Coping with Volume and Variety in Temporal Event Sequences: Strategies for Sharpening Analytic Focus

Event sequence datasets with high event cardinality and long sequences are difficult to visualize and analyze. In particular, it is hard to generate a high level visual summary of paths and volume of flow. Existing approaches of mining and visualizing frequent sequential patterns look promising, but have limitations in terms of scalability, interpretability and utility. We propose CoreFlow, a technique that automatically extracts and visualizes branching patterns in event sequences. CoreFlow constructs a tree by recursively applying a three‐step procedure: rank events, divide sequences into groups, and trim sequences by the chosen event. The resulting tree contains key events as nodes, and links represent aggregated flows between key events. Based on CoreFlow, we have developed an interactive system for event sequence analysis. Our approach can compute branching patterns for millions of events in a few seconds, with improved interpretability of extracted patterns compared to previous work. We also present case studies of using the system in three different domains and discuss success and failure cases of applying CoreFlow to real‐world analytic problems. These case studies call forth future research on metrics and models to evaluate the quality of visual summaries of event sequences.

Section: Discussionmentioning

confidence: 99%

CoreFlow: Extracting and Visualizing Branching Patterns from Event Sequences

Liu

Kerr

Dontcheva

et al. 2017

“…Event Sequences Event sequence exploration has been extensively studied within the visualization community [DSP∗17, PMR∗96, AMST11]. A common representation for events is to show them as linear sequences of glyphs, while providing interactive means to manipulate the sequences to perform tasks such as querying or exploration [BM13, Shn96].…”

Section: Related Workmentioning

confidence: 99%

“…The work of Du et al [DSP∗17] focuses mainly on event sequence analysis and exploration, but it enumerates strategies that can be used to outline the capabilities of ChronoCorrelator, to allow for easier comparison with existing tools. Our system supports extraction of records and categories (strategies S1 & S2), enables time windowing of the sequences by using time series as a filter (S5) and through the use of both predefined & custom categories it can be used to group events together (S9).…”

Section: Approachmentioning

confidence: 99%

ChronoCorrelator: Enriching Events with Time Series

Dortmont

Elzen

Wijk

2019

Event sequences and time series are widely recorded in many application domains; examples are stock market prices, electronic health records, server operation and performance logs. Common goals for recording are monitoring, root cause analysis and predictive analytics. Current analysis methods generally focus on the exploration of either event sequences or time series. However, deeper insights are gained by combining both. We present a visual analytics approach where users can explore both time series and event data simultaneously, combining visualization, automated methods and human interaction. We enable users to iteratively refine the visualization. Correlations between event sequences and time series can be found by means of an interactive algorithm, which also computes the presence of monotonic effects. We illustrate the effectiveness of our method by applying it to real world and synthetic data sets.

“…One possibility is to use dynamic time warping [BC94] techniques to cluster the scroll timeseries, however the size of our data set made this computationally infeasible. The time‐series could be transformed into a series of events (e.g., navigations between article sections) and analyzed using event flow visualization, however there are challenges in using these techniques at scale [DSP∗ 17], and we wished to be able to inspect nuanced scroll behavior within sections. To gain insight into patterns of reader behavior, we created ScrollHop (Figures 1, 4), a tool that visualizes each visit to the page as a single line displaying scroll position over time.…”

Section: Tools For Analyzing Reader Activitymentioning

confidence: 99%

Capture & Analysis of Active Reading Behaviors for Interactive Articles on the Web

Conlen

Kale

Heer

2019

Journalists, educators, and technical writers are increasingly publishing interactive content on the web. However, popular analytics tools provide only coarse information about how readers interact with individual pages, and laboratory studies often fail to capture the variability of a real‐world audience. We contribute extensions to the Idyll markup language to automate the detailed instrumentation of interactive articles and corresponding visual analysis tools for inspecting reader behavior at both micro‐ and macro‐levels. We present three case studies of interactive articles that were instrumented, posted online, and promoted via social media to reach broad audiences, and share data from over 50,000 reader sessions. We demonstrate the use of our tools to characterize article‐specific interaction patterns, compare behavior across desktop and mobile devices, and reveal reading patterns common across articles. Our contributed findings, tools, and corpus of behavioral data can help advance and inform more comprehensive studies of narrative visualization.