Emanuel Zgraggen

Vizdom: interactive analytics through pen and touch

Machine learning (ML) and advanced statistics are important tools for drawing insights from large datasets. However, these techniques often require human intervention to steer computation towards meaningful results. In this demo, we present Vizdom, a new system for interactive analytics through pen and touch. Vizdoms frontend allows users to visually compose complex workflows of ML and statistics operators on an interactive whiteboard, and the back-end leverages recent advances in workflow compilation techniques to run these computations at interactive speeds. Additionally, we are exploring approximation techniques for quickly visualizing partial results that incrementally refine over time. This demo will show Vizdoms capabilities by allowing users to interactively build complex analytics workflows using real-world datasets.

PanoramicData: Data Analysis through Pen & Touch.

Interactively exploring multidimensional datasets requires frequent switching among a range of distinct but inter-related tasks (e.g., producing different visuals based on different column sets, calculating new variables, and observing the interactions between sets of data). Existing approaches either target specific different problem domains (e.g., data-transformation or data-presentation) or expose only limited aspects of the general exploratory process; in either case, users are forced to adopt coping strategies (e.g., arranging windows or using undo as a mechanism for comparison instead of using side-by-side displays) to compensate for the lack of an integrated suite of exploratory tools. PanoramicData (PD) addresses these problems by unifying a comprehensive set of tools for visual data exploration into a hybrid pen and touch system designed to exploit the visualization advantages of large interactive displays. PD goes beyond just familiar visualizations by including direct UI support for data transformation and aggregation, filtering and brushing. Leveraging an unbounded whiteboard metaphor, users can combine these tools like building blocks to create detailed interactive visual display networks in which each visualization can act as a filter for others. Further, by operating directly on relational-databases, PD provides an approachable visual language that exposes a broad set of the expressive power of SQL including functionally complete logic filtering, computation of aggregates and natural table joins. To understand the implications of this novel approach, we conducted a formative user study with both data and visualization experts. The results indicated that the system provided a fluid and natural user experience for probing multi-dimensional data and was able to cover the full range of queries that the users wanted to pose.

How Progressive Visualizations Affect Exploratory Analysis

The stated goal for visual data exploration is to operate at a rate that matches the pace of human data analysts, but the ever increasing amount of data has led to a fundamental problem: datasets are often too large to process within interactive time frames. Progressive analytics and visualizations have been proposed as potential solutions to this issue. By processing data incrementally in small chunks, progressive systems provide approximate query answers at interactive speeds that are then refined over time with increasing precision. We study how progressive visualizations affect users in exploratory settings in an experiment where we capture user behavior and knowledge discovery through interaction logs and think-aloud protocols. Our experiment includes three visualization conditions and different simulated dataset sizes. The visualization conditions are: (1) blocking, where results are displayed only after the entire dataset has been processed; (2) instantaneous, a hypothetical condition where results are shown almost immediately; and (3) progressive, where approximate results are displayed quickly and then refined over time. We analyze the data collected in our experiment and observe that users perform equally well with either instantaneous or progressive visualizations in key metrics, such as insight discovery rates and dataset coverage, while blocking visualizations have detrimental effects.

Evaluating Subjective Accuracy in Time Series Pattern-Matching Using Human-Annotated Rankings

Finding patterns is a common task in time series analysis which has gained a lot of attention across many fields. A multitude of similarity measures have been introduced to perform pattern searches. The accuracy of such measures is often evaluated objectively using a one nearest neighbor classification (1NN) on labeled time series or through clustering. Prior work often disregards the subjective similarity of time series which can be pivotal in systems where a user specified pattern is used as input and a similarity-based ranking is expected as output (query-by-example). In this paper, we describe how a human-annotated ranking based on real-world queries and datasets can be created using simple crowdsourcing tasks and use this ranking as ground-truth to evaluate the perceived accuracy of existing time series similarity measures. Furthermore, we show how different sampling strategies and time series representations of pen-drawn queries effect the precision of these similarity measures and provide a publicly available dataset which can be used to optimize existing and future similarity search algorithms.

Tableur: Handwritten Spreadsheets

The need for back-of-the-envelope calculations, such as rough projections or simple budget estimations, occurs frequently and oftentimes while being away from desktop computers. While major software vendors have optimized their spreadsheet applications for mobile environments their generality still makes them heavyweight for such tasks. We have built Tableur a spreadsheet-like pen- & touch-based system targeted towards these use cases. Our design revolves around handwriting recognition - all data is represented as digital ink - and gestural commands. Through a rethought cell referencing system and by incorporating standard math notation recognition Tableur allows for simple formula creation and we experiment with techniques that support pattern-based prefilling of cells (Smart Fill) and exploration of what-if scenarios (Reverse Editing).

Investigating the Effect of the Multiple Comparisons Problem in Visual Analysis

The goal of a visualization system is to facilitate dataset-driven insight discovery. But what if the insights are spurious? Features or patterns in visualizations can be perceived as relevant insights, even though they may arise from noise. We often compare visualizations to a mental image of what we are interested in: a particular trend, distribution or an unusual pattern. As more visualizations are examined and more comparisons are made, the probability of discovering spurious insights increases. This problem is well-known in Statistics as the multiple comparisons problem (MCP) but overlooked in visual analysis. We present a way to evaluate MCP in visualization tools by measuring the accuracy of user reported insights on synthetic datasets with known ground truth labels. In our experiment, over 60% of user insights were false. We show how a confirmatory analysis approach that accounts for all visual comparisons, insights and non-insights, can achieve similar results as one that requires a validation dataset.

Towards Interactive Curation & Automatic Tuning of ML Pipelines

Democratizing Data Science requires a fundamental rethinking of the way data analytics and model discovery is done. Available tools for analyzing massive data sets and curating machine learning models are limited in a number of fundamental ways. First, existing tools require well-trained data scientists to select the appropriate techniques to build models and to evaluate their outcomes. Second, existing tools require heavy data preparation steps and are often too slow to give interactive feedback to domain experts in the model building process, severely limiting the possible interactions. Third, current tools do not provide adequate analysis of statistical risk factors in the model development. In this work, we present the first iteration of QuIC-M (pronounced quick-m), an interactive human-in-the-loop data exploration and model building suite. The goal is to enable domain experts to build the machine learning pipelines an order of magnitude faster than machine learning experts while having model qualities comparable to expert solutions.

Safe Visual Data Exploration

Exploring data via visualization has become a popular way to understand complex data. Features or patterns in visualization can be perceived as relevant insights by users, even though they may actually arise from random noise. Moreover, interactive data exploration and visualization recommendation tools can examine a large number of observations, and therefore result in further increasing chance of spurious insights. Thus without proper statistical control, the risk of false discovery renders visual data exploration unsafe and makes users susceptible to questionable inference.To address these problems, we present QUDE, a visual data exploration system that interacts with users to formulate hypotheses based on visualizations and provides interactive control of false discoveries.

Discrete Time Specifications In Temporal Queries

Analysis, exploration, and visualization of time-oriented data are ubiquitous tasks in various application domains, all of which involve the execution of temporal queries. Prior research in interactively specifying the time component for such queries has been focused on defining temporal relationships in data, i.e., querying event sequences through ordinal patterns. However, there has been much less emphasis on how to specify time as a quantitative data dimension in temporal queries. Motivated by the advent of the Internet of Things (IoT), we present a formal model that can be used to represent complex time specifications. Our model is the first step in an effort to enhance temporal user interfaces that enables discrete time specifications through a visual query interface.

cTed: Advancing Selection Mechanisms in Web Browsers

Selecting fragments of content on websites, such as text, lists, tables and images, and copying them to note-taking applications or word processors is a common task for information workers. However, web browsers only offer crude support for selections due to the restrictive underlying HTML/CSS model, making it difficult and sometimes even impossible for users to select and copy content. In this paper, we present cTed, a web browser plugin that allows for selection gestures to be drawn directly onto websites. Our plugin intelligently maps selection gestures to underlying HTML content and thereby enables users to more intuitively mark and extract regions of websites while preserving textual and semantic information.