Precision Interfaces for Different Modalities

Zhang, Haoci; Raj, Viraj; Sellam, Thibault; Wu, Eugene

doi:10.1145/3183713.3193570

Cited by 7 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Precision Interface series of work [5,6,35] explores interface generation from queries. PI [36] uses SQL queries as a proxy and generates interactive widgets from a sequence of input queries.…”

Section: Precision Interfacementioning

confidence: 99%

PI2: End-to-end Interactive Visualization Interface Generation from Queries

Chen

2022

Proceedings of the 2022 International Conference on Management of Data

Self Cite

View full text Add to dashboard Cite

Figure 1: This demonstrates the NL2INTERFACE user interface. A user uploads the COVID-19 dataset using the left panel and the table contents are displayed in the bottom left of the interface. Then, this user types two natural language queries in the top textbox. Our NL2INTERFACE will return an interactive interface consisting of two visualizations, one toggle, two button sets, and a click interaction over the map visualization. The user can interact with the interface to study the covid death trend within the last 30 days in Texas by clicking the state of "Texas" on the map, clicking the "deaths" button, and then clicking the "-30 days" button. The interface after these interactions is shown on the right.

show abstract

Section: Precision Interfacementioning

confidence: 99%

PI2: End-to-end Interactive Visualization Interface Generation from Queries

Chen

2022

Proceedings of the 2022 International Conference on Management of Data

Self Cite

View full text Add to dashboard Cite

show abstract

“…This requires an interactive interface where domain experts can look at subsets of interest and iteratively join and filter the data [ 62 ] to find the required cohort. Recently, query logs have been used to design precision interfaces [ 63 , 64 ] that customize the interface for the user’s task.…”

Section: Challenges In the Data Pipelinementioning

confidence: 99%

Amplifying Domain Expertise in Clinical Data Pipelines

2020

View full text Add to dashboard Cite

Digitization of health records has allowed the health care domain to adopt data-driven algorithms for decision support. There are multiple people involved in this process: a data engineer who processes and restructures the data, a data scientist who develops statistical models, and a domain expert who informs the design of the data pipeline and consumes its results for decision support. Although there are multiple data interaction tools for data scientists, few exist to allow domain experts to interact with data meaningfully. Designing systems for domain experts requires careful thought because they have different needs and characteristics from other end users. There should be an increased emphasis on the system to optimize the experts’ interaction by directing them to high-impact data tasks and reducing the total task completion time. We refer to this optimization as amplifying domain expertise. Although there is active research in making machine learning models more explainable and usable, it focuses on the final outputs of the model. However, in the clinical domain, expert involvement is needed at every pipeline step: curation, cleaning, and analysis. To this end, we review literature from the database, human-computer information, and visualization communities to demonstrate the challenges and solutions at each of the data pipeline stages. Next, we present a taxonomy of expertise amplification, which can be applied when building systems for domain experts. This includes summarization, guidance, interaction, and acceleration. Finally, we demonstrate the use of our taxonomy with a case study.

show abstract

“…□ Discussion: Note that our definition of widgets simply specifies a path and domain, and is not bound any specific visual representation. This allows Precision Interfaces to be easily extended to new interaction components or even different modalities such as voice or touch gestures [54]. Since Precision Interfaces operates at the syntactic level, certain combinations of AST transformations might lead to non-executable queries.…”

Section: Interface Generation Problemmentioning

confidence: 99%

Mining Precision Interfaces From Query Logs

Zhang

Sellam

et al. 2019

Proceedings of the 2019 International Conference on Management of Data

Self Cite

View full text Add to dashboard Cite

Interactive tools make data analysis more efficient and more accessible to end-users by hiding the underlying query complexity and exposing interactive widgets for the parts of the query that matter to the analysis. However, creating custom tailored (i.e., precise) interfaces is very costly, and automated approaches are desirable. We propose a syntactic approach that uses queries from an analysis to generate a tailored interface. We model interface widgets as functions I(q) → q ′ that modify the current analysis query q, and interfaces as the set of queries that its widgets can express. Our system, Precision Interfaces, analyzes structural changes between input queries from an analysis, and generates an output interface with widgets to express those changes. Our experiments on the Sloan Digital Sky Survey query log suggest that Precision Interfaces can generate useful interfaces for simple unanticipated tasks, and our optimizations can generate interfaces from logs of up to 10,000 queries in ≤ 10s.

show abstract

Precision Interfaces for Different Modalities

Cited by 7 publications

References 13 publications

PI2: End-to-end Interactive Visualization Interface Generation from Queries

PI2: End-to-end Interactive Visualization Interface Generation from Queries

Amplifying Domain Expertise in Clinical Data Pipelines

Mining Precision Interfaces From Query Logs

Contact Info

Product

Resources

About