Unified Database Architecture for Real-Time Business Intelligence
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Solution Overview
Problem
Current business intelligence systems are inefficient due to the incompatibility between online transaction processing (OLTP) and online analytic processing (OLAP) databases, leading to inadequate real-time business measurements, which result in poor decision-making and organizational inefficiencies.
Innovation Solution
The development of a Process Agnostic Measurement Store (PAMS) and Experience Knowledge Database (EKDB) system that integrates with a Process Agnostic Data Store (PADS), enabling machine self-learning assisted predictive analysis by continuously updating and correcting data in real-time, allowing for rapid computation of business measures and insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If OLTP and OLAP databases are used separately for transaction processing and business intelligence, then each system can operate independently with optimized performance, but data incompatibility and silos prevent efficient real-time business measurements and decision-making
Solution Approach 1:
The patent merges OLTP and OLAP databases into a unified database architecture that maintains the operational efficiency of transaction processing while enabling real-time analytical capabilities. This unified database eliminates data silos and incompatibility issues by providing a single source of truth that supports both transactional operations and business intelligence measurements simultaneously, allowing real-time computation of business measures without data transfer delays or compatibility problems
2Adaptability or versatility
If traditional BI tools require users to manually import operational data, then user flexibility is improved, but time consumption and operational complexity increase
Solution Approach 1:
The unified database system implements self-service capabilities where the database automatically provides data to business intelligence operations without requiring manual user intervention for data import. The system autonomously manages data synchronization, transformation, and delivery between operational and analytical workloads, eliminating the time-consuming manual data import process while maintaining user flexibility through self-service query and analysis interfaces
3Ease of manufacture
If data is stored in siloed OLTP and OLAP systems, then system specialization is improved, but data flow management across boundaries becomes problematic and inefficient
Solution Approach 1:
The unified database architecture provides multi-functionality by serving both transaction processing and business intelligence operations within a single system. This universal database structure eliminates the need for complex data flow management across multiple specialized systems, as the unified database inherently supports both operational transactions and analytical queries through its integrated architecture, reducing data management complexity while maintaining system capabilities
Data Source
AI summary
An automated method of detecting patterns corresponding to a plurality of real world business measures corresponding to a plurality of business processes, assessing the next instance of such measures and related business attributes, and describing the next best action to optimize business outcomes based upon a plurality of control parameters. The system operates by continuously abstracting input data from a process agnostic data system (PADS) that links real-world things, activities and processes, into a process agnostic measure store (PAMS) configured to accept measures data without limitation as to a specific process or a plurality of processes. The machine self-learning system can then automatically project a business outcome, suggest most relevant attributes that can impact the said outcome, and suggest actions to change such outcome(s).