COLLEGE PARK, Md. (PRNewswire) — University of Maryland faculty and graduate students in computer science and economics, together with a colleague from UCLA, have created the largest national database of food safety inspection information.
In the U.S., such inspections are done by local public health departments, which can take different approaches to conducting, coding and reporting inspection data. Using this unique new automated database, food service businesses and consumers can monitor and compare food safety practices from outlets across the nation.
The national database was developed by UMD Professor of Computer Science Ben Bederson, UMD Professor of Economics Ginger Jin, UCLA Associate Professor of Business Management Philip Leslie, new Ph.D. graduate Alexander Quinn (computer science) and UMD Ph.D. graduate student Ben Zou (economics).
According to Bederson, who also is UMD's Associate Provost of Learning Initiatives and Executive Director of its Teaching and Learning Transformation Center, the team's database uses data robots to automatically collect data from local government websites, and represents a huge leap from local and state databases that are built using manually-collected and sometimes poorly correlated data, and which can easily miss the big picture and have little impact on compliance actions.
"Building our system to reliably collect information from so many different jurisdictions was a formidable engineering challenge," said Bederson.
Another difficulty was developing normalization algorithms to compare data across jurisdictions where the data is very different. For some web pages, the team had to write custom 'scrapers' to get the data, and for others they had to interpret already available databases.
"Our data robots cover a large number of local jurisdictions across the U.S., continuously detecting new data posted by each jurisdiction, and integrating them into a single, standardized, and cumulative database," Bederson said, noting that the result is a database that is cost-effective, robust and scalable compared to manual alternatives.
The researchers also developed analytical tools that can be used to compare inspection outcomes across localities and states, and across chain and individual food outlets, such as restaurants, cafes, convenience stores, and grocery markets. This can improve inspection efficiency and promote retailer compliance, resulting in a decrease in food-borne illnesses, according to Bederson.
The team has created a regulatory data analytics company, Hazel Analytics, which according to Bederson is a direct outgrowth of their academic collaboration around food safety inspection data funded by the Sloan Foundation.
For non-commercial use, the database is publicly available at InspectionRepo.com at no cost.
"As we shared our work with industry players, government agencies such as the FDA and CDC, and other academics, our intuition was confirmed that there was commercial value in our database and analytical approach," said Bederson.
Hazel Analytics now produces a commercial grade restaurant inspection database and analytical services for the food service industry.
"We are currently in close talks with several major national chains. We expect to have our first paying customers this year," Bederson said.
The University of Maryland's Office of Technology Commercialization (OTC) helped Bederson and his group to develop, license and commercialize the technology.
"With OTC's help, we have worked closely with the Maryland Technology Development Cooperation (TEDCO) and received Phase I $100K funding from the Maryland Innovation Initiative (MII) program," Bederson said.
Bederson plans to apply the web 'scraping' technology to other inspection programs implemented by the local governments.