Robust Data Integration Framework for Disparate Systems
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Solution Overview
Problem
Existing data integration technologies face challenges in securely and efficiently accessing and aggregating data from disparate systems of records, leading to limitations in data processing and analysis capabilities.
Innovation Solution
A robust data integration framework that enables connections to multiple systems of records through various data intake connection types, including manual upload, pull digital integration, and push integration, while utilizing distinct data access protocols and mapping data schemas to enterprise object models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple data intake connection types and distinct data access protocols are used to access data from disparate systems, then data accessibility and integration capability are improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary data integration platform that sits between disparate data sources and the target system. This platform provides standardized connection interfaces and protocols, mediating the complexity of multiple data sources while presenting a unified access method. The intermediary handles protocol translation, authentication, and data normalization, thereby improving data accessibility without proportionally increasing the complexity of the target system.
Solution Approach 2:
The patent implements a universal data access framework that can handle multiple data intake connection types (manual upload, pull digital integration, push integration) through a single standardized interface. This multi-functional system provides common authentication mechanisms and data access patterns that work across different connection types and protocols, reducing the need for separate specialized components for each data source type.
2Productivity
If comprehensive data aggregation from multiple systems is implemented, then data processing capability is improved, but security risks increase
Solution Approach 1:
The patent implements preliminary authentication and authorization actions before data access is granted. The system performs identity verification, permission validation, and security policy enforcement at the point of connection establishment. By conducting these security checks in advance, the system enables comprehensive data aggregation from multiple sources while preventing unauthorized access and maintaining security controls throughout the data integration process.
3Measurement precision
If data schema mapping to enterprise object models is performed, then data accuracy and meaningfulness are improved, but processing time increases
Solution Approach 1:
The patent performs preliminary schema mapping and data normalization operations before the actual data processing workflow. The system pre-configures mappings between source data schemas and enterprise object models, establishing transformation rules in advance. This preliminary action reduces the processing time during runtime by eliminating the need for complex real-time schema interpretation, while still maintaining high data accuracy through the pre-established mapping relationships.
Data Source
AI summary
Embodiments provide for robust data integration. Some embodiments configure at least one computing device to establish at least one connection by any one of a plurality of data intake connection types. Each data intake connection type may be configured to facilitate the connection between the at least one computing device with at least one external computing device to access source data stored via the at least one external computing device via a distinct data access protocol. Some embodiments generate a job data object defining at least one data intake connection type. Some embodiments, access particular source data associated with the job data object on at least one particular external computing device, generate a filtered job data object by applying one or more filter data objects to the job data object, and retrieve relevant data from the source data by executing the filtered job data object.