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On April 22, 2021, during the Orbit 2021 symposium of the TypeDB community, I explained the progress of our work around the use case "What are the impacts of Semantic Graph Technologies in the Analysis of Impacts of Anti-Covid Measures?”
In fact, the challenges of the open-source project: PKN12 (Pandemic Knowledge Network, I will come back to the meaning of the number 12 ) are to propose new tools for analyzing the data (open-data) available of the epidemic, using TypeDB.
There is a rise in industry-specific data analytics solutions because building up and maintaining custom data warehouses is difficult. It requires extensive development and operational efforts to define the appropriate industry-specific data model for the business intelligence tools, follow all the shape changes over time (new tables, new columns, new relationships) and design the ETL processes for a wide variety of data sources. It is just hard to build a solution on top of a generic data warehouse where you can get great platform capabilities but you still have to start with a CREATE DATABASE SQL command.
This is the reason why Veeva decided to build Nitro, the data science and analytics platform. It is designed to accelerate time-to-value by getting data quickly from Veeva Commercial Cloud (CRM, Vault, Align, Network) and other common life sciences platforms (e.g. Salesforce Marketing Cloud) into Nitro using predefined intelligent connectors.
Debrief from a Grakn Community talk — featuring Alessia Basadonne, executive PHD candidate from University of Pavia and Medas Italy. This talk was delivered live at Grakn Cosmos 2020 in London, UK.
“From when I was very very little, I always dreamed of developing crazy ideas and making them a reality.”
Alessia’s current work is in developing a Clinical Decision Support System (CDSS). This isn’t a new concept as she highlights, but one with a lot of opportunity for improvements and developments. So…
This blog post covers use cases and architectures for Apache Kafka and Event Streaming in Pharma and Life Sciences. The technical example explores drug development and discovery with real time data processing, machine learning, workflow orchestration and image / video processing.
The following shows some of the use cases I have seen in the field in pharma and life sciences: