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Finding the root causes of anomalies in cloud computing systems quickly is crucial to ensure availability and efficiency since accurate root causes can guide engineers to take appropriate actions to address the anomalies and maintain customer satisfaction. However, it is difficult to investigate and identify the root causes based on large-scale and high-dimension monitoring data collected from complex cloud computing environments. Due to the inherently dynamic characteristics of cloud computing systems, the existing approaches in practice largely rely on manual analyses for flexibility and reliability, but massive unpredictable factors and high data complexity make the process time-consuming. Despite recent advances in automated detection and investigation approaches, the speed and quality of root cause analyses remain limited by the lack of expert involvement in these approaches. The limitations found in the current solutions motivate us to propose a visual analytics approach that facilitates the interactive investigation of the anomaly root causes in cloud computing systems. We identified three challenges, namely, a) modeling databases for the root cause investigation, b) inferring root causes from large-scale time series, and c) building comprehensible investigation results. In collaboration with domain experts, we addressed these challenges with RCInvestigator, a novel visual analytics system that establishes a tight collaboration between human and machine and assists experts in investigating the root causes of cloud computing system anomalies. We evaluated the effectiveness of RCInvestigator through two use cases based on real-world data and received positive feedback from experts.

Spreadsheets are widely recognized as the most popular end-user programming tools, which blend the power of formula-based computation, with an intuitive table-based interface. Today, spreadsheets are used by billions of users to manipulate tables, most of whom are neither database experts nor professional programmers. Despite the success of spreadsheets, authoring complex formulas remains challenging, as non-technical users need to look up and understand non-trivial formula syntax. To address this pain point, we leverage the observation that there is often an abundance of similar-looking spreadsheets in the same organization, which not only have similar data, but also share similar computation logic encoded as formulas. We develop an Auto-Formula system that can accurately predict formulas that users want to author in a target spreadsheet cell, by learning and adapting formulas that already exist in similar spreadsheets, using contrastive-learning techniques inspired by "similar-face recognition" from compute vision. Extensive evaluations on over 2K test formulas extracted from real enterprise spreadsheets show the effectiveness of Auto-Formula over alternatives. Our benchmark data is available at https://github.com/microsoft/Auto-Formula to facilitate future research., Comment: full version of a paper to appear in SIGMOD 2024

Writing formulas on spreadsheets, such as Microsoft Excel and Google Sheets, is a widespread practice among users performing data analysis. However, crafting formulas on spreadsheets remains a tedious and error-prone task for many end-users, particularly when dealing with complex operations. To alleviate the burden associated with writing spreadsheet formulas, this paper introduces a novel benchmark task called NL2Formula, with the aim to generate executable formulas that are grounded on a spreadsheet table, given a Natural Language (NL) query as input. To accomplish this, we construct a comprehensive dataset consisting of 70,799 paired NL queries and corresponding spreadsheet formulas, covering 21,670 tables and 37 types of formula functions. We realize the NL2Formula task by providing a sequence-to-sequence baseline implementation called fCoder. Experimental results validate the effectiveness of fCoder, demonstrating its superior performance compared to the baseline models. Furthermore, we also compare fCoder with an initial GPT-3.5 model (i.e., text-davinci-003). Lastly, through in-depth error analysis, we identify potential challenges in the NL2Formula task and advocate for further investigation., Comment: To appear at EACL 2024

The use of natural language interfaces (NLIs) for the creation of charts is becoming increasingly popular due to the intuitiveness of natural language interactions. One key challenge in this approach is to accurately capture user intents and transform them to proper chart specifications. This obstructs the wide use of NLI in chart generation, as users' natural language inputs are generally abstract (i.e., ambiguous or under-specified), without a clear specification of visual encodings. Recently, pre-trained large language models (LLMs) have exhibited superior performance in understanding and generating natural language, demonstrating great potential for downstream tasks. Inspired by this major trend, we propose ChartGPT, generating charts from abstract natural language inputs. However, LLMs are struggling to address complex logic problems. To enable the model to accurately specify the complex parameters and perform operations in chart generation, we decompose the generation process into a step-by-step reasoning pipeline, so that the model only needs to reason a single and specific sub-task during each run. Moreover, LLMs are pre-trained on general datasets, which might be biased for the task of chart generation. To provide adequate visualization knowledge, we create a dataset consisting of abstract utterances and charts and improve model performance through fine-tuning. We further design an interactive interface for ChartGPT that allows users to check and modify the intermediate outputs of each step. The effectiveness of the proposed system is evaluated through quantitative evaluations and a user study.

Querying time series based on their relations is a crucial part of multiple time series analysis. By retrieving and understanding time series relations, analysts can easily detect anomalies and validate hypotheses in complex time series datasets. However, current relation extraction approaches, including knowledge- and data-driven ones, tend to be laborious and do not support heterogeneous relations. By conducting a formative study with 11 experts, we concluded 6 time series relations, including correlation, causality, similarity, lag, arithmetic, and meta, and summarized three pain points in querying time series involving these relations. We proposed RelaQ, an interactive system that supports the time series query via relation specifications. RelaQ allows users to intuitively specify heterogeneous relations when querying multiple time series, understand the query results based on a scalable, multi-level visualization, and explore possible relations beyond the existing queries. RelaQ is evaluated with two use cases and a user study with 12 participants, showing promising effectiveness and usability.

Language models, such as GPT-3.5 and ChatGPT, demonstrate remarkable abilities to follow diverse human instructions and perform a wide range of tasks. However, when probing language models using a range of basic table-understanding tasks, we observe that today's language models are still sub-optimal in many table-related tasks, likely because they are pre-trained predominantly on \emph{one-dimensional} natural-language texts, whereas relational tables are \emph{two-dimensional} objects. In this work, we propose a new "\emph{table-tuning}" paradigm, where we continue to train/fine-tune language models like GPT-3.5 and ChatGPT, using diverse table-tasks synthesized from real tables as training data, with the goal of enhancing language models' ability to understand tables and perform table tasks. We show that our resulting Table-GPT models demonstrate (1) better \emph{table-understanding} capabilities, by consistently outperforming the vanilla GPT-3.5 and ChatGPT, on a wide-range of table tasks, including holdout unseen tasks, and (2) strong \emph{generalizability}, in its ability to respond to diverse human instructions to perform new table-tasks, in a manner similar to GPT-3.5 and ChatGPT.

Data charts are prevalent across various fields due to their efficacy in conveying complex data relationships. However, static charts may sometimes struggle to engage readers and efficiently present intricate information, potentially resulting in limited understanding. We introduce "Live Charts," a new format of presentation that decomposes complex information within a chart and explains the information pieces sequentially through rich animations and accompanying audio narration. We propose an automated approach to revive static charts into Live Charts. Our method integrates GNN-based techniques to analyze the chart components and extract data from charts. Then we adopt large natural language models to generate appropriate animated visuals along with a voice-over to produce Live Charts from static ones. We conducted a thorough evaluation of our approach, which involved the model performance, use cases, a crowd-sourced user study, and expert interviews. The results demonstrate Live Charts offer a multi-sensory experience where readers can follow the information and understand the data insights better. We analyze the benefits and drawbacks of Live Charts over static charts as a new information consumption experience., Comment: Accepted by IEEE TVCG

Data visualizations and narratives are often integrated to convey data stories effectively. Among various data storytelling formats, data videos have been garnering increasing attention. These videos provide an intuitive interpretation of data charts while vividly articulating the underlying data insights. However, the production of data videos demands a diverse set of professional skills and considerable manual labor, including understanding narratives, linking visual elements with narration segments, designing and crafting animations, recording audio narrations, and synchronizing audio with visual animations. To simplify this process, our paper introduces a novel method, referred to as Data Player, capable of automatically generating dynamic data videos with narration-animation interplay. This approach lowers the technical barriers associated with creating data videos rich in narration. To enable narration-animation interplay, Data Player constructs references between visualizations and text input. Specifically, it first extracts data into tables from the visualizations. Subsequently, it utilizes large language models to form semantic connections between text and visuals. Finally, Data Player encodes animation design knowledge as computational low-level constraints, allowing for the recommendation of suitable animation presets that align with the audio narration produced by text-to-speech technologies. We assessed Data Player's efficacy through an example gallery, a user study, and expert interviews. The evaluation results demonstrated that Data Player can generate high-quality data videos that are comparable to human-composed ones., Comment: 11 pages, 5 figures, accepted by IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VIS'23)

Creating an animated data video enriched with audio narration takes a significant amount of time and effort and requires expertise. Users not only need to design complex animations, but also turn written text scripts into audio narrations and synchronize visual changes with the narrations. This paper presents WonderFlow, an interactive authoring tool, that facilitates narration-centric design of animated data videos. WonderFlow allows authors to easily specify a semantic link between text and the corresponding chart elements. Then it automatically generates audio narration by leveraging text-to-speech techniques and aligns the narration with an animation. WonderFlow provides a visualization structure-aware animation library designed to ease chart animation creation, enabling authors to apply pre-designed animation effects to common visualization components. It also allows authors to preview and iteratively refine their data videos in a unified system, without having to switch between different creation tools. To evaluate WonderFlow's effectiveness and usability, we created an example gallery and conducted a user study and expert interviews. The results demonstrated that WonderFlow is easy to use and simplifies the creation of data videos with narration-animation interplay., Comment: Accepted by TVCG

Data pipelines are widely employed in modern enterprises to power a variety of Machine-Learning (ML) and Business-Intelligence (BI) applications. Crucially, these pipelines are \emph{recurring} (e.g., daily or hourly) in production settings to keep data updated so that ML models can be re-trained regularly, and BI dashboards refreshed frequently. However, data quality (DQ) issues can often creep into recurring pipelines because of upstream schema and data drift over time. As modern enterprises operate thousands of recurring pipelines, today data engineers have to spend substantial efforts to \emph{manually} monitor and resolve DQ issues, as part of their DataOps and MLOps practices. Given the high human cost of managing large-scale pipeline operations, it is imperative that we can \emph{automate} as much as possible. In this work, we propose Auto-Validate-by-History (AVH) that can automatically detect DQ issues in recurring pipelines, leveraging rich statistics from historical executions. We formalize this as an optimization problem, and develop constant-factor approximation algorithms with provable precision guarantees. Extensive evaluations using 2000 production data pipelines at Microsoft demonstrate the effectiveness and efficiency of AVH., Comment: full version of a paper accepted to KDD 2023

In geographic data videos, camera movements are frequently used and combined to present information from multiple perspectives. However, creating and editing camera movements requires significant time and professional skills. This work aims to lower the barrier of crafting diverse camera movements for geographic data videos. First, we analyze a corpus of 66 geographic data videos and derive a design space of camera movements with a dimension for geospatial targets and one for narrative purposes. Based on the design space, we propose a set of adaptive camera shots and further develop an interactive tool called GeoCamera. This interactive tool allows users to flexibly design camera movements for geographic visualizations. We verify the expressiveness of our tool through case studies and evaluate its usability with a user study. The participants find that the tool facilitates the design of camera movements., Comment: 15 pages. Published as a conference paper at the ACM Conference on Human Factors in Computing Systems (CHI) 2023

Computational notebooks are widely used for data analysis. Their interleaved displays of code and execution results (e.g., visualizations) are welcomed since they enable iterative analysis and preserve the exploration process. However, the communication of data findings remains challenging in computational notebooks. Users have to carefully identify useful findings from useless ones, document them with texts and visual embellishments, and then organize them in different tools. Such workflow greatly increases their workload, according to our interviews with practitioners. To address the challenge, we designed Notable to offer on-the-fly assistance for data storytelling in computational notebooks. It provides intelligent support to minimize the work of documenting and organizing data findings and diminishes the cost of switching between data exploration and storytelling. To evaluate Notable, we conducted a user study with 12 data workers. The feedback from user study participants verifies its effectiveness and usability., Comment: To appear in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI 2023)

A key challenge to visualization authoring is the process of getting familiar with the complex user interfaces of authoring tools. Natural Language Interface (NLI) presents promising benefits due to its learnability and usability. However, supporting NLIs for authoring tools requires expertise in natural language processing, while existing NLIs are mostly designed for visual analytic workflow. In this paper, we propose an authoring-oriented NLI pipeline by introducing a structured representation of users' visualization editing intents, called editing actions, based on a formative study and an extensive survey on visualization construction tools. The editing actions are executable, and thus decouple natural language interpretation and visualization applications as an intermediate layer. We implement a deep learning-based NL interpreter to translate NL utterances into editing actions. The interpreter is reusable and extensible across authoring tools. The authoring tools only need to map the editing actions into tool-specific operations. To illustrate the usages of the NL interpreter, we implement an Excel chart editor and a proof-of-concept authoring tool, VisTalk. We conduct a user study with VisTalk to understand the usage patterns of NL-based authoring systems. Finally, we discuss observations on how users author charts with natural language, as well as implications for future research.

Labeled datasets are essential for supervised machine learning. Various data labeling tools have been built to collect labels in different usage scenarios. However, developing labeling tools is time-consuming, costly, and expertise-demanding on software development. In this paper, we propose a conceptual framework for data labeling and OneLabeler based on the conceptual framework to support easy building of labeling tools for diverse usage scenarios. The framework consists of common modules and states in labeling tools summarized through coding of existing tools. OneLabeler supports configuration and composition of common software modules through visual programming to build data labeling tools. A module can be a human, machine, or mixed computation procedure in data labeling. We demonstrate the expressiveness and utility of the system through ten example labeling tools built with OneLabeler. A user study with developers provides evidence that OneLabeler supports efficient building of diverse data labeling tools.

Composite visualization is a popular design strategy that represents complex datasets by integrating multiple visualizations in a meaningful and aesthetic layout, such as juxtaposition, overlay, and nesting. With this strategy, numerous novel designs have been proposed in visualization publications to accomplish various visual analytic tasks. These well-crafted composite visualizations have formed a valuable collection for designers and researchers to address real-world problems and inspire new research topics and designs. However, there is a lack of understanding of design patterns of composite visualization, thus failing to provide holistic design space and concrete examples for practical use. In this paper, we opted to revisit the composite visualizations in VIS publications and answered what and how visualizations of different types are composed together. To achieve this, we first constructed a corpus of composite visualizations from IEEE VIS publications and decomposed them into a series of basic visualization types (e.g., bar chart, map, and matrix). With this corpus, we studied the spatial (e.g., separated or overlaying) and semantic relationships (e.g., with same types or shared axis) between visualizations and proposed a taxonomy consisting of eight different design patterns (e.g., repeated, stacked, accompanied, and nested). Furthermore, we analyzed and discussed common practices of composite visualizations, such as the distribution of different patterns and correlations between visualization types. From the analysis and examples, we obtained insights into different design patterns on the utilities, advantages, and disadvantages. Finally, we developed an interactive system to help visualization developers and researchers conveniently explore collected examples and design patterns.

Visual elements in an information presentation are often spatially and semantically grouped hierarchically for effective message delivery. Studying the hierarchical grouping information can help researchers and designers better explore layout structures and understand design demographics. However, recovering hierarchical grouping is challenging due to a large number of possibilities for compositing visual elements into a single-page design. This paper introduces an automatic approach that takes the layout of visual elements as input and returns the hierarchical grouping as output. To understand information presentations, we first contribute a dataset of 23,072 information presentations with diverse layouts to the community. Next, we propose our technique with a Transformer-based model to predict relatedness between visual elements and a bottom-up algorithm to produce the hierarchical grouping. Finally, we evaluate our technique through a technical experiment and a user study with 30 designers. The results show that the proposed technique is promising.

We contribute a deep-learning-based method that assists in designing analytical dashboards for analyzing a data table. Given a data table, data workers usually need to experience a tedious and time-consuming process to select meaningful combinations of data columns for creating charts. This process is further complicated by the need of creating dashboards composed of multiple views that unveil different perspectives of data. Existing automated approaches for recommending multiple-view visualizations mainly build on manually crafted design rules, producing sub-optimal or irrelevant suggestions. To address this gap, we present a deep learning approach for selecting data columns and recommending multiple charts. More importantly, we integrate the deep learning models into a mixed-initiative system. Our model could make recommendations given optional user-input selections of data columns. The model, in turn, learns from provenance data of authoring logs in an offline manner. We compare our deep learning model with existing methods for visualization recommendation and conduct a user study to evaluate the usefulness of the system., Comment: Accepted at the IEEE Visualization Conference (IEEE VIS 2021). 11 pages, 10 figures

Animated transitions help viewers understand changes between related visualizations. To clearly present the underlying relations between statistical charts, animation authors need to have a high level of expertise and a considerable amount of time to describe the relations with reasonable animation stages. We present AniVis, an automated approach for generating animated transitions to demonstrate the changes between two statistical charts. AniVis models each statistical chart into a tree-based structure. Given an input chart pair, the differences of data and visual properties of the chart pair are formalized as tree edit operations. The edit operations can be mapped to atomic transition units. Through this approach, the animated transition between two charts can be expressed as a set of transition units. Then, we conduct a formative study to understand people's preferences for animation sequences. Based on the study, we propose a set of principles and a sequence composition algorithm to compose the transition units into a meaningful animation sequence. Finally, we synthesize these units together to deliver a smooth and intuitive animated transition between charts. To test our approach, we present a prototype system and its generated results to illustrate the usage of our framework. We perform a comparative study to assess the transition sequence derived from the tree model. We further collect qualitative feedback to evaluate the effectiveness and usefulness of our method., Comment: Changed to 2-column format

Visualizations themselves have become a data format. Akin to other data formats such as text and images, visualizations are increasingly created, stored, shared, and (re-)used with artificial intelligence (AI) techniques. In this survey, we probe the underlying vision of formalizing visualizations as an emerging data format and review the recent advance in applying AI techniques to visualization data (AI4VIS). We define visualization data as the digital representations of visualizations in computers and focus on data visualization (e.g., charts and infographics). We build our survey upon a corpus spanning ten different fields in computer science with an eye toward identifying important common interests. Our resulting taxonomy is organized around WHAT is visualization data and its representation, WHY and HOW to apply AI to visualization data. We highlight a set of common tasks that researchers apply to the visualization data and present a detailed discussion of AI approaches developed to accomplish those tasks. Drawing upon our literature review, we discuss several important research questions surrounding the management and exploitation of visualization data, as well as the role of AI in support of those processes. We make the list of surveyed papers and related material available online at ai4vis.github.io., Comment: Accepted to IEEE TVCG. 20 pages, 12 figures and 8 tables. The associated website is https://ai4vis.github.io

Distributional text clustering delivers semantically informative representations and captures the relevance between each word and semantic clustering centroids. We extend the neural text clustering approach to text classification tasks by inducing cluster centers via a latent variable model and interacting with distributional word embeddings, to enrich the representation of tokens and measure the relatedness between tokens and each learnable cluster centroid. The proposed method jointly learns word clustering centroids and clustering-token alignments, achieving the state of the art results on multiple benchmark datasets and proving that the proposed cluster-token alignment mechanism is indeed favorable to text classification. Notably, our qualitative analysis has conspicuously illustrated that text representations learned by the proposed model are in accord well with our intuition.

Animated visualization is becoming increasingly popular as a compelling way to illustrate changes in time series data. However, maintaining the viewer's focus throughout the entire animation is difficult because of its time-consuming nature. Viewers are likely to become bored and distracted during the ever-changing animated visualization. Informed by the role of foreshadowing that builds the expectation in film and literature, we introduce visual foreshadowing to improve the engagement of animated visualizations. In specific, we propose designs of visual foreshadowing that engage the audience while watching the animation. To demonstrate our approach, we built a proof-of-concept animated visualization authoring tool that incorporates visual foreshadowing techniques with various styles. Our user study indicates the effectiveness of our foreshadowing techniques on improving engagement for animated visualization.

Infographic is a data visualization technique which combines graphic and textual descriptions in an aesthetic and effective manner. Creating infographics is a difficult and time-consuming process which often requires significant attempts and adjustments even for experienced designers, not to mention novice users with limited design expertise. Recently, a few approaches have been proposed to automate the creation process by applying predefined blueprints to user information. However, predefined blueprints are often hard to create, hence limited in volume and diversity. In contrast, good infogrpahics have been created by professionals and accumulated on the Internet rapidly. These online examples often represent a wide variety of design styles, and serve as exemplars or inspiration to people who like to create their own infographics. Based on these observations, we propose to generate infographics by automatically imitating examples. We present a two-stage approach, namely retrieve-then-adapt. In the retrieval stage, we index online examples by their visual elements. For a given user information, we transform it to a concrete query by sampling from a learned distribution about visual elements, and then find appropriate examples in our example library based on the similarity between example indexes and the query. For a retrieved example, we generate an initial drafts by replacing its content with user information. However, in many cases, user information cannot be perfectly fitted to retrieved examples. Therefore, we further introduce an adaption stage. Specifically, we propose a MCMC-like approach and leverage recursive neural networks to help adjust the initial draft and improve its visual appearance iteratively, until a satisfactory result is obtained. We implement our approach on proportion-related infographics, and demonstrate its effectiveness by sample results and expert reviews.