Building a Shared Conceptual Model of Complex, Heterogeneous Data Systems: A Demonstration

Michael R. Anderson, Yuze Lou, Jiayun Zou, Michael Cafarella, Sarah Chasins, Doug Downey, Tian Gao, Kexin Huang, Dinghao Shen, Jenny Vo-Phamhi, Yitong Wang, Yuning Wang, Anna Zeng

Research output: Contribution to conferencePaperpeer-review

Abstract

The world of data objects and systems is complex and heterogeneous, making collaboration across tools, teams, and institutions difficult. Important goals like effective data science, responsible data governance, and well-informed data consumption all require participation from multiple parties who share conceptual data models despite being unfamiliar with, or organizationally distant from each other. In order to be productive together, data collaborators need a shared conceptual model that includes traditional schemas and system models, such as pipelines and procedures. This shared model does not have to be entirely correct, but to enable effective collaboration, it should be tool-, team-, and institution-independent. We describe a working demonstration system that aims to build this shared conceptual model. This system borrows ideas from knowledge graphs and other massive collaborative efforts to curate data artifacts beyond the reach of any one person or institution.

Original languageEnglish (US)
StatePublished - 2022
Event12th Annual Conference on Innovative Data Systems Research, CIDR 2022 - Santa Cruz, United States
Duration: Jan 9 2022Jan 12 2022

Conference

Conference12th Annual Conference on Innovative Data Systems Research, CIDR 2022
Country/TerritoryUnited States
CitySanta Cruz
Period1/9/221/12/22

ASJC Scopus subject areas

  • Artificial Intelligence
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management

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