MRAT: The Mixed Reality Analytics Toolkit

Nebeling, Michael; Speicher, Maximilian; Wang, Xizi; Rajaram, Shwetha; Hall, Brian D.; Xie, Zijian; Raistrick, Alexander R. E.; Aebersold, Michelle; Happ, Edward G.; Wang, Jiayin; Ya’nan, Sun; Zhang, Lotus; Ramsier, Leah; Kulkarni, Rhea

doi:10.1145/3313831.3376330

Cited by 48 publications

(16 citation statements)

References 26 publications

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“…It would be interesting to enrich this pipeline with the integration of V-NLI. What's more, the emergence of various toolkits for immersive analytics [27], [28], [175] provides a great opportunity to expand V-NLI to immersive scenes. Besides, as described in Section 7, view transformations are rarely involved in existing systems.…”

Section: Presentationmentioning

confidence: 99%

Towards Natural Language Interfaces for Data Visualization: A Survey

Shen,

Luo

et al. 2021

Preprint

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Utilizing Visualization-oriented Natural Language Interfaces (V-NLI) as a complementary input modality to direct manipulation for visual analytics can provide an engaging user experience. It enables users to focus on their tasks rather than worrying about operating the interface to visualization tools. In the past two decades, leveraging advanced natural language processing technologies, numerous V-NLI systems have been developed both within academic research and commercial software, especially in recent years. In this article, we conduct a comprehensive review of the existing V-NLIs. In order to classify each paper, we develop categorical dimensions based on a classic information visualization pipeline with the extension of a V-NLI layer. The following seven stages are used: query understanding, data transformation, visual mapping, view transformation, human interaction, context management, and presentation. Finally, we also shed light on several promising directions for future work in the community.

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Section: Presentationmentioning

confidence: 99%

Towards Natural Language Interfaces for Data Visualization: A Survey

Shen,

Luo

et al. 2021

Preprint

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“…It is therefore preferable to assess AR UI design metrics through systematic approaches and even automation, with prominent features of real-time monitoring of system performance, direct information collection via user-device interaction in AR, and more proactive responses to improve user perceptions to AR information [102]. However, to the best of our knowledge, the number of existing evaluation frameworks is very limited and their scopes are limited to specific contexts and scenarios, such as disaster management [167]. More generic evaluation frameworks with a high selectivity of AR evaluation metrics pose research opportunities in the domain of AR interface designs [73,102].…”

Section: Metric Evaluations For Ar Uismentioning

confidence: 99%

Mobile Augmented Reality: User Interfaces, Frameworks, and Intelligence

Cao,

Lam,

Lee

et al. 2021

Preprint

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Mobile Augmented Reality (MAR) integrates computer-generated virtual objects with physical environments for mobile devices. MAR systems enable users to interact with MAR devices, such as smartphones and headworn wearables, and performs seamless transitions from the physical world to a mixed world with digital entities. These MAR systems support user experiences by using MAR devices to provide universal accessibility to digital contents. Over the past 20 years, a number of MAR systems have been developed, however, the studies and design of MAR frameworks have not yet been systematically reviewed from the perspective of user-centric design. This article presents the first effort of surveying existing MAR frameworks (count: 37) and further discusses the latest studies on MAR through a top-down approach: (1) MAR applications; (2) MAR visualisation techniques adaptive to user mobility and contexts; (3) systematic evaluation of MAR frameworks including supported platforms and corresponding features such as tracking, feature extraction plus sensing capabilities; and (4) underlying machine learning approaches supporting intelligent operations within MAR systems. Finally, we summarise the development of emerging research fields, current state-of-the-art, and discuss the important open challenges and possible theoretical and technical directions. This survey aims to benefit both researchers and MAR system developers alike. CCS Concepts: • Human-centered computing → Mixed / augmented reality; Graphical user interfaces; • Software and its engineering → Development frameworks and environments; • Computing methodologies → Machine learning approaches.

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“…NexP [43] helps beginners design user studies for HCI. MRAT [47] enables experiment designers to collect and visualize data coming from interaction techniques developed in Unity, and test them using diverse tasks without coding. Another possible approach is the use of TestBeds, which have successfully been used for evaluating virtual reality selection and manipulation techniques [8,52].…”

Section: Designing User Studies For Information Visualizationmentioning

confidence: 99%

Flex-ER

Lobo

Hurter

Irani

2020

Proc. ACM Hum.-Comput. Interact.

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Extended Reality (XR) systems (which encapsulate AR, VR and MR) is an emerging field which enables the development of novel visualization and interaction techniques. To develop and to assess such techniques, researchers and designers have to face choices in terms of which development tools to adopt, and with very little information about how such tools support some of the very basic tasks for information visualization, such as selecting data items, linking and navigating. As a solution, we propose Flex-ER, a flexible web-based environment that enables users to prototype, debug and share experimental conditions and results. Flex-ER enables users to quickly switch between hardware platforms and input modalities by using a JSON specification that supports both defining interaction techniques and tasks at a low cost. We demonstrate the flexibility of the environment through three task design examples: brushing, linking and navigating. A qualitative user study suggest that Flex-ER can be helpful to prototype and explore different interaction techniques for immersive analytics. CCS Concepts: • Human-centered computing → User studies; Empirical studies in HCI; Empirical studies in visualization; Mixed / augmented reality; Virtual reality.

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MRAT: The Mixed Reality Analytics Toolkit

Cited by 48 publications

References 26 publications

Towards Natural Language Interfaces for Data Visualization: A Survey

Towards Natural Language Interfaces for Data Visualization: A Survey

Mobile Augmented Reality: User Interfaces, Frameworks, and Intelligence

Flex-ER

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