Collaborative Data Exploration through Visualization: A Thesis Proposal Analyzing Impact of Conversational Assistants (2025)

Abstract: Data visualization is integral to any Exploratory Data Analysis (EDA) task. However, generating visualization requires expertise, presenting a steep learning curve and a significant cognitive load. Natural language interfaces for EDA aim to lower this barrier by allowing users to generate visualizations through natural language queries. However, complexity remains when EDA is performed collaboratively, requiring an environment to support multi-user interaction. In this thesis proposal, we discuss challenges in user-system interaction in a collaborative multi-user setup, such as errors in visualization generation due to misinterpretation of user requests. We hypothesize that a Conversational Assistant (CA) capable of understanding user-initiated clarification requests and generating accurate responses can improve user experience and support collaborative EDA tasks. To this end, we propose to develop such a CA (Figure tab:system_issues) and evaluate it through a user study, thus examining its impact on user experience in a collaborative environment for EDA.

## introduction eda is a method for analyzing data that predominantly uses graphical techniques such as bar charts, heatmaps etc., to uncover patterns, outliers, and insights (national institute of standards and technology (nist), 2023) from the data. originating from john tukey's exploratory data analysis @xcite , over the years, eda has evolved @xcite @xcite to become a vital tool across domains like healthcare, finance, and education @xcite . while visualization generation plays a crucial role in eda, the steep learning curve associated with traditional tools often excludes non-technical users, who face challenges in adopting these techniques for decision-making @xcite . to address these challenges, sarker suggests developing user-friendly tools catering to non-technical users to foster a more inclusive and accessible data-driven work culture. oftentimes, eda is done in multi-user collaborative settings that leverage users' diverse perspectives to enhance sense-making. however, existing visualization tools such as tableau, ms excel, and plotly cater primarily to single users, limiting multi-user collaboration @xcite @xcite . this underscores a need for extending tools to support data exploration in collaborative environments, also keeping in mind the need to make such systems accessible to nontechnical users. the best approach for modeling such a tool would be a natural language interface, with which users can perform eda by generating data visualizations in a collaborative multi-user environment. further, users should be able to tell the system what they want in an accessible setup. this entails a ca with which users can engage using natural language, and the system should mediate between the user and the visualization generator. however, human conversations are often characterized by incomplete queries, ambiguous utterances and coreferences. this necessitates the ca to accommodate the characteristics of human conversation and respond meaningfully to ensure a positive experience for the users. recently @xcite conducted a thorough analysis by comparing an extension of the ca articulate2 @xcite with articulate+ @xcite through user studies and listed extensive insight from their findings (discussed in section 3.1). we use these insights to motivate our research objectives and start by systematically investigating user experiences with conversational interfaces for collaborative multi-user data visualization (section 3). first, we look at challenges impacting the user's interaction with the ca in this user study. specifically, we examine how clarification requests initiated by the users during their interaction with the ca might help improve the user's experience in a multi-user, collaborative eda task scenario. through this work, our goal is not only to contribute a ca framework, but also an understanding of how clarification behavior affects the interaction quality in multi-user collaborating conversational interfaces. clarification requests: clarification requests (crs) play a crucial role in grounding-the process of establishing mutual understanding in dialogue @xcite . when humans engage in a conversation, a speaker requests clarification when they do not understand the form or content of the utterance of the other speaker. while grounding seems natural in humanhuman conversation, in human-system dialogue, it is not trivial, and so is identification and generation of clarification by the conversational system. early foundational work by @xcite categorized clarification requests (crs) into reprise interrogatives-including echo and reference questions-and elliptical forms like reprise sluices. @xcite expanded this with non-reprise clarifications, gaps, and gap fillers, while @xcite and @xcite introduced finer-grained categories such as partial repetitions, reformulations, semantic clarifications, and acoustic misunderstandings. much of the existing research on cr has focused on those initiated by the system, typically triggered by ambiguous user input, speech recognition errors, or underspecified intent. a comprehensive overview of these can be found in the work of @xcite . in contrast, in this thesis, the focus is on user-initiated clarification requests. one notable effort in this direction is the work by @xcite , who annotated user-initiated crs in the codraw dataset @xcite , a multi-modal, goal-oriented collaborative dialogue corpus. their study highlights how instruction followers request clarification when facing ambiguous instructions, underscoring the importance of modeling such interactions in collaborative settings. ## proposed research effective collaboration in an eda task requires a ca to enable users to interact naturally, as with a human collaborator. this effectiveness also depends on its ability to respond correctly to user inputs. after a closer inspection of user interactions from @xcite 's work, we found some challenges that impact the system's usability and overall user experience. we put forward these challenges next and discuss how they lead us to the research question of this proposal. ## conclusion overall, this thesis proposal emphasizes the importance of designing a ca for eda and evaluating it in real-time with users. beyond eda, developing such a user-centric ca framework has broader implications for data-driven decision-making. with 77% of u.s. organizations relying on such datadriven strategies foot_1 , an interactive ca can help non-technical users make data-informed decisions. recent studies @xcite further highlight the role of data-driven methodologies in organizations of all sizes. by enabling intuitive, context-aware interactions, such a ca framework can enhance collaborative data exploration and make data visualization more accessible, thereby improving decision-making across diverse domains.