Supporting effective common ground construction in Asynchronous Collaborative Visual Analytics

During asynchronous collaborative analysis, handoff of partial findings is challenging because externalizations produced by analysts may not adequately communicate their investigative process. To address this challenge, we developed techniques to automatically capture and help encode tacit aspects of the investigative process based on an analyst's interactions, and streamline explicit authoring of handoff annotations. We designed our techniques to mediate awareness of analysis coverage, support explicit communication of progress and uncertainty with annotation, and implicit communication through playback of investigation histories. To evaluate our techniques, we developed an interactive visual analysis system, KTGraph, that supports an asynchronous investigative document analysis task. We conducted a two-phase user study to characterize a set of handoff strategies and to compare investigative performance with and without our techniques. The results suggest that our techniques promote the use of more effective handoff strategies, help increase an awareness of prior investigative process and insights, as well as improve final investigative outcomes.

Section: Externalization Approachesmentioning

confidence: 99%

Section: Externalization Approachesmentioning

confidence: 99%

Supporting Handoff in Asynchronous Collaborative Sensemaking Using Knowledge-Transfer Graphs

Zhao

Glueck

Isenberg

et al. 2018

“…Importance of establishing common ground between members of a collaborating team has been acknowledged in several areas, such as visual analytics [6,7] and linguistics [8]. It is well known that common ground is a requirement for successful communication [6] and that even an imperfect shared knowledge will reduce cost of the communication between collaborators [16].…”

Section: Awareness In Collaborative Workmentioning

confidence: 99%

“…It is well known that common ground is a requirement for successful communication [6] and that even an imperfect shared knowledge will reduce cost of the communication between collaborators [16]. Weaver [36] mentioned that maintaining a visual common ground is the most difficult problem in collaborative visualization.…”

Section: Awareness In Collaborative Workmentioning

confidence: 99%

Supporting Awareness through Collaborative Brushing and Linking of Tabular Data

Hajizadeh

Tory

Leung

2013

Maintaining an awareness of collaborators' actions is critical during collaborative work, including during collaborative visualization activities. Particularly when collaborators are located at a distance, it is important to know what everyone is working on in order to avoid duplication of effort, share relevant results in a timely manner and build upon each other's results. Can a person's brushing actions provide an indication of their queries and interests in a data set? Can these actions be revealed to a collaborator without substantially disrupting their own independent work? We designed a study to answer these questions in the context of distributed collaborative visualization of tabular data. Participants in our study worked independently to answer questions about a tabular data set, while simultaneously viewing brushing actions of a fictitious collaborator, shown directly within a shared workspace. We compared three methods of presenting the collaborator's actions: brushing & linking (i.e. highlighting exactly what the collaborator would see), selection (i.e. showing only a selected item), and persistent selection (i.e. showing only selected items but having them persist for some time). Our results demonstrated that persistent selection enabled some awareness of the collaborator's activities while causing minimal interference with independent work. Other techniques were less effective at providing awareness, and brushing & linking caused substantial interference. These findings suggest promise for the idea of exploiting natural brushing actions to provide awareness in collaborative work.

“…For example, VizWiz [10] leverages efficient data movement to connect visually-impaired users to sighted collaborators to get near real-time answers to visual search questions. This affordance is critical in facilitating distributed collaboration [8,14,19,33,67,76,80], as well as access to distributed information [33,44,48,76]. Efficient data movement techniques also facilitate rapid access to data that is too large to fit in memory.…”

Section: Efficient Data Movementmentioning

confidence: 99%

An Affordance-Based Framework for Human Computation and Human-Computer Collaboration

Crouser

Chang

2012

Abstract-VisualAnalytics is "the science of analytical reasoning facilitated by visual interactive interfaces" [70]. The goal of this field is to develop tools and methodologies for approaching problems whose size and complexity render them intractable without the close coupling of both human and machine analysis. Researchers have explored this coupling in many venues: VAST, Vis, InfoVis, CHI, KDD, IUI, and more. While there have been myriad promising examples of human-computer collaboration, there exists no common language for comparing systems or describing the benefits afforded by designing for such collaboration. We argue that this area would benefit significantly from consensus about the design attributes that define and distinguish existing techniques. In this work, we have reviewed 1,271 papers from many of the top-ranking conferences in visual analytics, human-computer interaction, and visualization. From these, we have identified 49 papers that are representative of the study of human-computer collaborative problem-solving, and provide a thorough overview of the current state-of-the-art. Our analysis has uncovered key patterns of design hinging on humanand machine-intelligence affordances, and also indicates unexplored avenues in the study of this area. The results of this analysis provide a common framework for understanding these seemingly disparate branches of inquiry, which we hope will motivate future work in the field.