Semantic Data Framework for Cross-Domain Browser Visualization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current solutions for accessing and synthesizing data across multiple domains in a browser-based web environment are restricted by the same-origin policy, limiting dynamic user configurations and preventing effective data visualization, especially with data sources computed at query time.
Innovation Solution
A schema-independent method for modeling data from diverse sources, where a server transforms data into a semantic format and transmits it to a client for visualization, enabling cross-domain data synthesis and visualization across multiple domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a pre-built aggregated data warehouse is used to access data across domains, then data access is enabled, but all information domains must be known at design time and dynamic user-based configurations at run-time are prevented
Solution Approach 1:
The system transitions from static pre-built data warehouses to dynamic run-time data access. Users can dynamically configure and access data from multiple domains at run-time without requiring all domains to be known at design time, enabling flexible adaptation to new data sources and configurations
Solution Approach 2:
A server acts as an intermediary between the client application and multiple domain data sources. The server receives data from selected domains, transforms it into a semantic data format, and transmits it to the client, enabling cross-domain data access without requiring the client to directly access multiple domains
2Adaptability or versatility
If same-origin policy headers are used to enable cross-domain communication, then cross-domain data access is enabled, but coordination between data sources and consumers is required and static configuration prevents easy dynamic inclusion of new domains
Solution Approach 1:
The server serves as a mediator that eliminates the need for direct coordination between multiple domains and the client application. The server handles all cross-domain data access, transformation, and synthesis, allowing domains to be dynamically included without coordination overhead
Solution Approach 2:
The system enables self-service dynamic domain inclusion where users can add new data domains at run-time without requiring coordination with domain owners or complex configuration changes. The server automatically handles the integration of new domains through its data transformation and synthesis capabilities
3Reliability
If JSONP calls are used for cross-domain communication, then cross-domain data loading is enabled, but this exploits security vulnerabilities and is not a safe or long-term option
Solution Approach 1:
The server acts as a secure intermediary that provides cross-domain data access through legitimate server-to-server communication protocols. This eliminates the need to exploit browser security vulnerabilities while maintaining full cross-domain data access capability through proper authentication and authorization mechanisms
4Adaptability or versatility
If iframe elements are used to access cross-domain information, then cross-domain information is accessible to users, but it cannot be accessed to the underlying shared program memory for synthesis with other data
Solution Approach 1:
The server acts as an intermediary that retrieves cross-domain data and transmits it to the client application in a format suitable for program memory access and synthesis. This enables both user-facing cross-domain information display and backend data synthesis operations without the restrictions of iframe isolation
Solution Approach 2:
The server provides universal data access functionality that serves multiple purposes: it enables user-facing cross-domain information display, provides data for program memory synthesis, and supports various data formats and protocols. This single component replaces multiple specialized solutions
Data Source
AI summary
A computer-implemented schema-independent method of modeling data from diverse sources is described. A server transmits to a client computer a blueprint for visualizing and interacting with data, wherein the blueprint defines an application, for visualizing and interacting with data. The application may operate on the client computer within a web browser and may include program code or scripts that operate within the web browser and transmit data and commands to and from the server. In response to receiving a data fetch message from the application, the server receives data from a selected one of a plurality of domains. The server then transforms the received data into a semantic data format. The transformed data is then stored by the server as a first data set. The first data set can then be transmitted to the client computer for further processing and visualization by the application using the blueprint.