EuroVA18

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2632398
Brno, Czech Republic 4 June 2018
Table of Contents
Analytics and Guidance
ComModeler: Topic Modeling Using Community Detection
[full paper Image] [meta data Image]
Tommy Dang and Vinh The Nguyen
Visual Exploration of Spatial and Temporal Variations of Tweet Topic Popularity
[full paper Image] [meta data Image]
Jie Li, Siming Chen, Gennady Andrienko, and Natalia Andrienko
Visual Predictive Analytics using iFuseML
[full paper Image] [meta data Image]
Gunjan Sehgal, Mrinal Rawat, Bindu Gupta, Garima Gupta, Geetika Sharma, and Gautam Shroff
Guidance or No Guidance? A Decision Tree Can Help
[full paper Image] [meta data Image]
Davide Ceneda, Theresia Gschwandtner, Thorsten May, Silvia Miksch, Marc Streit, and Christian Tominski
Applications
A Visual Analytics System for Managing Mobile Network Failures
[full paper Image] [meta data Image]
Marco Angelini, Luca Bardone, Marina Geymonat, Mario Mirabelli, Chiara Remondino, Giuseppe Santucci, Barbara Stabellini, and Paolo Tamborrini
Personalized Visual-Interactive Music Classification
[full paper Image] [meta data Image]
Christian Ritter, Christian Altenhofen, Matthias Zeppelzauer, Arjan Kuijper, Tobias Schreck, and Jürgen Bernard
A Set-based Visual Analytics Approach to Analyze Retail Data
[full paper Image] [meta data Image]
Muhammad Adnan and Roy A. Ruddle
polimaps: Supporting Predictive Policing with Visual Analytics
[full paper Image] [meta data Image]
Florian Stoffel, Hanna Post, Marcus Stewen, and Daniel A. Keim
Work-in-Progress
Combining the Automated Segmentation and Visual Analysis of Multivariate Time Series
[full paper Image] [meta data Image]
Jürgen Bernard, Christian Bors, Markus Bögl, Christian Eichner, Theresia Gschwandtner, Silvia Miksch, Heidrun Schumann, and Jörn Kohlhammer
Towards Visual Cyber Security Analytics for the Masses
[full paper Image] [meta data Image]
Alex Ulmer, Marija Schufrin, Hendrik Lücke-Tieke, Clindo Devassy Kannanayikkal, and Jörn Kohlhammer
A Concept for Consensus-based Ordering of Views
[full paper Image] [meta data Image]
Wolfgang Jentner, Dominik Jäckle, Ulrich Engelke, Daniel A. Keim, and Tobias Schreck

BibTeX (EuroVA18)
@inproceedings{
10.2312:eurova.20181104,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Christian Tominski and Tatiana von Landesberger
},
title = {{
ComModeler: Topic Modeling Using Community Detection}},

author = {
Dang, Tommy
 and 
Nguyen, Vinh The
},
year = {
2018},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-064-2},

DOI = {
10.2312/eurova.20181104}

}
@inproceedings{
10.2312:eurova.20181105,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Christian Tominski and Tatiana von Landesberger
},
title = {{
Visual Exploration of Spatial and Temporal Variations of Tweet Topic Popularity}},

author = {
Li, Jie
 and 
Chen, Siming
 and 
Andrienko, Gennady
 and 
Andrienko, Natalia
},
year = {
2018},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-064-2},

DOI = {
10.2312/eurova.20181105}

}
@inproceedings{
10.2312:eurova.20181106,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Christian Tominski and Tatiana von Landesberger
},
title = {{
Visual Predictive Analytics using iFuseML}},

author = {
Sehgal, Gunjan
 and 
Rawat, Mrinal
 and 
Gupta, Bindu
 and 
Gupta, Garima
 and 
Sharma, Geetika
 and 
Shroff, Gautam
},
year = {
2018},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-064-2},

DOI = {
10.2312/eurova.20181106}

}
@inproceedings{
10.2312:eurova.20181107,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Christian Tominski and Tatiana von Landesberger
},
title = {{
Guidance or No Guidance? A Decision Tree Can Help}},

author = {
Ceneda, Davide
 and 
Gschwandtner,  Theresia
 and 
May, Thorsten
 and 
Miksch, Silvia
 and 
Streit, Marc
 and 
Tominski, Christian
},
year = {
2018},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-064-2},

DOI = {
10.2312/eurova.20181107}

}
@inproceedings{
10.2312:eurova.20181108,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Christian Tominski and Tatiana von Landesberger
},
title = {{
A Visual Analytics System for Managing Mobile Network Failures}},

author = {
Angelini, Marco
 and 
Bardone, Luca
 and 
Geymonat, Marina
 and 
Mirabelli, Mario
 and 
Remondino, Chiara
 and 
Santucci, Giuseppe
 and 
Stabellini, Barbara
 and 
Tamborrini, Paolo
},
year = {
2018},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-064-2},

DOI = {
10.2312/eurova.20181108}

}
@inproceedings{
10.2312:eurova.20181109,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Christian Tominski and Tatiana von Landesberger
},
title = {{
Personalized Visual-Interactive Music Classification}},

author = {
Ritter, Christian
 and 
Altenhofen, Christian
 and 
Zeppelzauer, Matthias
 and 
Kuijper, Arjan
 and 
Schreck, Tobias
 and 
Bernard, Jürgen
},
year = {
2018},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-064-2},

DOI = {
10.2312/eurova.20181109}

}
@inproceedings{
10.2312:eurova.20181110,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Christian Tominski and Tatiana von Landesberger
},
title = {{
A Set-based Visual Analytics Approach to Analyze Retail Data}},

author = {
Adnan, Muhammad
 and 
Ruddle, Roy A.
},
year = {
2018},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-064-2},

DOI = {
10.2312/eurova.20181110}

}
@inproceedings{
10.2312:eurova.20181111,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Christian Tominski and Tatiana von Landesberger
},
title = {{
polimaps: Supporting Predictive Policing with Visual Analytics}},

author = {
Stoffel, Florian
 and 
Post, Hanna
 and 
Stewen, Marcus
 and 
Keim, Daniel A.
},
year = {
2018},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-064-2},

DOI = {
10.2312/eurova.20181111}

}
@inproceedings{
10.2312:eurova.20181112,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Christian Tominski and Tatiana von Landesberger
},
title = {{
Combining the Automated Segmentation and Visual Analysis of Multivariate Time Series}},

author = {
Bernard, Jürgen
 and 
Bors, Christian
 and 
Bögl, Markus
 and 
Eichner, Christian
 and 
Gschwandtner, Theresia
 and 
Miksch, Silvia
 and 
Schumann, Heidrun
 and 
Kohlhammer, Jörn
},
year = {
2018},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-064-2},

DOI = {
10.2312/eurova.20181112}

}
@inproceedings{
10.2312:eurova.20181113,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Christian Tominski and Tatiana von Landesberger
},
title = {{
Towards Visual Cyber Security Analytics for the Masses}},

author = {
Ulmer, Alex
 and 
Schufrin, Marija
 and 
Lücke-Tieke, Hendrik
 and 
Kannanayikkal, Clindo Devassy
 and 
Kohlhammer, Jörn
},
year = {
2018},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-064-2},

DOI = {
10.2312/eurova.20181113}

}
@inproceedings{
10.2312:eurova.20181114,

booktitle = {
EuroVis Workshop on Visual Analytics (EuroVA)},

editor = {
Christian Tominski and Tatiana von Landesberger
},
title = {{
A Concept for Consensus-based Ordering of Views}},

author = {
Jentner, Wolfgang
 and 
Jäckle, Dominik
 and 
Engelke, Ulrich
 and 
Keim, Daniel A.
 and 
Schreck, Tobias
},
year = {
2018},

publisher = {
The Eurographics Association},


ISBN = {978-3-03868-064-2},

DOI = {
10.2312/eurova.20181114}

}

Browse

Recent Submissions

Now showing 1 - 12 of 12
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    EuroVA 2018: Frontmatter
    (The Eurographics Association, 2018) Tominski, Christian; Landesberger, Tatiana von; Christian Tominski and Tatiana von Landesberger
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    ComModeler: Topic Modeling Using Community Detection
    (The Eurographics Association, 2018) Dang, Tommy; Nguyen, Vinh The; Christian Tominski and Tatiana von Landesberger
    This paper introduces ComModeler, a novel approach for topic modeling using community finding in dynamic networks. Our algorithm first extracts the terms/keywords, formulates a network of collocated terms, then refines the network based on various features (such as term/relationship frequency, sudden changes in their frequency time series, or vertex betweenness centrality) to reveal the structures/communities in dynamic social networks. These communities correspond to different hidden topics in the input text documents. Although initially motivated to analyze text documents, we soon realized the ComModeler has more general implications for other application domains. We demonstrate the ComModeler on several real-world datasets, including the IEEE VIS publications from 1990 to 2016, together with collocated phrases obtained from various political blogs.
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    Visual Exploration of Spatial and Temporal Variations of Tweet Topic Popularity
    (The Eurographics Association, 2018) Li, Jie; Chen, Siming; Andrienko, Gennady; Andrienko, Natalia; Christian Tominski and Tatiana von Landesberger
    We present a visual analytical approach to exploring variation of topic popularity in social media (such as Twitter) over space and time. Our approach includes an analytical pipeline and a multi-view visualization tool. As attempts of topic extraction from very short texts like tweets may not produce meaningful results, we aggregate the texts prior to applying topic modelling techniques. Interactive visualisations support detection of burst events in social media posting activities at different locations, show the spatial, temporal, quantitative, and semantic aspects of these events, and enable the user to explore how popularity of topics varies over cities and time. A case study has been conducted using a real-world tweet dataset.
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    Visual Predictive Analytics using iFuseML
    (The Eurographics Association, 2018) Sehgal, Gunjan; Rawat, Mrinal; Gupta, Bindu; Gupta, Garima; Sharma, Geetika; Shroff, Gautam; Christian Tominski and Tatiana von Landesberger
    Solving a predictive analytics problem involves multiple machine learning tasks in a workflow. Directing such workflows efficiently requires an understanding of data so as to identify and handle missing values and outliers, compute feature correlations and to select appropriate models and hyper-parameters. While traditional machine learning techniques are capable of handling these challenges to a certain extent, visual analysis of data and results at each stage can significantly assist in optimal processing of the workflow. We present iFuseML , a visual interactive framework to support analysts in machine learning workflows via insightful data visualizations as well as natural language interfaces where appropriate. Our platform lets the user intuitively search and explore datasets, join relevant datasets using natural language queries, detect and visualize multidimensional outliers and explore feature relationships using Bayesian coordinated views. We also demonstrate how visualization assists in comparing prediction errors to guide ensemble models so as to generate more accurate predictions. We illustrate our framework using a house price dataset from Kaggle, where using iFuseML simplified the machine learning workflow and helped improve the resulting prediction accuracy.
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    Guidance or No Guidance? A Decision Tree Can Help
    (The Eurographics Association, 2018) Ceneda, Davide; Gschwandtner, Theresia; May, Thorsten; Miksch, Silvia; Streit, Marc; Tominski, Christian; Christian Tominski and Tatiana von Landesberger
    Guidance methods have the potential of bringing considerable benefits to Visual Analytics (VA), alleviating the burden on the user and allowing a positive analysis outcome. However, the boundary between conventional VA approaches and guidance is not sharply defined. As a consequence, framing existing guidance methods is complicated and the development of new approaches is also compromised. In this paper, we try to bring these concepts in order, defining clearer boundaries between guidance and no-guidance. We summarize our findings in form of a decision tree that allows scientists and designers to easily frame their solutions. Finally, we demonstrate the usefulness of our findings by applying our guideline to a set of published approaches.
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    A Visual Analytics System for Managing Mobile Network Failures
    (The Eurographics Association, 2018) Angelini, Marco; Bardone, Luca; Geymonat, Marina; Mirabelli, Mario; Remondino, Chiara; Santucci, Giuseppe; Stabellini, Barbara; Tamborrini, Paolo; Christian Tominski and Tatiana von Landesberger
    Large mobile operators have to quickly react to mobile network failures to ensure service continuity and this task is a complex one, due to the continuous and very fast evolution of mobile networks: from 2G to 3G and onto LTE, each significant milestone in the mobile technology has increased the complexity of networks and services management. Failures must be promptly analyzed and sorted according to different prioritizing objectives, in order to devise suitable fix plans able to mitigate failures impact in terms of money loss or damaged reputation. This paper presents a visual analytics solution for supporting the failure management activities of TIM (Telecom Italia Group), the biggest Italian provider of telecommunications services with over 30M active mobile subscribers. The proposed system has been developed collaboratively by University of Rome ''La Sapienza'', Polytechnic of Turin, and TIM, analyzing the operators' requirements and viable optimization strategies for prioritizing interventions that rely on statistical data on mobile cells occupation, in order to identify the impact of failures in term of end users' connectivity.
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    Personalized Visual-Interactive Music Classification
    (The Eurographics Association, 2018) Ritter, Christian; Altenhofen, Christian; Zeppelzauer, Matthias; Kuijper, Arjan; Schreck, Tobias; Bernard, Jürgen; Christian Tominski and Tatiana von Landesberger
    We present an interactive visual music classification tool that will allow users to automatically structure music collections in a personalized way. With our approach, users play an active role in an iterative process of building classification models, using different interactive interfaces for labeling songs. The interactive tool conflates interfaces for the detailed analysis at different granularities, i.e., audio features, music songs, as well as classification results at a glance. Interactive labeling is provided with three complementary interfaces, combining model-centered and human-centered labeling-support principles. A clean visual design of the individual interfaces depicts complex model characteristics for experts, and indicates our work-inprogress towards the abilities of non-experts. The result of a preliminary usage scenario shows that, with our system, hardly any knowledge about machine learning is needed to create classification models of high accuracy with less than 50 labels.
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    A Set-based Visual Analytics Approach to Analyze Retail Data
    (The Eurographics Association, 2018) Adnan, Muhammad; Ruddle, Roy A.; Christian Tominski and Tatiana von Landesberger
    This paper explores how a set-based visual analytics approach could be useful for analyzing customers' shopping behavior, and makes three main contributions. First, it describes the scale and characteristics of a real-world retail dataset from a major supermarket. Second, it presents a scalable visual analytics workflow to quickly identify patterns in shopping behavior. To assess the workflow, we conducted a case study that used data from four convenience stores and provides several insights about customers' shopping behavior. Third, from our experience with analyzing real-world retail data and comments made by our industry partner, we outline four research challenges for visual analytics to tackle large set intersection problems.
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    polimaps: Supporting Predictive Policing with Visual Analytics
    (The Eurographics Association, 2018) Stoffel, Florian; Post, Hanna; Stewen, Marcus; Keim, Daniel A.; Christian Tominski and Tatiana von Landesberger
    Recently, predictive policing has gained a lot of attention, as the benefits, e.g., better crime prevention or an optimized resource planning are essential goals for law enforcement agencies. Commercial predictive policing systems commonly visualize predictions on maps but provide only little support for human analysts in the technical and methodological processes that constitute corresponding implementations. In this paper, we report on a project of bringing visual analytics to the field of predictive policing. We introduce a process model that includes machine learning as well as visualization and has been developed together with experts from a law enforcement agency. We also showcase a visual analytics tool, called polimaps, that is part of a real-world predictive policing project and implements elements of the proposed process.
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    Combining the Automated Segmentation and Visual Analysis of Multivariate Time Series
    (The Eurographics Association, 2018) Bernard, Jürgen; Bors, Christian; Bögl, Markus; Eichner, Christian; Gschwandtner, Theresia; Miksch, Silvia; Schumann, Heidrun; Kohlhammer, Jörn; Christian Tominski and Tatiana von Landesberger
    For the automatic segmentation of multivariate time series domain experts at first need to consider a huge space of alternative configurations of algorithms and parameters. We assume that only a small subset of these configurations needs to be computed and analyzed to lead users to meaningful configurations. To expedite this search, we propose the conceptualization of a segmentation workflow. First, with an algorithmic segmentation pipeline, domain experts can calculate segmentation results with different parameter configurations. Second, in an interactive visual analysis step, domain experts can explore segmentation results to further adapt and improve segmentation pipeline in an informed way. In the interactive analysis approach influences of algorithms, parameters, and different types of uncertainty information are conveyed, which is decisive to trigger selective and purposeful re-calculations. The workflow is built upon reflections on collaborations with domain experts working in activity recognition, which also defines our usage scenario demonstrating the applicability of the workflow.
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    Towards Visual Cyber Security Analytics for the Masses
    (The Eurographics Association, 2018) Ulmer, Alex; Schufrin, Marija; Lücke-Tieke, Hendrik; Kannanayikkal, Clindo Devassy; Kohlhammer, Jörn; Christian Tominski and Tatiana von Landesberger
    Understanding network activity and cyber threats is of major concern these days, for business and private users alike. As more and more online applications assist us in our daily life, there is a growing potential vulnerability to cyber crime. With this paper, we want to share our vision of cyber security analytics becoming an accessible everyday task through visual analysis tools. We describe the context of this vision and our experience with the first achievements in this direction. With our new prototype, anyone can analyze their network traffic logs and get security-relevant information out of it, a task that was too difficult and sometimes too expensive in the past. We present an open, accessible and user-friendly visual network analyzer for PCAP (packet capture) files, critically discuss our first prototype, and give an outlook to anomaly detection supported by active learning in this context.
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    A Concept for Consensus-based Ordering of Views
    (The Eurographics Association, 2018) Jentner, Wolfgang; Jäckle, Dominik; Engelke, Ulrich; Keim, Daniel A.; Schreck, Tobias; Christian Tominski and Tatiana von Landesberger
    High-dimensional data poses a significant challenge for analysis, as patterns typically exist only in subsets of dimensions or records. A common approach to reveal patterns, such as meaningful structures or relationships, is to split the data and then to create a visual representation (views) for each data subset. This introduces the problem of ordering the views effectively because patterns can depend on the presented sequence. Existing methods provide metrics and heuristics to achieve an ordering of views based on their data characteristics. However, an effective ordering of subspace views is expected to rely on task- and data-dependent properties. Hence, heuristic-based ordering methods can be highly objective and not relevant to the task at hand, which is why the user involvement is key to find a meaningful ordering. We introduce a concept for a consensus-based ordering of views that learns to form sequences of subset views fitting the overall users' needs. This concept allows users to decide on the ordering freely and accumulates their preference into a global view that reflects the consensus. We showcase and discuss this concept based on ordering colored tiles from the controversially discussed rainbow color map.