Remote Data Collection Agents for Database Interoperability
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Solution Overview
Problem
Franchisors and other businesses face challenges in collecting, consolidating, and reporting key operational and financial indicators from diverse remote locations due to different data storage formats and systems, leading to limited data availability and inability to compare performance with peer groups or industry norms, which is exacerbated by the need for extensive IT support and customization.
Innovation Solution
A remote data collection system that automates data retrieval from various database types across multiple locations, using a central server communicating with remote data collection agents, which can adapt to different LOB applications and database vendors without requiring custom programming, and employs metadata messages for configuration and management, enabling centralized data consolidation and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If automated data collection is implemented, then data availability and consolidation capability are improved, but system complexity and initial setup requirements worsen
Solution Approach 1:
The patent introduces a data collection agent as an intermediary component that automatically extracts data from various database types at remote locations and transmits it to a central server. This agent handles the complexity of different database formats and protocols, enabling automated data collection without requiring the central system to directly interface with diverse data sources, thus improving data availability while managing system complexity through modular design
2Adaptability or versatility
If customization for different database types is provided, then adaptability to various LOB applications is improved, but device complexity and support requirements worsen
Solution Approach 1:
The patent implements a universal data collection agent that can interface with multiple database types (SQL databases, Oracle databases, flat files, spreadsheets) through a standardized abstraction layer. The agent uses generic data extraction techniques and metadata-driven configuration to handle different database formats, enabling a single multi-functional system to adapt to various LOB applications without requiring custom programming for each database type, thus improving versatility while reducing support requirements
3Device complexity
If manual data collection methods are used, then system complexity is reduced, but productivity and data consolidation capability worsen
Solution Approach 1:
The patent implements automated data collection where the data collection agent operates autonomously to extract and transmit data without manual intervention. The system includes self-configuration capabilities where the agent can automatically discover database structures, authenticate connections, and schedule data extraction tasks. This self-service approach eliminates manual data collection processes, significantly improving productivity and data consolidation capability while keeping system complexity manageable through automation
4Reliability
If extensive IT support is provided for data collection, then reliability and data accuracy are improved, but loss of time and operational efficiency worsen
Solution Approach 1:
The patent implements a feedback mechanism where the central server receives metadata about the data collection process status, success rates, and error conditions from the data collection agents. This feedback enables automated monitoring and alerting systems that can detect and report issues without requiring constant IT support intervention. The system maintains data accuracy through validation checks and error handling protocols while reducing time loss by enabling self-diagnosis and automated remediation of common issues
Data Source
AI summary
Remote data collection systems and methods retrieve data including financial, sales, marketing, operational and the like data from a plurality of databases and database types remotely over a network in an automated, platform-agnostic manner. A remote data collection system includes a network interface, a connection to a data source, a processor communicatively coupled to the network interface and the connection, and memory storing instructions for remote data collection that, when executed, cause the processor to: receive a request to extract data from the data source; extract the data in a non-intrusive manner from the data source using a two phase process comprising a reconciliation phase and a collection phase; and transmit one of an entire set and a subset of the extracted data based on the request.