Chromatography Data Decoding for Multi-Instrument Access
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Solution Overview
Problem
Conventional chromatography data processing systems require each application to be programmed to interpret different types of data streams from various instruments, limiting the types of analyses that can be performed and necessitating redundant data storage and duplication.
Innovation Solution
A chromatography data processing environment utilizing autonomous services with RESTful endpoints that expose decoders to interpret raw data from multiple instruments, allowing applications to request data without needing to understand its format, enabling storage and retrieval of diverse data types in a centralized repository.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If each application is programmed to interpret different data streams from various instruments, then applications can process data from multiple sources, but the complexity of application programming increases and limits the types of analyses that can be performed
Solution Approach 1:
The patent introduces a data service layer with standardized RESTful endpoints that acts as an intermediary between chromatography instruments and applications. This service layer handles data interpretation and decoding, allowing applications to retrieve data through uniform interfaces without needing to understand instrument-specific data formats, thereby reducing programming complexity while maintaining multi-instrument compatibility
Solution Approach 2:
The system segments the data processing architecture into distinct layers: instrument data acquisition, data service processing with decoders, and application consumption. By separating data interpretation functions into the data service layer, the patent reduces application complexity while preserving the ability to handle diverse instrument data types
2Adaptability or versatility
If applications are reprogrammed to support new data types, then new analysis capabilities can be added, but development time and system disruption increase
Solution Approach 1:
The data service layer implements universal RESTful endpoints that can handle multiple data types through a common interface. New data types can be added by creating new decoder components that conform to the existing interface standards, allowing the system to support diverse chromatography data formats without requiring changes to application code or existing service logic
Solution Approach 2:
The system enables self-service integration of new instruments and data types through standardized interfaces. New decoders can be developed independently and registered with the data service, which automatically makes them available through the existing RESTful endpoints without requiring system-wide reconfiguration or application reprogramming
3Ease of operation
If data is stored in a centralized repository with multiple data types, then data retrieval is simplified, but the complexity of data storage and management increases
Solution Approach 1:
The patent applies parameter changes by transforming diverse instrument data into a standardized format through decoders that convert various data types into consistent JSON structures. This standardization allows heterogeneous data to be stored uniformly in the centralized repository, simplifying retrieval operations while managing storage complexity through consistent data representation
Data Source
AI summary
Exemplary embodiments provide methods, mediums, and systems for providing services and systems for managing data in a chromatography data processing environment. The system may implement one or more autonomous services that read encoded data and decode the data for use by one or more applications. To communicate with the autonomous services, the autonomous services may expose endpoints accessible to requesting applications. For instance, the endpoints may apply decoders to read a stream of data generated by a chromatography instrument. Each of the decoders may be configured to read different configurations of data (e.g., to parse the stream based on the way that the instrument encodes the stream). Accordingly, the acquired data from many different instruments can be stored in a centralized repository, and may be accessed by any authorized application through the endpoints. The data processing environment therefore serves as a single access point for many different types of data.