Big-Data View Integration for Cross-Platform Office Data Sync
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Solution Overview
Problem
Conventional office management software lacks cross-platform integration, leading to inefficiencies, redundancies, and desynchronized data, which hinders effective management and performance evaluation across disparate platforms.
Innovation Solution
A big-data view integration platform that generates integration guided user interfaces (GUIs) to unify data from various platforms, including legacy and non-legacy services, using push-, pull-, and event-based data ingestion, and transforms data into an enhanced integration format for real-time processing and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple disparate management software products are used to manage office operations, then specific business functions can be performed, but data becomes desynchronized and manual work increases
Solution Approach 1:
The patent merges multiple disparate management software products into a single integrated platform that consolidates office management, performance evaluation, and business planning functions. This integration eliminates data desynchronization by creating a unified data architecture where all modules access and share common data sources, thereby maintaining versatility while dramatically improving operational efficiency through automated data flow and eliminated manual processes.
Solution Approach 2:
The integrated platform implements multi-functionality by designing a universal system architecture that can perform diverse office management functions (staff management, performance evaluation, business planning, compensation management) within a single platform. The system uses a common data layer and standardized interfaces to enable different modules to work together seamlessly, providing adaptability across multiple business functions without requiring separate software products.
2Ease of operation
If conventional management software is used, then basic office management tasks can be performed, but comprehensive performance evaluation and cross-platform data correlation are not achieved
Solution Approach 1:
The patent introduces an intermediary integration layer that sits between basic office management functions and performance evaluation systems. This intermediary layer collects, standardizes, and correlates data from multiple sources including office management software, performance metrics, and business planning tools. It transforms disparate data into unified performance insights, enabling comprehensive evaluation while maintaining ease of operation for basic tasks through the underlying integrated infrastructure.
3Adaptability or versatility
If manual processes and paper work are used to manage business operations, then flexibility in handling diverse tasks is maintained, but time consumption and errors increase
Solution Approach 1:
The integrated platform implements self-service automation where the system automatically performs data collection, validation, and processing across different business functions. The platform uses standardized data interfaces and automated workflows to handle diverse tasks without requiring manual intervention, thereby maintaining adaptability through configurable processes while dramatically reducing time consumption and errors through systematic automation.
4Reliability
If disparate platforms with different data formats are used, then specific platform functionalities are optimized, but data integration and real-time synchronization become untenable
Solution Approach 1:
The patent applies parameter changes by implementing a standardized data transformation layer that converts various data formats and protocols from disparate platforms into a unified internal data model. The system maintains reliability of individual platform functions by preserving their original operation while transforming their data output parameters to match the integration standard, thereby enabling real-time synchronization without increasing integration complexity for end users.
Data Source
AI summary
A big-data view integration platform generates integration guided user interfaces (GUIs). A first edge node ingests push-based and pull-based data from a plurality of platform services, which include legacy and non-legacy services having incompatible communication protocols. An event-based queue receives from the first edge node a plurality of queue events as indirect push-based data. A second set of queue events includes direct push-based data as received directly from a non-legacy platform service. A conformity component integrates the push-based data, the pull-based data, and the plurality of queue events into integration data having an enhanced integration format. A view integration component generates a plurality of data views from the integration data. A second edge node exposes the plurality of data views via an access services application programming interface (API). A new service execution component accesses the access services API to generate integration GUIs based on the data views.