Event Data Integration Layer for Unstructured Operational Analytics
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Solution Overview
Problem
Current operating systems for organizations are inadequate in recording and classifying complex business operations, particularly failing to integrate variable and unstructured data, which hinders effective data analytics and decision-making in dynamic environments.
Innovation Solution
A reflective analytics system that collects and integrates user observations and notes into operational data using tags, allowing for auto-filling of contextual information and enabling intelligent processing and display of trends and conclusions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current operating systems are used to record and classify business operations, then basic operational data can be stored, but complex and unstructured event data cannot be effectively integrated or analyzed
Solution Approach 1:
The patent introduces an event data integration layer that acts as an intermediary between unstructured event data and the existing operational data warehouse. This layer includes event data collectors that capture unstructured data from multiple sources, event data classifiers that categorize the data using machine learning algorithms, and event data integrators that merge the classified data with structured operational data. This intermediary structure enables the system to handle complex unstructured data without requiring complete redesign of the existing data infrastructure.
Solution Approach 2:
The patent segments the data integration process into distinct functional modules: data collection, data classification, data integration, and data analysis. Each module handles specific aspects of the data processing pipeline, allowing the system to manage complex unstructured data through a series of manageable steps rather than attempting to process all data types simultaneously in a monolithic structure.
2Measurement precision
If traditional data analytics are used, then quantitative data patterns can be identified, but open-ended contextual and unstructured information cannot be integrated
Solution Approach 1:
The patent transforms unstructured event data into structured formats through automated classification algorithms that assign categorical parameters to previously unstructured information. Machine learning models analyze the content, context, and metadata of event data to generate structured classifications, thereby converting unstructured data into a format that can be integrated with quantitative operational data for comprehensive analytics.
Solution Approach 2:
The patent creates a composite data structure that combines structured operational data with classified unstructured event data. The event data warehouse integrates multiple data types including text descriptions, metadata, timestamps, and classified categories into a unified data structure that preserves the characteristics of both quantitative and qualitative information, enabling holistic analytics that consider both numerical patterns and contextual nuances.
3Productivity
If workflow systems are designed for routine tasks, then operational efficiency can be improved, but they cannot respond to variable and complex environments
Solution Approach 1:
The patent introduces dynamic adaptability into the workflow system by implementing machine learning-based event classification and routing. Instead of static, pre-defined workflow paths, the system dynamically classifies incoming events and automatically routes them to appropriate handlers based on learned patterns and contextual understanding. This dynamic approach allows the system to adapt to variable and complex environments while maintaining efficiency for routine tasks.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously learns from classified event data and operational outcomes to improve its classification and routing decisions. The event data classification models are trained on historical data and refined based on actual system performance, enabling the workflow system to become increasingly adaptive to complex environments over time while preserving the efficiency gains from automated routine task handling.
Data Source
AI summary
A system and method for tagging and integrating event data into operational data with the aid of a digital computer is provided.A plurality of categories of tags are maintained, each of the categories comprising a plurality of tags. A note comprising data about an event from a user is received. One or more of the tags are assigned to the note based on the user and the data. One or more of the tags is received from the user and the note is tagged with the received tags. Operational data comprising workflow data of an objective nature is defined. The note is integrated into the workflow data using the received tags, the assigned tags, and the categories of the received tags and the assigned tags. The workflow data with the integrated note is displayed on a display.