Structured Query File System for Transaction Data Analytics
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Solution Overview
Problem
Existing systems for querying transaction data are inflexible and require manual processing, limiting the ability of merchants and financial institutions to generate analytical insights efficiently.
Innovation Solution
A computer-implemented method and system that generates a request file with a request definition object and a segment object to query transactional databases, producing a structured response file based on user input, allowing for efficient processing and flexible data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing systems use inflexible querying tools, then specific transaction data can be obtained, but manual processing is still required and analytical insights cannot be generated efficiently
Solution Approach 1:
The system enables self-service by allowing users to directly query transactional databases using structured query files without requiring manual data processing. The automated query processing and result generation eliminates the need for manual intervention, making the system serve itself and the user simultaneously.
Solution Approach 2:
The patent replaces manual mechanical processing with automated electronic query processing. The structured query files and automated database querying system substitute the manual mechanical operations, transforming the workflow from manual data handling to automated electronic processing.
2Adaptability or versatility
If existing systems use inflexible querying tools, then specific types of transaction data can be retrieved, but the system lacks flexibility for different data retrieval needs
Solution Approach 1:
The system achieves universality by designing a standardized query file structure that can handle multiple types of data retrieval requests. The same query processing mechanism works for various transactional databases and different analytical needs, making the system multi-functional and adaptable to diverse requirements.
Solution Approach 2:
The system enables adaptability through parameter changes in the query files. By modifying parameters such as date ranges, transaction types, and filtering criteria within the structured query format, the system can flexibly retrieve different types of transaction data without changing the underlying system structure.
3Loss of time
If manual processing is used to generate analytical insights, then data can be analyzed, but computing resources are wasted and processing time increases
Solution Approach 1:
The system performs preliminary action by pre-structuring the query files with all necessary parameters and filters before executing the database query. This preparation ensures that only relevant data is retrieved and processed, eliminating the need for subsequent manual filtering and analysis, thus saving both time and computing resources.
Solution Approach 2:
The automated query processing system enables continuous useful action by seamlessly transitioning from query submission to data retrieval to result generation without manual intervention gaps. This continuous automated workflow eliminates idle time and reduces the overall processing time while optimizing computing resource utilization.
Data Source
AI summary
Provided is a system, method, and apparatus for generating analytics with structured query files. The method includes the steps of generating at least one graphical user interface configured to receive query parameters from a user for querying transaction data, generating a request file based on the query parameters, the request file including a request definition object and a segment object, processing the request file to query at least one transactional database based at least partially on the request file, and generating a response file including transaction data based on a return from the query of the at least one database, the response file structured based on the request definition object from the request file.