Longitudinal Data Index Structures for Faster Event Timeline Queries
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies face inefficiencies in managing and querying longitudinal data, particularly in healthcare settings, due to the complexity of tracking sequential events across large datasets, leading to time-consuming and resource-intensive database inquiries.
Innovation Solution
A computer-implemented system utilizing a two-stage data structure arrangement comprising a cohort index structure and an ontologies-storing structure, along with a specialized extraction framework, to efficiently manage and extract longitudinal data, enabling interactive chronological timelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional database structures are used to store longitudinal data, then data storage is simple, but querying and extracting sequential events becomes time-consuming and resource-intensive
Solution Approach 1:
The patent segments longitudinal data into discrete events with specific schemas, organizing them by event type rather than storing as continuous records. This segmentation allows for targeted querying of specific event types without scanning entire datasets, dramatically improving query performance and reducing inquiry time.
Solution Approach 2:
The patent performs preliminary organization of data into structured event schemas during data ingestion, pre-defining event types and their properties. This preliminary structuring enables rapid retrieval and extraction of sequential events without requiring complex processing during query operations.
2Adaptability or versatility
If complex extraction frameworks are implemented to handle longitudinal data, then data extraction capability is improved, but system complexity and resource consumption increase
Solution Approach 1:
The patent implements a universal event schema framework that can handle multiple types of longitudinal data (medical records, sensor data, transaction logs) through a single standardized structure. This multi-functional approach provides versatile extraction capability without requiring separate complex systems for different data types, thereby reducing overall system complexity.
Solution Approach 2:
The patent uses configurable event schema parameters to adapt the extraction framework to different data types and requirements. By changing parameters such as event type definitions, property schemas, and temporal relationships, the system achieves high adaptability without structural complexity changes.
Data Source
AI summary
In some embodiments, the present disclosure provides for an exemplary computer-implemented system that may include a longitudinal data engine, including: a processor and specialized index generation software to generate: an index data structure for a respective event type associated with each respective subject or object; where each respective index data structure is a respective event type-specific data schema, defining how to store events of a particular event type to form longitudinal data of each respective subject or object; an ontology data structure that is configured to describe one or more properties of a respective event of a respective subject or object; and longitudinal data extraction software to extract a respective longitudinal data for a plurality of index data structures and a plurality of ontology data structures associated with a plurality of subjects or objects.