Scalable Data Structure for Translating Disparate Event Data
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Solution Overview
Problem
Existing data storage systems face challenges in handling datasets that are individually structured but collectively do not follow a consistent data model, making it difficult to store and retrieve data from multiple sources with varying data structures.
Innovation Solution
A system that translates event data into a scalable data structure, allowing for the generation of a unified schema that accommodates different types of event data with varying timing indications, enabling easy retrieval and visualization of events across multiple data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is stored in a structured way to enable easy retrieval, then data retrieval efficiency is improved, but flexibility to accommodate different data models deteriorates
Solution Approach 1:
The patent implements a dynamic data structure where the schema can be automatically adapted to different data models. The system translates event data from various sources into a standardized time-series format, allowing the structure to dynamically adjust to accommodate different event types, timing indications, and data formats while maintaining efficient retrieval through consistent indexing and querying mechanisms.
Solution Approach 2:
The patent creates a universal data structure that can handle multiple types of event data from different sources. By defining a common schema with flexible fields for event type, timing, and attributes, the system enables a single data structure to serve multiple purposes and accommodate various data models without sacrificing retrieval efficiency.
2Quantity of substance
If data from multiple sources with varying structures is stored together, then data comprehensiveness is improved, but data structure consistency deteriorates
Solution Approach 1:
The patent applies parameter changes by transforming event data from different sources into a standardized format with consistent parameters. The translation process maps various event types, timing indications, and data structures into a unified schema with standardized fields for time, event type, and attributes, ensuring structure consistency while preserving the comprehensiveness of multi-source data.
Solution Approach 2:
The patent introduces an intermediary translation layer that mediates between diverse data sources and the unified data structure. This translation component acts as a buffer that receives various event data formats, transforms them into the standardized schema, and outputs consistent structured data, thereby maintaining both comprehensiveness and consistency.
3Measurement precision
If event data with different timing indications is normalized to a single time series, then data comparability is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the normalization process into distinct translation rules for different event types and timing indications. By breaking down the complex normalization task into manageable segments with specific translation logic for each event category, the system achieves precise data comparability while keeping processing complexity manageable through modular rule-based transformation.
Data Source
AI summary
The disclosure relates to systems and methods of translating event data to generate a scalable data structure to identify or predict an event of interest such as a clinical diagnosis. The scalable data structure is expandable to accommodate various types of event data each having different types of timing indications on a timeline. The system may translate the event data in a way that event data can inherit event data values from other event data in a single time series of events. The scalable data structure may be used to generate unified visualizations of all translated events as well as for forecasting and predicting events of interest. The scalable data structure may be implemented in various contexts such as for clinical diagnostics in which clinical trial data or medical health data from various sources are translated to generate the scalable data structure.