Offline Data Packet Queue With In-Queue Update Replacement
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Solution Overview
Problem
Existing systems face challenges in efficiently aggregating and analyzing data from disparate sources with different protocols and conventions, particularly in industries like construction management, where real-time tracking of equipment, supplies, and personnel is necessary, and data must be securely ingested, classified, and shared across multiple audiences.
Innovation Solution
A data aggregation platform utilizing machine learning algorithms and neural networks to securely ingest, classify, and partition data from multiple sources, enabling real-time analysis and management of construction projects, with features like facial recognition and predictive modeling to optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is aggregated from multiple disparate sources with different protocols and conventions, then data comprehensiveness and analysis capability are improved, but system complexity and difficulty of integration increase
Solution Approach 1:
The patent introduces a data aggregation platform as an intermediary layer between multiple disparate data sources and the analysis system. This platform includes data ingestion components that receive data from various sources using different protocols, normalize and standardize the data formats, and present unified data structures to downstream systems. The intermediary handles protocol translation, data validation, and format conversion, thereby maintaining data comprehensiveness while shielding the core system from integration complexity.
Solution Approach 2:
The system is divided into distinct modular components: data ingestion modules for each data source, data normalization layers, data storage components, and analysis engines. Each module handles specific data sources or transformation tasks independently. This segmentation allows individual components to be developed, maintained, and scaled separately, reducing overall system complexity while enabling comprehensive data aggregation across multiple sources.
2Productivity
If real-time data tracking and analysis is implemented across multiple data sources, then operational efficiency and decision-making speed are improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data normalization, validation, and enrichment during the data ingestion phase, before data reaches the analysis stage. Data is pre-processed and stored in standardized formats with necessary transformations already applied. This preliminary action reduces the computational burden during real-time analysis, enabling faster query response times while maintaining comprehensive data processing capabilities.
Solution Approach 2:
The patent implements localized data processing where data is normalized and validated at the source-specific ingestion points rather than centrally. Each data source has dedicated normalization rules and processing logic optimized for its specific format and characteristics. This local quality approach enables parallel processing of multiple data streams simultaneously, improving overall throughput and reducing total processing time while maintaining high operational efficiency.
3Reliability
If data is securely ingested and partitioned from multiple sources, then data security and access control are improved, but system complexity and implementation difficulty increase
Solution Approach 1:
The system implements a universal authentication and authorization framework that handles multiple data sources and access scenarios through a single security model. The data aggregation platform includes centralized identity management, role-based access control, and authentication mechanisms that work consistently across all data sources and user types. This multi-functional security layer provides comprehensive data protection while presenting a unified, manageable interface to administrators, reducing implementation complexity despite the multi-source environment.
4Loss of information
If machine learning algorithms and neural networks are used for data classification and analysis, then data insight and resource optimization are improved, but computational complexity and processing requirements increase
Solution Approach 1:
The system applies machine learning algorithms selectively to specific data classification tasks rather than processing all data through complex models. The data aggregation platform uses lighter-weight classification methods for routine data categorization and reserves sophisticated neural network analysis for critical insight generation. This partial application approach achieves sufficient data insight quality for operational needs while avoiding the excessive computational complexity of applying advanced algorithms universally.
Data Source
AI summary
Systems, methods, and devices for data ingestion, database management, and data security. A method includes determining that a receiver device is offline and cannot communicate with a server. The method includes providing a plurality of data packets to a data packet queue and receiving an update to an existing data packet stored within the data packet queue. The method further includes minimizing a total memory requirement for the data packet queue by replacing the existing data packet with the update and in response to determining the receiver device is now online, communicating the data packet queue to the receiver device over a network.