Kilian Thiel

KNIME: The Konstanz Information Miner

The Konstanz Information Miner is a modular environment, which enables easy visual assembly and interactive execution of a data pipeline. It is designed as a teaching, research and collaboration platform, which enables simple integration of new algorithms and tools as well as data manipulation or visualization methods in the form of new modules or nodes. In this paper we describe some of the design aspects of the underlying architecture and briefly sketch how new nodes can be incorporated.

KNIME - the Konstanz information miner: version 2.0 and beyond

The Konstanz Information Miner is a modular environment, which enables easy visual assembly and interactive execution of a data pipeline. It is designed as a teaching, research and collaboration platform, which enables simple integration of new algorithms and tools as well as data manipulation or visualization methods in the form of new modules or nodes. In this paper we describe some of the design aspects of the underlying architecture, briey sketch how new nodes can be incorporated, and highlight some of the new features of version 2.0.

Node Similarities from Spreading Activation

In this paper we propose two methods to derive two different kinds of node similarities in a network based on their neighborhood. The first similarity measure focuses on the overlap of direct and indirect neighbors. The second similarity compares nodes based on the structure of their - possibly also very distant - neighborhoods. Instead of using standard node measures, both similarities are derived from spreading activation patterns over time. Whereas in the first method the activation patterns are directly compared, in the second method the relative change of activation over time is compared. We apply both methods to a real-world graph dataset and discuss the results.

Towards Visual Exploration of Topic Shifts

This paper presents two approaches to visually analyze the topic shift of a pool of documents over a given period of time. The first of the proposed methods is based on a multi-dimensional scaling algorithm, which places vectors representing terms occurring in certain years (period-frequency-vectors) in a spatial, two-dimensional space. This kind of visualization enables the detection of terms occurring in documents, published in particular years, or terms spread over different years. The second method uses a graph based approach. Publishing dates of documents, as well as their terms are represented by the vertices of a graph. Terms related to a specific publishing year are connected to the vertex of the year via an edge. By usage of activation spreading techniques, terms frequently occurring in documents published in particular years can be discovered visually. We tested both approaches with 2431 abstracts of papers published in the IEEE Transactions on SMC-A, SMC-B, and SMC-C in the years 1996 to 2006. Our experiments indicate that a number of interesting terms can be nicely separated in clumps according to individual years or periods of time. In addition, one can visualize the emergence of specific terms over certain periods of time and how these and other terms fade away again later.

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.

Bisociative discovery of interesting relations between domains

The discovery of surprising relations in large, heterogeneous information repositories is gaining increasing importance in real world data analysis. If these repositories come from diverse origins, forming different domains, domain bridging associations between otherwise weakly connected domains can provide insights into the data that can otherwise not be accomplished. In this paper, we propose a first formalization for the detection of such potentially interesting, domain-crossing relations based purely on structural properties of a relational knowledge description.

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.

Towards discovery of subgraph bisociations

The discovery of surprising relations in large, heterogeneous information repositories is gaining increasing importance in real world data analysis. If these repositories come from diverse origins, forming different domains, domain bridging associations between otherwise weakly connected domains can provide insights into the data that are not accomplished by aggregative approaches. In this paper, we propose a first formalization for the detection of such potentially interesting, domain-crossing relations based purely on structural properties of a relational knowledge description.

Distance aware tag clouds

Distance aware tag clouds add visualization of relations between terms to standard tag clouds. In addition to term importance (which is usually depicted through font size) the placement of terms represents the relation between words in the corpus. These relations are modeled as similarities between words and are visualized via the distance between the corresponding tags in the tag cloud. In this paper a modified multi-dimensional scaling (MDS) approach for tag positioning is presented. Applying standard MDS results in unsatisfying and unusable representations due to two problems. The first problem stems from word overlap which is orthogonal to the second problem: excessive empty space. Both of these shortcomings are addressed by introducing methods for overlap removal and empty space reduction. We show that these two modifications only moderately increase the resulting MDS stress value of the new positioning while they remove most of the overlaps and reduce the amount of white space.

Pure spreading activation is pointless

Almost every application of spreading activation is accompanied by its own set of often heuristic restrictions on the dynamics. We show that in constraint-free scenarios spreading activation would actually yield query-independent results, so that the specific choice of restrictions is not only a pragmatic computational issue, but crucially determines the outcome.