1,721,057 research outputs found
A Window-based Approach for Mining Long Duration Event-sequences
No full textThis paper presents an interactive sequence mining approach for exploring long duration event-sequences and identifying interesting patterns within them. The approach extends previous work on exploratory sequence mining by using a sliding window to split the sequence prior to mining. Patterns are interactively grown and visualized through a tree representation, while a set of accompanying views allows for identified patterns to be explored in the context in which they occur. The approach is motivated and exemplified in the domain of air traffic control and, in particular, air traffic controller training.EuroVis Workshop on Visual Analytics (EuroVA)Visual Analysis of High Dimensional and Temporal Dat
An exploratory visual analytics tool for multivariate dynamic networks
Full text linkVisualizing multivariate dynamic networks is a challenging task. The evolution of the dynamic network within the temporal axis must be depicted in conjunction with the associated multivariate attributes. In this paper, an exploratory visual analytics tool is proposed to display multivariate dynamic networks with spatial attributes. The proposed tool displays the distribution of multivariate temporal domain and network attributes in scattered views. Moreover, in order to expose the evolution of a single or a group of nodes in the dynamic network along the temporal axis, an egocentric approach is applied in which a node is represented with its neighborhood as an ego-network. This approach allows users to observe a node's surrounding environment along the temporal axis. On top of the traditional ego-network visualization methods, such as timelines, the proposed tool encodes ego-networks as feature vectors consisting of the domain and network attributes and projects them onto 2D views. As a result, the distance between projected ego-networks represents the dissimilarity across the temporal axis in a single view. The proposed tool is demonstrated with a real-world use case scenario on merchant networks obtained from a one-year-long credit card transactions
DualNetView: Dual Views for Visualizing the Dynamics of Networks
No full textThe force-directed layout is a popular visual method for revealing network structures, such as clusters and important vertices. However, it is not capable of representing temporal patterns, such as how clusters/communities evolve. Dynamic network visualizations trade the overall structures for temporal relationships. In this paper, we present a dual view framework for capturing both overall structures and temporal patterns within networks. The linked supplemental views utilize the strengths of both visualization techniques to provide useful insights into the given networks. To demonstrate the usefulness of our proposed dual views, we provide three use cases of dynamic networks: computer networks communications, activities of suspicious processes in computer systems, and social networks.EuroVis Workshop on Visual Analytics (EuroVA)Visual Analytics Methods and Application
A Generic Model for Projection Alignment Applied to Neural Network Visualization
No full textDimensionality reduction techniques are popular tools for the visualization of neural network models due to their ability to display hidden layer activations and aiding the understanding of how abstract representations are being formed. However, many techniques render poor results when used to compare multiple projections resulted from different feature sets, such as the outputs of different hidden layers or the outputs from different models processing the same data. This problem occurs due to the lack of an alignment factor to ensure that visual differences represent actual differences between the feature sets and not artifacts generated by the technique. In this paper, we propose a generic model to align multiple projections when visualizing different feature sets that can be applied to any gradient descent-based dimensionality reduction technique. We employ this model to generate a variant of the UMAP method and show the results of its application.EuroVis Workshop on Visual Analytics (EuroVA)Intersecting Humans and A
SpatialRugs: Enhancing Spatial Awareness of Movement in Dense Pixel Visualizations
No full textCompact visual summaries of spatio-temporal movement data often strive to express accurate positions of movers. We present SpatialRugs, a technique to enhance the spatial awareness of movements in dense pixel visualizations. SpatialRugs apply 2D colormaps to visualize location mapped to a juxtaposed display. We explore the effect of various colormaps discussing perceptual limitations and introduce a custom color-smoothing method to mitigate distorted patterns of collective movement behavior.EuroVis Workshop on Visual Analytics (EuroVA)Visual Analytics Methods and Application
Enhanced Attribute-Based Explanations of Multidimensional Projections
No full textMultidimensional projections (MPs) are established tools for exploring the structure of high-dimensional datasets to reveal groups of similar observations. For optimal usage, MPs can be augmented with mechanisms that explain what such points have in common that makes them similar. We extend the set of such explanatory instruments by two new techniques. First, we compute and encode the local dimensionality of the data in the projection, thereby showing areas where the MP can be well explained by a few latent variables. Secondly, we compute and display local attribute correlations, thereby helping the user to discover alternative explanations for the underlying phenomenon. We implement our explanatory tools using an image-based approach, which is efficient to compute, scales well visually for large and dense MP scatterplots, and can handle any projection technique. We demonstrate our approach using several datasets.EuroVis Workshop on Visual Analytics (EuroVA)Visual Analysis of High Dimensional and Temporal Dat
Learning and Teaching in Co-Adaptive Guidance for Mixed-Initiative Visual Analytics
No full textGuidance processes in visual analytics applications often lack adaptivity. In this position paper, we contribute the concept of co-adaptive guidance, building on the principles of initiation and adaptation. We argue that both the user and the system adapt their data-, task- and user/system-models over time. Based on these principles, we propose reasoning about the guidance design space through introducing the concepts of learning and teaching that complement the existing dimension of implicit and explicit guidance, thus, deriving the four guidance dynamics user-teaching, system-teaching, user-learning, and system-learning. Finally, we classify current guidance approaches according to the dynamics, demonstrating their applicability to co-adaptive guidance.EuroVis Workshop on Visual Analytics (EuroVA)Intersecting Humans and A
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