Stateless Agents for Distributed Data Integration
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Solution Overview
Problem
Conventional data integration techniques face challenges in handling diverse, unstructured data sources, leading to complexity, lack of transparency, and security risks during data transmission and processing, especially when integrating large datasets from geographically dispersed locations.
Innovation Solution
A system and method that enables users to build set-based transformation rules with transparent outcomes, using stateless agents for distributed data processing, encryption, and compression, allowing for efficient and secure data integration across multiple sources with reduced latency and bandwidth requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional data integration techniques are used to handle diverse unstructured data sources, then data integration capability is achieved, but system complexity increases and transparency is lost
Solution Approach 1:
The system segments data integration into standardized modular components (extractors, transformers, loaders) that can independently process different data sources. Each component handles specific tasks through standardized interfaces, reducing overall system complexity while maintaining versatility in handling diverse unstructured data sources.
Solution Approach 2:
The patent introduces intermediary components including a metadata repository and standardized transformation rules that mediate between diverse data sources and target systems. These intermediaries provide abstraction layers that simplify complexity while enabling adaptable integration of various data types and sources.
2Adaptability or versatility
If data is transmitted across long distances for integration, then data accessibility is improved, but security risks increase
Solution Approach 1:
The system performs preliminary actions by encrypting data at the source before transmission and maintaining encrypted state throughout the integration process. Security measures are established in advance rather than applied during transmission, reducing security risks while preserving data accessibility across distributed locations.
Solution Approach 2:
The patent extracts and separates sensitive data handling from the main data transmission flow by using dedicated security components including encryption modules and secure authentication mechanisms. This extraction of security functions allows data to be accessed across long distances while minimizing exposure to security risks through isolated security processing.
3Loss of energy
If large amounts of data are compressed for transmission, then bandwidth requirements are reduced, but processing time increases
Solution Approach 1:
The system applies partial compression by selectively compressing only the portions of data that benefit from it, rather than compressing all data uniformly. This approach reduces bandwidth requirements for large datasets while minimizing processing time overhead by avoiding unnecessary compression of already-efficient data formats.
Solution Approach 2:
The patent dynamically adjusts compression parameters based on data characteristics, transmission requirements, and system state. By changing compression levels and algorithms according to specific conditions, the system optimizes the trade-off between bandwidth reduction and processing time for different data scenarios.
4Manufacturing precision
If transformation rules are applied to integrate data from multiple sources, then data integration quality is improved, but debugging difficulty increases
Solution Approach 1:
The system implements feedback mechanisms including logging, monitoring, and validation at each transformation stage. Transformation rules are executed with detailed tracking of input data, transformation operations, and output results, enabling quality assurance while simplifying debugging through comprehensive feedback on transformation behavior and outcomes.
Solution Approach 2:
The patent applies preliminary validation and testing of transformation rules before full execution. Data samples are processed through transformation rules with pre-execution validation to detect issues early, improving integration quality while reducing debugging difficulty by identifying problems before they propagate through the full data integration process.
Data Source
AI summary
Methods and systems for large scale data integration in distributed or massively parallel environments comprises a development phase wherein the results of a proposed jobflow can be viewed by the user during development, including the results of upstream units where the data sources and data targets can be any of a variety of different platforms, and further comprises the use of remote agents proximate to those data sources and data targets with direct communication between the associated agents under the direction of a topologically central controller to provide, among other things, improved security, reduced latency, reduced bandwidth requirements, and faster throughput.