A longitudinal meta-database query is a type of database query that involves the analysis of data over a period of time. This can involve looking at changes in data over time and comparing data across different time points.
A metadatabase query engine identifies information about other databases. In the context of a longitudinal metadatabase query, this might include information about the structure and content of different databases that contain data collected over time.
An example of a longitudinal metadatabase query might be used to analyze changes in sales data for a company for several years. It could involve combining data from multiple databases containing sales data for a different year and analyzing the data to identify trends and patterns.
An alternative example could be a study that aims to understand trends in healthcare utilization over time. The study might gather data from multiple sources, such as hospital records, insurance claims, and patient surveys, and analyze the data to identify healthcare service trends or a specific cohort required for developing an ML prediction model.
In summary, a longitudinal metadatabase query is a powerful tool for analyzing data over time to understand how data changes over the long term and to develop a specific cohort based on data residing in multiple data repositories.
Genetica’s Graphiti module is an intuitive Longitudinal Meta Database Query Engine that requires no prior knowledge of database structures or SQL query coding, which empowers business stakeholders to run queries without the dependency on data engineers.
Like to know more about Genetica’s Graphiti(T)module?
Contact Genetica @: www.genetica.ai/contact-us/
