Template Recommendation Platform for Cross-System Data Exploration
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Solution Overview
Problem
Organizations face inefficiencies and reduced productivity due to disparate systems operating in silos, lacking data analysis and exploration capabilities, making it difficult to visualize and analyze data across multiple systems, and correlating data from disconnected systems.
Innovation Solution
A platform that recommends and executes visual and execution templates to automate control and data exploration across multiple systems by establishing connections, identifying target systems, transmitting operations, and discovering best-fit templates for data visualization and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually switch applications to view information across multiple systems, then they can access data from different systems, but productivity decreases and inefficiency increases
Solution Approach 1:
The patent combines multiple disparate systems into a unified interface that allows users to access and analyze data from all connected systems in a single location. The system integrates data from various sources including databases, files, and external systems, presenting them through a common exploration environment that eliminates the need to switch applications.
Solution Approach 2:
The platform provides universal data exploration and visualization capabilities that work across multiple system types and data formats. The system can connect to diverse data sources, perform consistent exploration operations, and generate visualizations regardless of the underlying system architecture, making it adaptable to various organizational systems.
2Adaptability or versatility
If legacy systems without data analysis capabilities are used, then existing systems can be maintained, but data visualization and analysis become difficult
Solution Approach 1:
The patent introduces an intermediary platform that sits between legacy systems and users, providing data analysis and visualization capabilities without requiring modifications to the legacy systems themselves. The platform connects to existing systems through various interfaces, retrieves data, and performs exploration operations, acting as a mediator that adds analytical capabilities to systems that lack them natively.
Solution Approach 2:
The system performs preliminary data retrieval and preparation by connecting to legacy systems and extracting data before the user needs to analyze it. The platform pre-processes data from multiple sources, making it ready for exploration and visualization operations, thereby eliminating the need for users to manually extract and prepare data from legacy systems.
3Loss of time
If data is extracted manually from databases associated with legacy systems, then data can be obtained, but the process becomes tedious and time-consuming
Solution Approach 1:
The patent implements self-service data extraction where the platform automatically connects to databases, retrieves required data, and makes it available for analysis without user intervention. The system autonomously performs data extraction operations based on user exploration requests, eliminating the manual effort previously required to extract data from databases.
Solution Approach 2:
The system performs preliminary data extraction by establishing connections to databases and pre-fetching data that may be needed for exploration operations. This automated preliminary action reduces the time users would otherwise spend manually extracting data, as the system proactively retrieves and prepares data in advance.
4Loss of information
If data from disconnected systems cannot be correlated, then system independence is maintained, but insights and analysis across systems become difficult
Solution Approach 1:
The patent segments the data correlation problem by treating each system as an independent data source while providing a unified exploration layer above them. The platform maintains separate connections to each system but provides integrated exploration capabilities that can correlate data across sources through a common query interface, allowing analysis without requiring complex system integration.
Solution Approach 2:
The platform acts as an intermediary that receives exploration requests and automatically correlates data from multiple disconnected systems. It translates user queries into appropriate operations on each connected system, retrieves relevant data, and synthesizes results, thereby enabling cross-system analysis without requiring the systems themselves to be interconnected or aware of each other.
Data Source
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AI summary
The present disclosure relates to platform configured to recommend Visual and Execution templates to automate exploration across one or more disparate systems. The platform is configured for receiving data from a target system. Further, the platform is configured to identifying a set of best fit templates, from a set of templates. In one embodiment, the set of templates may comprise one or more templates created by the user, one or more templates published by other users associated with the user, and one or more system generated templates. The platform is configured to analyze the set of templates based on the type of data received from the target system and a set of predefined rules to identify a subset of best fit templates (discovered Templates) from the set of templates. Furthermore, the platform is configured to execute the subset of best fit templates for performing one or more data processing operations.