Extensible Star Schema Data Warehouse for Flow Cytometry
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Solution Overview
Problem
Traditional flow cytometry data management systems are inadequate for handling large amounts of Polychromatic Flow Cytometry data, as they suffer from degraded data management and query performance, and are unable to efficiently unify proteomic and genomic data with flow cytometry data, nor can they dynamically extend database schemas to accommodate growing data sets beyond a terabyte.
Innovation Solution
A system and method for managing flow cytometry data using an extensible star schema data warehouse that allows users to query and mine data via a graphical user interface, enabling dynamic extension of the database schema and integrating proteomic and genomic data with flow cytometry data, using techniques such as pivot operations to optimize query performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional relational or transactional databases are used to store flow cytometry data, then data update performance is enhanced, but query performance deteriorates when large amounts of data are summarized
Solution Approach 1:
The patent segments the database into two distinct parts: an OLTP database for handling data updates and an OLAP data warehouse for handling queries. This segmentation allows each database type to be optimized for its specific function, resolving the contradiction between update performance and query performance.
Solution Approach 2:
The patent introduces an intermediary data warehouse layer that sits between the OLTP database and the user interface. This intermediary OLAP database receives data from the OLTP database and provides optimized query performance, acting as a mediator that resolves the performance trade-off.
2Quantity of substance
If conventional flow cytometry data systems are used, then current data storage is maintained, but the system cannot unify proteomic and genomic data with flow cytometry data
Solution Approach 1:
The patent implements a universal data warehouse schema that can accommodate multiple types of data including flow cytometry data, proteomic data, and genomic data. This multi-functional database structure allows diverse data types to be unified and queried together, resolving the limitation of data integration.
Solution Approach 2:
The patent employs a dynamic and extensible database schema that can adapt to accommodate new data types and structures as needed. This dynamic architecture allows the system to evolve and integrate new data sources like proteomic and genomic data without being constrained by a fixed schema.
3Stability of the object's composition
If traditional database schemas are used, then current data structures are maintained, but the schema cannot be dynamically extended to accommodate growing data sets
Solution Approach 1:
The patent implements a dynamic database schema that can be extended and modified to accommodate growing and evolving data requirements. This dynamic schema maintains stability for existing data structures while providing flexibility to add new dimensions, metrics, and data types as needed.
Solution Approach 2:
The patent uses a nested dimensional modeling approach where dimension tables can contain nested hierarchies and extensible attributes. This nested structure allows the schema to maintain a stable core while providing extensible layers that can accommodate additional data dimensions without disrupting the existing structure.
Data Source
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AI summary
Methods, systems, and computer program products for storing, managing, querying, and displaying data are described herein. The method operates by classifying and displaying received flow cytometry data. In an embodiment, the method creates a first extensible dimension table with columns that include keywords from received data. The method creates a second extensible dimension table with columns corresponding to parameters from the received data. The method then populates dimension tables with data describing values of dimensions of the classified data. The method populates a first fact table from the classified data. The method then populates a second fact table with a summary of data from the first fact table. The method displays a user interface on a display of a computer device. The method then permits a user to select query attributes including keywords and parameters. The method builds and executes queries based upon dimensions generated from grouped selected query attributes.