EuroVA2022

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2633197
Table of Contents
Human-Model Collaboration and Personalization
ScrutinAI: A Visual Analytics Approach for the Semantic Analysis of Deep Neural Network Predictions
[full paper Image] [meta data Image]
Elena Haedecke, Michael Mock, and Maram Akila
RankASco: A Visual Analytics Approach to Leverage Attribute-Based User Preferences for Item Rankings
[full paper Image] [meta data Image]
Jenny Schmid, Lena Cibulski, Ibrahim Al Hazwani, and Jürgen Bernard
Interactive Visual Explanation of Incremental Data Labeling
[full paper Image] [meta data Image]
Raphael Beckmann, Cristian Blaga, Mennatallah El-Assady, Matthias Zeppelzauer, and Jürgen Bernard
A Comprehensive Workflow for Effective Imitation and Reinforcement Learning with Visual Analytics
[full paper Image] [meta data Image]
Yannick Metz, Udo Schlegel, Daniel Seebacher, Mennatallah El-Assady, and Daniel Keim
Applications
Toward Disease Diagnosis Visual Support Bridging Classic and Precision Medicine
[full paper Image] [meta data Image]
Alessia Palleschi, Manuela Petti, Paolo Tieri, and Marco Angelini
Understanding Business Analysts' Needs for Data Report Authoring
[full paper Image] [meta data Image]
Zhuohao Zhang, Sana Malik, Shunan Guo, Jane Hoffswell, Ryan Rossi, Fan Du, and Eunyee Koh
Voyage Viewer: Empowering Human Mobility at a Global Scale
[full paper Image] [meta data Image]
Isabella Loaiza, Tobin South, Germán Sánchez, Serena Chan, Alice Yu, Felipe Montes, Mohsen Bahrami, and Alex Pentland
CryptoComparator: A Visual Analytics Environment for Cryptocurrencies Analysis
[full paper Image] [meta data Image]
Pietro Manganelli Conforti, Matteo Emanuele, Pietro Nardelli, Giuseppe Santucci, and Marco Angelini
Visual Analytics Techniques
A Pipeline for Tailored Sampling for Progressive Visual Analytics
[full paper Image] [meta data Image]
Marius Hogräfer, Jakob Burkhardt, and Hans-Jörg Schulz
Reordering Sets of Parallel Coordinates Plots to Highlight Differences in Clusters
[full paper Image] [meta data Image]
Elliot Koh, Michael Blumenschein, Lin Shao, and Tobias Schreck
Multivariate Time Series Retrieval with Symbolic Aggregate Approximation, Regular Expression, and Query Expansion
[full paper Image] [meta data Image]
Yuncong Yu, Tim Becker, and Michael Behrisch
CODAS: Integrating Business Analytics and Report Authoring
[full paper Image] [meta data Image]
Zhuohao Zhang, Sana Malik, Shunan Guo, Jane Hoffswell, Ryan Rossi, Fan Du, and Eunyee Koh
Towards Understanding Edit Histories of Multivariate Graphs
[full paper Image] [meta data Image]
Philip Berger, Heidrun Schumann, and Christian Tominski

BibTeX (EuroVA2022)
@inproceedings{
10.2312:eurova.20222009,

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

editor = {
Bernard, Jürgen
 and 
Angelini, Marco
},
title = {{
EuroVa 2022: Frontmatter}},

author = {
Bernard, Jürgen
 and 
Angelini, Marco
},
year = {
2022},

publisher = {
The Eurographics Association},


ISSN = {2664-4487},
ISBN = {978-3-03868-183-0},

DOI = {
10.2312/eurova.20222009}

}
@inproceedings{
10.2312:eurova.20221071,

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

editor = {
Bernard, Jürgen
 and 
Angelini, Marco
},
title = {{
ScrutinAI: A Visual Analytics Approach for the Semantic Analysis of Deep Neural Network Predictions}},

author = {
Haedecke, Elena
 and 
Mock, Michael
 and 
Akila, Maram
},
year = {
2022},

publisher = {
The Eurographics Association},


ISSN = {2664-4487},
ISBN = {978-3-03868-183-0},

DOI = {
10.2312/eurova.20221071}

}
@inproceedings{
10.2312:eurova.20221072,

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

editor = {
Bernard, Jürgen
 and 
Angelini, Marco
},
title = {{
RankASco: A Visual Analytics Approach to Leverage Attribute-Based User Preferences for Item Rankings}},

author = {
Schmid, Jenny
 and 
Cibulski, Lena
 and 
Hazwani, Ibrahim Al
 and 
Bernard, Jürgen
},
year = {
2022},

publisher = {
The Eurographics Association},


ISSN = {2664-4487},
ISBN = {978-3-03868-183-0},

DOI = {
10.2312/eurova.20221072}

}
@inproceedings{
10.2312:eurova.20221073,

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

editor = {
Bernard, Jürgen
 and 
Angelini, Marco
},
title = {{
Interactive Visual Explanation of Incremental Data Labeling}},

author = {
Beckmann, Raphael
 and 
Blaga, Cristian
 and 
El-Assady, Mennatallah
 and 
Zeppelzauer, Matthias
 and 
Bernard, Jürgen
},
year = {
2022},

publisher = {
The Eurographics Association},


ISSN = {2664-4487},
ISBN = {978-3-03868-183-0},

DOI = {
10.2312/eurova.20221073}

}
@inproceedings{
10.2312:eurova.20221074,

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

editor = {
Bernard, Jürgen
 and 
Angelini, Marco
},
title = {{
A Comprehensive Workflow for Effective Imitation and Reinforcement Learning with Visual Analytics}},

author = {
Metz, Yannick
 and 
Schlegel, Udo
 and 
Seebacher, Daniel
 and 
El-Assady, Mennatallah
 and 
Keim, Daniel
},
year = {
2022},

publisher = {
The Eurographics Association},


ISSN = {2664-4487},
ISBN = {978-3-03868-183-0},

DOI = {
10.2312/eurova.20221074}

}
@inproceedings{
10.2312:eurova.20221077,

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

editor = {
Bernard, Jürgen
 and 
Angelini, Marco
},
title = {{
Voyage Viewer: Empowering Human Mobility at a Global Scale}},

author = {
Loaiza, Isabella
 and 
South, Tobin
 and 
Sánchez, Germán
 and 
Chan, Serena
 and 
Yu, Alice
 and 
Montes, Felipe
 and 
Bahrami, Mohsen
 and 
Pentland, Alex
},
year = {
2022},

publisher = {
The Eurographics Association},


ISSN = {2664-4487},
ISBN = {978-3-03868-183-0},

DOI = {
10.2312/eurova.20221077}

}
@inproceedings{
10.2312:eurova.20221075,

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

editor = {
Bernard, Jürgen
 and 
Angelini, Marco
},
title = {{
Toward Disease Diagnosis Visual Support Bridging Classic and Precision Medicine}},

author = {
Palleschi, Alessia
 and 
Petti, Manuela
 and 
Tieri, Paolo
 and 
Angelini, Marco
},
year = {
2022},

publisher = {
The Eurographics Association},


ISSN = {2664-4487},
ISBN = {978-3-03868-183-0},

DOI = {
10.2312/eurova.20221075}

}
@inproceedings{
10.2312:eurova.20221076,

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

editor = {
Bernard, Jürgen
 and 
Angelini, Marco
},
title = {{
Understanding Business Analysts' Needs for Data Report Authoring}},

author = {
Zhang, Zhuohao
 and 
Malik, Sana
 and 
Guo, Shunan
 and 
Hoffswell, Jane
 and 
Rossi, Ryan
 and 
Du, Fan
 and 
Koh, Eunyee
},
year = {
2022},

publisher = {
The Eurographics Association},


ISSN = {2664-4487},
ISBN = {978-3-03868-183-0},

DOI = {
10.2312/eurova.20221076}

}
@inproceedings{
10.2312:eurova.20221080,

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

editor = {
Bernard, Jürgen
 and 
Angelini, Marco
},
title = {{
Reordering Sets of Parallel Coordinates Plots to Highlight Differences in Clusters}},

author = {
Koh, Elliot
 and 
Blumenschein, Michael
 and 
Shao, Lin
 and 
Schreck, Tobias
},
year = {
2022},

publisher = {
The Eurographics Association},


ISSN = {2664-4487},
ISBN = {978-3-03868-183-0},

DOI = {
10.2312/eurova.20221080}

}
@inproceedings{
10.2312:eurova.20221079,

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

editor = {
Bernard, Jürgen
 and 
Angelini, Marco
},
title = {{
A Pipeline for Tailored Sampling for Progressive Visual Analytics}},

author = {
Hogräfer, Marius
 and 
Burkhardt, Jakob
 and 
Schulz, Hans-Jörg
},
year = {
2022},

publisher = {
The Eurographics Association},


ISSN = {2664-4487},
ISBN = {978-3-03868-183-0},

DOI = {
10.2312/eurova.20221079}

}
@inproceedings{
10.2312:eurova.20221081,

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

editor = {
Bernard, Jürgen
 and 
Angelini, Marco
},
title = {{
Multivariate Time Series Retrieval with Symbolic Aggregate Approximation, Regular Expression, and Query Expansion}},

author = {
Yu, Yuncong
 and 
Becker, Tim
 and 
Behrisch, Michael
},
year = {
2022},

publisher = {
The Eurographics Association},


ISSN = {2664-4487},
ISBN = {978-3-03868-183-0},

DOI = {
10.2312/eurova.20221081}

}
@inproceedings{
10.2312:eurova.20221078,

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

editor = {
Bernard, Jürgen
 and 
Angelini, Marco
},
title = {{
CryptoComparator: A Visual Analytics Environment for Cryptocurrencies Analysis}},

author = {
Conforti, Pietro Manganelli
 and 
Emanuele, Matteo
 and 
Nardelli, Pietro
 and 
Santucci, Giuseppe
 and 
Angelini, Marco
},
year = {
2022},

publisher = {
The Eurographics Association},


ISSN = {2664-4487},
ISBN = {978-3-03868-183-0},

DOI = {
10.2312/eurova.20221078}

}
@inproceedings{
10.2312:eurova.20221083,

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

editor = {
Bernard, Jürgen
 and 
Angelini, Marco
},
title = {{
Towards Understanding Edit Histories of Multivariate Graphs}},

author = {
Berger, Philip
 and 
Schumann, Heidrun
 and 
Tominski, Christian
},
year = {
2022},

publisher = {
The Eurographics Association},


ISSN = {2664-4487},
ISBN = {978-3-03868-183-0},

DOI = {
10.2312/eurova.20221083}

}
@inproceedings{
10.2312:eurova.20221082,

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

editor = {
Bernard, Jürgen
 and 
Angelini, Marco
},
title = {{
CODAS: Integrating Business Analytics and Report Authoring}},

author = {
Zhang, Zhuohao
 and 
Malik, Sana
 and 
Guo, Shunan
 and 
Hoffswell, Jane
 and 
Rossi, Ryan
 and 
Du, Fan
 and 
Koh, Eunyee
},
year = {
2022},

publisher = {
The Eurographics Association},


ISSN = {2664-4487},
ISBN = {978-3-03868-183-0},

DOI = {
10.2312/eurova.20221082}

}

Browse

Recent Submissions

Now showing 1 - 14 of 14
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    EuroVa 2022: Frontmatter
    (The Eurographics Association, 2022) Bernard, Jürgen; Angelini, Marco; Bernard, Jürgen; Angelini, Marco
  • Thumbnail Image
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    ScrutinAI: A Visual Analytics Approach for the Semantic Analysis of Deep Neural Network Predictions
    (The Eurographics Association, 2022) Haedecke, Elena; Mock, Michael; Akila, Maram; Bernard, Jürgen; Angelini, Marco
    We present ScrutinAI, a Visual Analytics approach to exploit semantic understanding for deep neural network (DNN) predictions analysis, focusing on models for object detection and semantic segmentation. Typical fields of application for such models, e.g. autonomous driving or healthcare, have a high demand for detecting and mitigating data- and model-inherent shortcomings. Our approach aims to help analysts use their semantic understanding to identify and investigate potential weaknesses in DNN models. ScrutinAI therefore includes interactive visualizations of the model's inputs and outputs, interactive plots with linked brushing, and data filtering with textual queries on descriptive meta data. The tool fosters hypothesis driven knowledge generation which aids in understanding the model's inner reasoning. Insights gained during the analysis process mitigate the ''black-box character'' of the DNN and thus support model improvement and generation of a safety argumentation for AI applications. We present a case study on the investigation of DNN models for pedestrian detection from the automotive domain.
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    RankASco: A Visual Analytics Approach to Leverage Attribute-Based User Preferences for Item Rankings
    (The Eurographics Association, 2022) Schmid, Jenny; Cibulski, Lena; Hazwani, Ibrahim Al; Bernard, Jürgen; Bernard, Jürgen; Angelini, Marco
    Item rankings are useful when a decision needs to be made, especially if there are multiple attributes to be considered. However, existing tools either do not support both categorical and numerical attributes, require programming expertise for expressing preferences on attributes, do not offer instant feedback, or lack flexibility in expressing various types of user preferences. In this work, we present RankASco: a human-centered visual analytics approach that supports the interactive and visual creation of rankings. RankASco leverages a series of visual interfaces, enabling broad user groups to a) select attributes of interest, b) express preferences on attribute scorings based on different mental models, and c) analyze and refine item ranking results.
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    Interactive Visual Explanation of Incremental Data Labeling
    (The Eurographics Association, 2022) Beckmann, Raphael; Blaga, Cristian; El-Assady, Mennatallah; Zeppelzauer, Matthias; Bernard, Jürgen; Bernard, Jürgen; Angelini, Marco
    We present a visual analytics approach for the in-depth analysis and explanation of incremental machine learning processes that are based on data labeling. Our approach offers multiple perspectives to explain the process, i.e., data characteristics, label distribution, class characteristics, and classifier characteristics. Additionally, we introduce metrics from which we derive novel aggregated analytic views that enable the analysis of the process over time. We demonstrate the capabilities of our approach in a case study and thereby demonstrate how our approach improves the transparency of the iterative learning process.
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    A Comprehensive Workflow for Effective Imitation and Reinforcement Learning with Visual Analytics
    (The Eurographics Association, 2022) Metz, Yannick; Schlegel, Udo; Seebacher, Daniel; El-Assady, Mennatallah; Keim, Daniel; Bernard, Jürgen; Angelini, Marco
    Multiple challenges hinder the application of reinforcement learning algorithms in experimental and real-world use cases even with recent successes in such areas. Such challenges occur at different stages of the development and deployment of such models. While reinforcement learning workflows share similarities with machine learning approaches, we argue that distinct challenges can be tackled and overcome using visual analytic concepts. Thus, we propose a comprehensive workflow for reinforcement learning and present an implementation of our workflow incorporating visual analytic concepts integrating tailored views and visualizations for different stages and tasks of the workflow.
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    Voyage Viewer: Empowering Human Mobility at a Global Scale
    (The Eurographics Association, 2022) Loaiza, Isabella; South, Tobin; Sánchez, Germán; Chan, Serena; Yu, Alice; Montes, Felipe; Bahrami, Mohsen; Pentland, Alex; Bernard, Jürgen; Angelini, Marco
    The challenge of refugee relocation is fertile ground to pose a new direction in the quest for extended human intelligence: developing systems that leverage big data, and the power of social learning to provide personalized visual analytics for big life decisions. To probe into this new avenue, this paper presents Voyage Viewer, a novel open-access multi-stream data dashboard called Voyage Viewer. It helps individuals make their own relocation and migration decisions given personalized queries and visualizations, which stands in contrast to previous top-down approaches that use algorithms to match individuals and places, as is the case for some refugee relocation programs. Voyage Viewer hopes to foster social learning between community members to improve the match between migrants and their potential new communities so that both can reap the benefits of the move.
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    Toward Disease Diagnosis Visual Support Bridging Classic and Precision Medicine
    (The Eurographics Association, 2022) Palleschi, Alessia; Petti, Manuela; Tieri, Paolo; Angelini, Marco; Bernard, Jürgen; Angelini, Marco
    The traditional approach in medicine starts with investigating patients' symptoms to make a diagnosis. While with the advent of precision medicine, a diagnosis results from several factors that interact and need to be analyzed together. This added complexity asks for increased support for medical personnel in analyzing these data altogether. Our objective is to merge the traditional approach with network medicine to offer a tool to investigate together symptoms, anatomies, diseases, and genes to establish a diagnosis from different points of view. This paper aims to help the clinician with the typical workflow of disease analysis, proposing a Visual Analytics tool to ease this task. A use case demonstrates the benefits of the proposed solution.
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    Understanding Business Analysts' Needs for Data Report Authoring
    (The Eurographics Association, 2022) Zhang, Zhuohao; Malik, Sana; Guo, Shunan; Hoffswell, Jane; Rossi, Ryan; Du, Fan; Koh, Eunyee; Bernard, Jürgen; Angelini, Marco
    Business analysts often create static, data-driven reports to summarize and communicate findings from marketing dashboards. However, the requirements and workflow for creating data-driven reports in business analytics have not been fully investigated. In this work, we interviewed fifteen professional analysts to understand their unique needs for data-driven report authoring and identify gaps between their goals, technical skills, and existing reporting tools. Our findings suggest eight fundamental takeaways for report authoring, such as the need for persistent interactive experiences combined with more robust narrative authoring for linking story pieces and customizing the narrative layout. Based on these interviews, we synthesize the results into five design guidelines to direct future analytic reporting tools.
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    Reordering Sets of Parallel Coordinates Plots to Highlight Differences in Clusters
    (The Eurographics Association, 2022) Koh, Elliot; Blumenschein, Michael; Shao, Lin; Schreck, Tobias; Bernard, Jürgen; Angelini, Marco
    Visualizing high-dimensional (HD) data is a key challenge for data scientists. The importance of this challenge is to properly map data properties, e.g., patterns, outliers, and correlations, from a HD data space onto a visualization. Parallel coordinate plots (PCPs) are a common way to do this. However, a PCP visualization can be arranged in several ways by reordering its axes, which may lead to different visual representations. Many methods have been developed with the aim of evaluating the quality of reorderings of given PCP view. A high-dimensional data set can be divided into multiple classes, and being able to identify differences between the classes is important. Then, besides overlaying the groups in a single PCP, we can show the different groups in individual PCPs in a small multiple fashion. This raises the problem of jointly reordering sets of PCPs to create meaningful reorderings of the set of plots. We propose a joint reordering strategy, based on maximizing the pairwise visual difference in PCPs, such as to support their contrastive comparison. We present an implementation and an evaluation of the reordering strategy to assess the effectiveness of the method. The approach shows feasible in bringing out pairwise difference in PCP plots and hence support comparison of grouped data.
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    A Pipeline for Tailored Sampling for Progressive Visual Analytics
    (The Eurographics Association, 2022) Hogräfer, Marius; Burkhardt, Jakob; Schulz, Hans-Jörg; Bernard, Jürgen; Angelini, Marco
    Progressive Visual Analytics enables analysts to interactively work with partial results from long-running computations early on instead of forcing them to wait. For very large datasets, the first step is to divide that input data into smaller chunks using sampling, which are then passed down the progressive analysis pipeline all the way to their progressive visualization in the end. The quality of the partial results produced by the progression heavily depends on the quality of these chunks, that is, chunks need to be representative of the dataset. Whether or not a sampling approach produces representative chunks does however depend on the particular analysis scenario. This stands in contrast to the common use of random sampling as a ''one-size-fits-most'' approach in PVA. In this paper, we propose a sampling pipeline and its open source implementation which can be used to tailor the used sampling method for an analysis scenario at hand. This pipeline consists of three configurable steps - linearization, subdivision, and selection - and for each, we propose exemplar operators. We then demonstrate its utility by providing tailored samplings for three distinct scenarios.
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    Multivariate Time Series Retrieval with Symbolic Aggregate Approximation, Regular Expression, and Query Expansion
    (The Eurographics Association, 2022) Yu, Yuncong; Becker, Tim; Behrisch, Michael; Bernard, Jürgen; Angelini, Marco
    We present SAXRegEx, a method for pattern search in multivariate time series in the presence of various distortions, such as duration variation, warping, and time delay between signals. For example, in the automotive industry, calibration engineers spontaneously search for event-induced patterns in fresh measurements under time pressure. Current methods do not sufficiently address duration (horizontal along the time axis) scaling and inter-track time delay. One reason is that it can be overwhelmingly complex to consider scaling and warping jointly and analyze temporal dynamics and attribute interrelation simultaneously. SAXRegEx meets this challenge with a novel symbolic representation modeling adapted to handle time series with multiple tracks. We employ methods from text retrieval, i.e., regular expression matching, to perform a pattern retrieval and develop a novel query expansion algorithm to deal flexibly with pattern distortions. Experiments show the effectiveness of our approach, especially in the presence of such distortions, and its efficiency surpassing the state-of-the-art methods. While we design the method primarily for automotive data, it is well transferable to other domains.
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    CryptoComparator: A Visual Analytics Environment for Cryptocurrencies Analysis
    (The Eurographics Association, 2022) Conforti, Pietro Manganelli; Emanuele, Matteo; Nardelli, Pietro; Santucci, Giuseppe; Angelini, Marco; Bernard, Jürgen; Angelini, Marco
    Cryptocurrencies are a novel phenomenon in the finance world that is gaining more attention from the general public, banks, investors, and lately also academic research. A characteristic of cryptocurrencies is to be the target of investments that, due to the volatility of most of the cryptocurrencies, tends to be at high risk and behave very differently from classic currencies. A way of reducing this risk is to look at the history of existing cryptocurrencies and compare them in order to spot promising trends for increased gain. This paper introduces CryptoComparator, a Visual Analytics tool designed for allowing analysis of correlations and trends of cryptocurrencies. The system exploits an initial proposal for a double elliptic graph layout, reconfigurable with three different ordering functions, in order to support fast visual search of cryptocurrencies by correlation strength. One usecase developed with a domain expert in cryptocurrency financial activities demonstrates qualitatively the usage of the system.
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    Towards Understanding Edit Histories of Multivariate Graphs
    (The Eurographics Association, 2022) Berger, Philip; Schumann, Heidrun; Tominski, Christian; Bernard, Jürgen; Angelini, Marco
    The visual analysis of multivariate graphs increasingly involves not only exploring the data, but also editing them. Existing editing approaches for multivariate graphs support visual analytics workflows by facilitating a seamless switch between data exploration and editing. However, it remains difficult to comprehend performed editing operations in retrospect and to compare different editing results. Addressing these challenges, we propose a model describing what graph aspects can be edited and how. Based on this model, we develop a novel approach to visually track and understand data changes due to edit operations. To visualize the different graph states resulting from edits, we extend an existing graph visualization approach so that graph structure and the associated multivariate attributes can be represented together. Branching sequences of edits are visualized as a node-link tree layout where nodes represent graph states and edges visually encode the performed edit operations and the graph aspects they affect. Individual editing operations can be inspected by dynamically expanding edges to detail views on demand. In addition, we support the comparison of graph states through an interactive creation of attribute filters that can be applied to other states to highlight similarities.
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    CODAS: Integrating Business Analytics and Report Authoring
    (The Eurographics Association, 2022) Zhang, Zhuohao; Malik, Sana; Guo, Shunan; Hoffswell, Jane; Rossi, Ryan; Du, Fan; Koh, Eunyee; Bernard, Jürgen; Angelini, Marco
    Business analysts create rich dashboards to find data insights and subsequently communicate these findings with data-driven reports that combine visualization screenshots and descriptive text. Conventional analytics reports convey findings statically and passively, which suffers from limited interactivity and adaptability to data changes. There is therefore a need to facilitate authoring of interactive reports in business analytics. To better support the needs of business analysts, we developed CODAS: a report authoring tool that allows analysts to transform dashboards into interactive, web-based reports through a no-coding user interface and a workflow that is compatible to business analysts' existing data analytics pipelines. CODAS supports authoring multiple levels of interactions, organizing story elements, and generating the final artifact. Through our case studies with two expert analysts, we discuss the usefulness of our system and report our findings on analysts' report authoring workflow. Our findings suggest that CODAS enables business analysts to create interactive, data-driven reports comfortably, and can complement their exisitng data analytics workflow without extra learning effort.