Data Ingestion and Content Explorer (DICE)
Description
Multiple stakeholders across HFP have the business need to search for content within artifacts and documents uploaded to various systems. For systems like CARA and CARTS, document search capabilities are limited due to the Appian technology stack utilized by these systems. As the Human Food Data Platform continues to grow, it will also need to provide SMEs with the capability to search within the data platform. Offices need a quicker way to search for content within documents and databases to find relevant data across a multitude of use cases including regulatory and compliance reviews, outbreak response investigation, and research tracking and administration. Additionally, multiple HFP offices have business processes requiring extracting structured data from unstructured documents for data analysis, regulatory reviews, and other business intelligence insights which are currently supported through manual operations. DICE will enable SMEs to obtain properly formatted structured data from unstructured data sources.
Detailed example
The system includes a user interface that allows users to view returned search results, templatize unstructured documents using the intelligent document processing workflow, and view extracted text with confidence scores from unstructured documents.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
b) Pilot – The use case has been deployed in a limited test or pilot capacity.
Expected benefit
Accelerates the time for subject matter experts (SMEs) to find relevant data and content lost within images, hand-written documents, emails, and other artifacts and provides this in in a one-stop-shop user experience. Allows users to search through millions of documents quickly and makes data accessible to everyone in the HFP and not just those who have backend access. Extracting text from these artifacts makes it available for further analysis and natural language processing. Data can be further processed to detect sentiment, entities, key phrases, syntax, and topics. AWS and API based architecture brings a flexible and scalable framework to HFP to facilitate search use cases while enabling a cost-effective solution. Shared infrastructure for unstructured and structured intelligent search capabilities minimizes cost across CFSAN offices who have this same need.
Controls / human review
ATO: No; PIA: Not published
Data needed
CARTS system data, CARA system data.