Modular Data Analysis Platform for Flexible Pipeline Construction
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Solution Overview
Problem
Organizations face challenges in developing custom data processing modules and integrating compatible solutions due to the need for significant software development resources, especially under tight timelines or budgets, as current big data processing solutions are often domain-specific and lack flexibility.
Innovation Solution
A modular electronic data analysis platform program that includes a server device with a processor and an electronic data analysis platform program, which stores modular data processing tools, maps user data sources to predetermined types, selects appropriate tools, generates data analysis pipelines, and processes data to achieve specific analytic goals, allowing for flexible and adaptable data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If organizations develop in-house custom data processing modules and pipelines, then data processing capability and adaptability are improved, but software development resources and time are significantly increased
Solution Approach 1:
The patent implements a universal data processing platform with pre-built modular tools that can handle multiple data types and processing scenarios. The system provides a unified interface for ingesting various data sources (structured, unstructured, semi-structured data from databases, files, APIs, streaming sources) and applies standardized processing operations (filtering, aggregation, transformation, enrichment) across different data types, eliminating the need to develop separate custom solutions for each scenario.
Solution Approach 2:
The data processing capability is divided into independent, reusable modular tools that can be selectively combined. Each module performs a specific function (data ingestion, filtering, aggregation, transformation, enrichment) and can be independently configured and deployed. This segmentation allows organizations to assemble processing pipelines from pre-built components rather than developing entire pipelines from scratch, reducing development resources while maintaining adaptability.
2Manufacturing precision
If domain-specific data processing solutions are used, then specialized processing needs are met, but flexibility and integration with other solutions are reduced
Solution Approach 1:
The platform provides domain-specific processing capabilities through a universal interface. Pre-built modular tools handle specialized processing needs (structured data querying, unstructured data text processing, semi-structured data parsing) while maintaining consistent data type definitions and processing operations. This allows the system to deliver domain-specific accuracy through standardized mechanisms that can be integrated with other solutions.
Solution Approach 2:
The system introduces standardized data type definitions and processing operation interfaces as intermediaries between domain-specific processing needs and integration requirements. By mapping various data sources and processing operations to a common framework of predefined data types (structured, unstructured, semi-structured) and standard operations, the system enables both specialized processing and seamless integration with external solutions.
3Productivity
If custom data processing pipelines are developed, then specific analytic goals are achieved, but development time and budget are increased
Solution Approach 1:
The system performs preliminary action by providing pre-built modular data processing tools and pipelines that have already been developed, tested, and optimized. Organizations can immediately deploy these pre-configured solutions for common data processing scenarios (data ingestion from various sources, filtering, aggregation, transformation, enrichment) without undergoing lengthy development cycles. Customization is achieved through configuration rather than development, dramatically reducing deployment time while maintaining processing efficiency.
Solution Approach 2:
The platform enables copying and reusing of proven data processing pipelines and modular tools across different projects and organizations. Pre-built processing logic for common scenarios (handling structured data from databases, processing unstructured text data, parsing semi-structured JSON/XML) can be replicated and adapted to new requirements through configuration parameters rather than rewriting code, significantly accelerating deployment while ensuring consistent processing efficiency.
Data Source
AI summary
A server device configured to execute an electronic data analysis platform program to store a plurality of modular data processing tools, each modular data processing tool configured to perform data processing with predetermined data types and to combine with other modular data processing tools in a data analysis pipeline, receive a user input of one or more user data sources, map the data of the one or more user data sources to one or more of the predetermined data types, determine a data analytic goal for the mapped one or more user data sources, select one or more modular data processing tools configured to process the one or more predetermined data types mapped to data of the mapped one or more user data sources to generate the data analytic goal, and generate a data analysis pipeline configured to generate the data analytic goal.