualization designed towards explorative tasks and support tools for integration and communication with external data sources and mining tools, especially the data-mining platform KNIME. All parts of the interface can be customized to fit the requirements of special tasks, including the use of node type dependent icons, highlighting of nodes and clusters. Through an evaluation we have shown the applicability of CET for structure-based analysis tasks.

CET: a tool for creative exploration of graphs

We present a tool for interactive exploration of graphs that integrates advanced graph mining methods in an interactive visualization framework. The tool enables efficient exploration and analysis of complex graph structures. For flexible integration of state-of-the-art graph mining methods, the viewer makes use of the open source data mining platform KNIME. In contrast to existing graph visualization interfaces, all parts of the interface can be dynamically changed to specific visualization requirements, including the use of node type dependent icons, methods for a marking if nodes or edges and highlighting and a fluent graph that allows for iterative growing, shrinking and abstraction of (sub)graphs.

Bisociative music discovery and recommendation

Surprising a user with unexpected and fortunate recommendations is a key challenge for recommender systems. Motivated by the concept of bisociations, we propose ways to create an environment where such serendipitous recommendations become more likely. As application domain we focus on music recommendation using MusicGalaxy, an adaptive user-interface for exploring music collections. It leverages a non-linear multi-focus distortion technique that adaptively highlights related music tracks in a projection-based collection visualization depending on the current region of interest. While originally developed to alleviate the impact of inevitable projection errors, it can also adapt according to user-preferences. We discuss how using this technique beyond its original purpose can create distortions of the visualization that facilitate bisociative music discovery.

Evaluating Decisions: Characteristics, Evaluation of Outcome and Serious Games

The characteristics of decisions and the evaluation of their outcome are highly complex. In this paper, we first give a short analysis of different types of decisions such as long-term and short-term decisions or dilemmas by discussing different scenarios and examples. We identify characteristics of decisions and give a short account of the usually quite complex decision scenarios involved and the high uncertainty of long-term predictions. We introduce serious games as a tool for evaluation of decisions through simulation. By using serious games long-term effects of decisions can be evaluated and known measurement criteria for short-term decision can be transformed to support long-term decisions.

Exploration of Web Search Results Based on the Formal Concept Analysis

In this paper, we present an approach to support exploratory search by structuring search results based on concept lattices, which are created on the fly using advanced methods from the area of Formal Concept Analysis (FCA). The main aim of the approach is to organize query based search engine results (e.g. web documents) as a hierarchy of clusters that are composed of documents with similar attributes. The concept lattice provides a structured view on the query-related domains and hence can improve the understanding of document properties and shared features. Additionally, we applied a fuzzy extension of FCA in order to support the usage of different types of attributes within the analyzed query results set. The approach has been integrated into an interactive web search interface. It provides a smooth integration of keyword-based web search and interactive visualization of concept lattice and its concepts in order to support complex search tasks.

Exploring Technical Documents: A Prototype Study

Finding information in unknown, large data sets is not an easy task, especially if they consist of documents in an unfamiliar domain. A collection of several hundred technical reports has been analyzed in order to organize it for efficient and fluent searching, browsing, navigation and even exploration. We describe a user study on an interactive system – the EFB-Explorer – that visualizes the data set by different attributes to reflect the specific relevance of a retrieved document to a user’s query and offers easy-to-use zooming interaction as well as semantic zooming.