DeepAI AI Chat
Log In Sign Up

Model-Adaptive Interface Generation for Data-Driven Discovery

by   Hongsuda Tangmunarunkit, et al.
USC Information Sciences Institute

Discovery of new knowledge is increasingly data-driven, predicated on a team's ability to collaboratively create, find, analyze, retrieve, and share pertinent datasets over the duration of an investigation. This is especially true in the domain of scientific discovery where generation, analysis, and interpretation of data are the fundamental mechanisms by which research teams collaborate to achieve their shared scientific goal. Data-driven discovery in general, and scientific discovery in particular, is distinguished by complex and diverse data models and formats that evolve over the lifetime of an investigation. While databases and related information systems have the potential to be valuable tools in the discovery process, developing effective interfaces for data-driven discovery remains a roadblock to the application of database technology as an essential tool in scientific investigations. In this paper, we present a model-adaptive approach to creating interaction environments for data-driven discovery of scientific data that automatically generates interactive user interfaces for editing, searching, and viewing scientific data based entirely on introspection of an extended relational data model. We have applied model-adaptive interface generation to many active scientific investigations spanning domains of proteomics, bioinformatics, neuroscience, occupational therapy, stem cells, genitourinary, craniofacial development, and others. We present the approach, its implementation, and its evaluation through analysis of its usage in diverse scientific settings.


page 11

page 21

page 22

page 27


Seven Principles for Effective Scientific Big-DataSystems

We should be in a golden age of scientific discovery, given that we have...

Learning from learning machines: a new generation of AI technology to meet the needs of science

We outline emerging opportunities and challenges to enhance the utility ...

Automating Truth: The Case for Crowd-Powered Scientific Investigation in Economics

Scientific investigation procedures have been evolving to follow an ever...

A Preliminary Review of Influential Works in Data-Driven Discovery

The Gordon and Betty Moore Foundation ran an Investigator Competition as...

Detecting Quality Problems in Research Data: A Model-Driven Approach

As scientific progress highly depends on the quality of research data, t...

Visual analytics of set data for knowledge discovery and member selection support

Visual analytics (VA) is a visually assisted exploratory analysis approa...

Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction

The increasing size and complexity of scientific data could dramatically...