Automated Fact Discovery Engine for Enterprise Data Analysis
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Solution Overview
Problem
Traditional approaches to analyzing enterprise information are limited by the need to determine metrics in advance, making it difficult to address new questions and requiring manual and time-consuming processes, especially with large amounts of data and complex queries.
Innovation Solution
A cloud-based system that uses a discovery engine to automatically create a generic structured set of facts from locally accessible data and stores them in a central repository using a standard format, allowing for efficient and accurate analysis of enterprise information through user-generated queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual mapping and query creation are used in traditional architectures, then data can be collected and analyzed, but the process becomes time-consuming and error-prone, especially with large amounts of data and complex queries
Solution Approach 1:
The system enables self-service through automated discovery where the discovery engine automatically executes discovery code against local data sources, creates structured sets of facts, and generates queries without requiring manual mapping by users. This automation eliminates the time-consuming manual processes while maintaining accurate data analysis capabilities.
Solution Approach 2:
The patent replaces manual mechanical processes (manual mapping, manual query creation) with automated computational processes. The discovery engine automatically executes discovery code, creates structured facts, and generates queries, substituting human manual operations with automated systems that perform the same functions more efficiently.
2Adaptability or versatility
If traditional architectures with manual mapping are used, then data from multiple sources can be integrated, but adding new data providers requires creating new mappings which increases complexity and time requirements
Solution Approach 1:
The system automatically integrates new data sources through self-service discovery. When new data providers are added, the discovery engine automatically executes discovery code against the new sources, creates structured sets of facts, and generates appropriate queries without requiring manual mapping configuration. This maintains adaptability while reducing complexity.
Solution Approach 2:
The patent changes the approach from static manual mappings to dynamic automated discovery. Instead of requiring fixed mapping configurations for each data source, the system uses parameterized discovery code that can automatically adapt to different data sources. This allows new data providers to be integrated by simply adding their data sources without creating new manual mappings.
3Measurement precision
If pre-determined metrics are collected in advance, then data can be analyzed for specific questions, but new questions cannot be addressed if no stored metrics are readily available
Solution Approach 1:
The system transitions from static pre-determined metrics to dynamic automated discovery. The discovery engine dynamically executes discovery code against local data sources to automatically create structured sets of facts relevant to any question. This allows the system to adapt to new questions by automatically discovering and creating the necessary metrics and queries on-demand, while maintaining measurement precision through automated fact creation.
Solution Approach 2:
The patent implements preliminary action through automated discovery code execution. Before analyzing data for any question, the discovery engine first automatically executes discovery code to create the necessary structured sets of facts. This preliminary automated action ensures that all necessary data structures and metrics are ready before the actual analysis, allowing both accurate measurement and flexible adaptation to new questions.
Data Source
AI summary
According to some embodiments, a central cloud-based repository of data may contain consolidated facts about enterprise applications. A computer processor of a discovery engine may execute discovery in a local application process and, based on the executed discovery, automatically create a generic structured set of facts from locally accessible data. The discovery engine may then store the generic structured set of facts in the central cloud-based data repository using a standard format (e.g., JSON). The central cloud-based data repository may, for example, store facts from different application instances and/or facts from different applications. According to some embodiments, a user generated query is created in a query language (e.g., SQL) and executed on the central cloud-based repository of data to automatically create an answer. Moreover, an automated ML agent may, in some embodiments, evaluate information in the central cloud-based repository of data.