Breaking down analysis obstacles in data-driven decision-making

October 9, 2020

Around the world, we face unprecedented challenges, from a always evolving COVID-19 pandemic to more and more violent pure disasters to a warming local weather. 

The issues our society faces as we speak can’t be tackled by scientists inside one self-discipline alone, and a transparent want exists for an open data community that analyzes important information and fosters cross-disciplinary scientific collaboration. 

Wenwen Li
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Wenwen Li, affiliate professor in Arizona State University’s School of Geographical Sciences and Urban Planning, is a part of a multidisciplinary staff of researchers that acquired a $5 million grant from the National Science Foundation’s Convergence Accelerator to create an open, cross-domain “knowledge graph” that can join and hyperlink details and data in new ways in which haven’t been accessible or usable earlier than. 

The data graph will have the ability to incorporate and interpret a number of dimensions of information throughout disciplines and produce related, actionable insights needed to tell essential data-driven choices. 

“Building a domain knowledge graph is a critical step towards developing artificial general intelligence for future machines to reason like human beings,” stated Li, co-principal investigator of the undertaking, who focuses on sensible cyberinfrastructure and geospatial large information analytics. “The IT giants, such as Google and Facebook, have developed enterprise-level knowledge graphs to better understand the world’s information to improve web search and product recommendation.” 

Building a scientific data graph that fashions analysis information may be very difficult — information comes from totally different sources, are encoded in several codecs, are massive in dimension, and are sometimes wanting metadata. Additionally, a lot of the prevailing information are hidden within the deep internet, making their discovery and reuse much more troublesome. 

In the event of their data graph, Li and her staff, which is led by Krzysztof Janowicz of the University of California, Santa Barbara, intention to sort out these points by growing novel AI and machine-learning strategies to find, hyperlink, combine, visualize and share the cross-domain information.   

Tackling the ‘where’ downside

Critical to the analysis, the staff will use “space” and “time” because the central themes to index and enrich scientific information, enabling the data graph to include geospatial evaluation and higher sort out the “where” downside usually requested in environmental and social science analysis. 

They’ve named their resultant data graph, “KnowWhereGraph.”

“By building the KnowWhereGraph, our research will connect and link the data and knowledge dots across disciplines, making them easily discoverable, understandable, usable, replicable and highly interconnected,” Li stated. “This work will break the communication and research barriers across traditional science boundaries and accelerate the scientific discovery process in a novel way.” 

Besides creating novel analysis, the staff’s purpose can be to develop a set of open supply merchandise, together with the graph question and visualization instruments and geoenrichment providers to foster their widespread utilization and commercialization.

Collaborative geospatial analysis 

The KnowWhereGraph undertaking is considered one of 9 initiatives that have been chosen by the NSF Convergence Accelerator out of a Phase I cohort of greater than 40 extremely aggressive groups that have been all centered on high-impact societal options.

The undertaking represents a cross-sector collaboration consisting of a number of universities: University of California, Santa Barbara; Arizona State University; Kansas State University; Michigan State University; and the University of Southern California; trade companions Esri, Oliver Wyman, Hydronos Labs, LLC, IN1OT; Direct Relief as an NGO; and the U.S Geological Survey and the U.S. Department of Agriculture representing the federal government. 

“I am very excited about this project, not only because the important research that we are conducting will bring potentially significant societal benefits and changes, but also because of the opportunity to work in such an amazing team,” Li stated. “I have known these superb scholars for years for their outstanding work, and I am so glad that we now have the opportunity to build a strong collaborative research network to conduct convergence research and promote data science together.”


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