Middleware Server Data Aggregation for Mobile Offline Access
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Solution Overview
Problem
Existing middleware frameworks for providing enterprise data to mobile devices are inefficient, as they require new frameworks for each enterprise application, fail to capture data context, depend on network connectivity, and do not support caching, leading to data integrity issues and delayed information availability.
Innovation Solution
A system and method that uses a middleware server to connect enterprise systems with mobile devices, transforming data into mobile-compatible formats, applying business and generic rules, and caching data locally on mobile devices, enabling offline access and reducing network dependency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing middleware frameworks are used to extract and convert business data, then data can be provided to mobile devices, but new frameworks are required for each enterprise application and development effort increases
Solution Approach 1:
The patent implements a universal middleware framework that can serve multiple enterprise applications simultaneously. The framework includes reusable components such as a connection manager, data extractor, transformer, and validator that can be configured for different applications without requiring separate framework development. This multi-functional approach allows a single framework to handle diverse enterprise applications, reducing development effort and complexity.
Solution Approach 2:
The middleware framework is segmented into independent, modular components including connection management, data extraction, transformation, validation, and caching layers. Each component can be independently configured, developed, and maintained. This segmentation allows the framework to be adaptable to different enterprise applications while maintaining a consistent core structure, reducing overall complexity.
2Ease of operation
If existing middleware frameworks transform data to mobile-compatible format, then data can be displayed on mobile devices, but data context is not captured and data cannot be presented in readily consumable format
Solution Approach 1:
The middleware framework performs preliminary actions by capturing and preserving data context during the extraction phase. It identifies and retains contextual information such as data relationships, metadata, and business rules before transformation. This ensures that when data is transformed to mobile-compatible format, the contextual information is already embedded and can be readily consumed by mobile applications without loss of meaning or relationships.
3Productivity
If existing middleware frameworks align data in suitable format, then data can be displayed on mobile devices, but a lot of development effort is required and time to launch increases
Solution Approach 1:
The framework uses parameter-based configuration to adapt data alignment to different enterprise applications. Instead of requiring custom development for each application, the framework allows configuration through parameters such as data source connections, transformation rules, and output formats. This parameter-driven approach enables rapid adaptation to different applications, significantly reducing development effort and time to launch.
4Reliability
If existing middleware frameworks extract business data, then data can be provided to mobile devices, but business rules for validating data are not captured and network connectivity is required for validation
Solution Approach 1:
The middleware framework implements self-service validation by capturing business rules during the data extraction phase and embedding them within the framework itself. The validation functionality is integrated into the framework, allowing it to automatically validate extracted data against captured business rules without requiring external network connectivity or separate validation systems. This reduces network dependency and simplifies the overall system architecture.
5Productivity
If existing middleware frameworks provide data over network, then data can be accessed on mobile devices, but filtering is not performed and enterprise applications become slow on low bandwidth network
Solution Approach 1:
The framework extracts and filters only the necessary data before transmission to mobile devices. It identifies relevant data based on configured criteria and business rules, extracting only what is needed for the specific enterprise application and user context. This selective extraction reduces the volume of data transmitted over the network, improving access speed and reducing bandwidth consumption, especially on low bandwidth networks.
6Reliability
If existing middleware frameworks depend on network connectivity, then data can be validated centrally, but caching is not supported and data is not available when connection is not established
Solution Approach 1:
The framework performs preliminary actions by caching extracted and validated data locally within the middleware framework before network connectivity is required. It maintains a local cache of business data and validation rules, allowing the system to continue operating and providing data even when network connection is not established. This preliminary caching action ensures data availability and reduces network dependency.
7Reliability
If existing middleware frameworks do not support caching, then data can be kept synchronized with enterprise systems, but data is not available offline and data integrity issues occur
Solution Approach 1:
The framework performs preliminary caching of extracted data and validation rules before offline operation is needed. It maintains a local cache that stores business data and associated integrity constraints, allowing the system to operate offline while preserving data integrity through pre-cached validation rules. This preliminary action ensures both data availability offline and data integrity maintenance.
Solution Approach 2:
The framework implements feedback mechanisms to maintain data integrity in cached data. It validates cached data against stored business rules and provides feedback on data quality, allowing the system to detect and correct integrity issues even when operating offline. This feedback loop ensures data integrity is maintained despite the absence of real-time network connectivity.
Data Source
AI summary
A system and computer-implemented method for aggregating and providing data from one or more enterprise systems to one or more mobile devices is provided. The system comprises one or more enterprise systems configured to store data pertaining to one or more enterprises. The system further comprises a middleware server, in communication with one or more mobile devices, configured to connect with the one or more enterprise systems based on a predefined information architecture. Further, the middleware server is configured to extract the data from the one or more connected enterprise systems. Furthermore, the middleware server is configured to transform the extracted data into one or more mobile compatible formats. In addition, the middleware server is configured to send the transformed data to the one or more mobile devices. The system further comprises one or more mobile devices configured to locally store and render the transformed data.