Visual Analysis of Streaming Data with SAVI and SenseMAP

wo tools were developed for the analysis tasks in the VAST Challenge 2014 Mini-Challenge 3: Social Analytics VIsualiszation (SAVI) and Sense Making with Analytic Provenance (SenseMAP). The SAVI provides linked timeline and geospatial view of the message streams. It allows search and filter based on text analysis, such as named entity extraction and sentiment analysis, and enable analyst to identity interesting events, tracking them temporally and geospatially, and find related information. The SenseMAP allows analysts to record finding, together with the visualisations that lead to it, and generate hypotheses and con- struct narratives. The entire sense making process is captured so it is possible to go back to any previous reasoning state and share or playback the process. The fact that complex investigations are often carried out by several people working collaboratively is supported in our tools by allowing several people to use SAVI concurrently on the same dataset. Used in this mode, each users analyses are kept separate. However, the SenseMap tool supports collaboration and sharing of hypothesis and narrative structures. Each users findings are avail- able to others, and the findings of different users can be compared, combined, merged, and so on, to produce a collective analytic re- sult. The key insights that informed the design were firstly, that it was important to see and interact with dataset from different perspec- tives, and secondly that an essential part of an investigation is the construction and representation of a narrative

SenseMap: Supporting Browser-based Online Sensemaking through Analytic Provenance

Sensemaking is described as the process in which people collect, organize and create representations of information, all centered around some problem they need to understand. People often get lost when solving complicated tasks using big datasets over long periods of exploration and analysis. They may forget what they have done, are unaware of where they are in the context of the overall task, and are unsure where to continue. In this paper, we introduce a tool, SenseMap, to address these issues in the context of browser-based online sensemaking. We conducted a semistructured interview with nine participants to explore their behaviors in online sensemaking with existing browser functionality. A simplified sensemaking model based on Pirolli and Card’s model is derived to better represent the behaviors we found: users iteratively collect information sources relevant to the task, curate them in a way that makes sense, and finally communicate their findings to others. SenseMap automatically captures provenance of user sensemaking actions and provides multi-linked views to visualize the collected information and enable users to curate and communicate their findings. To explore how SenseMap is used, we conducted a user study in a naturalistic work setting with five participants completing the same sensemaking task related to their daily work activities. All participants found the visual representation and interaction of the tool intuitive to use. Three of them engaged with the tool and produced successful outcomes. It helped them to organize information sources, to quickly find and navigate to the sources they wanted, and to effectively communicate their findings

SenseMap: supporting browser-based online sensemaking through analytic provenance

Sensemaking is described as the process in which people collect, organize and create representations of information, all centered around some problem they need to understand. People often get lost when solving complicated tasks using big datasets over long periods of exploration and analysis. They may forget what they have done, are unaware of where they are in the context of the overall task, and are unsure where to continue. In this paper, we introduce a tool, SenseMap, to address these issues in the context of browser-based online sensemaking. We conducted a semi-structured interview with nine participants to explore their behaviors in online sensemaking with existing browser functionality. A simplified sensemaking model based on Pirolli and Card's model is derived to better represent the behaviors we found: users iteratively collect information sources relevant to the task, curate them in a way that makes sense, and finally communicate their findings to others. SenseMap automatically captures provenance of user sensemaking actions and provides multi-linked views to visualize the collected information and enable users to curate and communicate their findings. To explore how SenseMap is used, we conducted a user study in a naturalistic work setting with five participants completing the same sensemaking task related to their daily work activities. All participants found the visual representation and interaction of the tool intuitive to use. Three of them engaged with the tool and produced successful outcomes. It helped them to organize information sources, to quickly find and navigate to the sources they wanted, and to effectively communicate their findings

Visualization of analytic provenance for sensemaking

Sensemaking is an iterative and dynamic process, in which people collect data relevant to their tasks, analyze the collected information to produce new knowledge, and possibly inform further actions. During the sensemaking process, it is difficult for the human’s working memory to keep track of the progress and to synthesize a large number of individual findings and derived hypotheses, thus limits the performance. Analytic provenance captures both the data exploration process and and its accompanied reasoning, potentially addresses these information overload and disorientation problems. Visualization can help recall, revisit and reproduce the sensemaking process through visual representations of provenance data. More interesting and challenging, analytic provenance has the potential to facilitate the ongoing sensemaking process rather than providing only post hoc support. This thesis addresses the challenge of how to design interactive visualizations of analytic provenance data to support such an iterative and dynamic sensemaking. Its original contribution includes four visualizations that help users explore complex temporal and reasoning relationships hidden in the sensemaking problems, using both automatically and manually captured provenance. First SchemaLine, a timeline visualization, enables users to construct and refine narratives from their annotations. Second, TimeSets extends SchemaLine to explore more complex relationships by visualizing both temporal and categorical information simultaneously. Third, SensePath captures and visualizes user actions to enable analysts to gain a deep understanding of the user’s sensemaking process. Fourth, SenseMap visualization prevents users from getting lost, synthesizes new relationship from captured information, and consolidates their understanding of the sensemaking problem. All of these four visualizations are developed using a user-centered design approach and evaluated empirically to explore how they help target users make sense of their real tasks. In summary, this thesis contributes novel and validated interactive visualizations of analytic provenance data that enable users to perform effective sensemaking

TimeSets: temporal sensemaking in intelligence analysis

TimeSets is a temporal data visualization technique designed to reveal insights into event sets, such as all the events linked to one person or organization. In this paper we describe two TimeSets-based visual analytics tools for intelligence analysis. In the first case, TimeSets is integrated with other visual analytics tools to support open-source intelligence analysis with Twitter data, particularly the challenge of finding the right questions to ask. The second case uses TimeSets in a participatory design process with analysts that aims to meet their requirements of uncertainty analysis involving fake news. Lessons learned are potentially beneficial to other application domains

Visual analysis of streaming data with SAVI and SenseMAP

Two tools were developed for the analysis tasks in the VAST Challenge 2014 Mini-Challenge 3: Social Analytics VIsualiszation (SAVI) and Sense Making with Analytic Provenance (SenseMAP)