Universal Analytical Data Mart Integrating Disparate Sources
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Solution Overview
Problem
Existing systems face challenges in efficiently integrating and analyzing large amounts of data from disparate sources, particularly in identifying patterns across different types of applications and managing diverse business logic requirements.
Innovation Solution
A universal analytical data mart system and data structure that allows data from various sources to be integrated, enabling streamlined business intelligence, reporting, and ad-hoc analysis by processing dimensional and fact data and storing it in a datamart that provides multiple frames of reference including people, places, objects, and events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data from multiple disparate sources is integrated into a unified analytical data mart, then data consistency and analysis accuracy are improved, but system complexity and integration difficulty increase
Solution Approach 1:
The patent implements an intermediary layer (analytical data mart with dimensional and fact tables) between disparate data sources and analytical applications. This intermediary standardizes data integration by processing dimensional data (descriptors) and fact data (measurable attributes) through unified ETL processes, thereby improving data consistency while managing system complexity through abstraction.
Solution Approach 2:
The patent segments the integrated data system into distinct dimensional tables (containing descriptors like people, places, objects, events) and fact tables (containing measurable attributes). This segmentation allows independent management and processing of different data types, improving overall data consistency while reducing the complexity of managing integrated data as a monolithic structure.
2Adaptability or versatility
If manual management of different business logic rules for different applications is performed, then application-specific analysis requirements are met, but time consumption and error rates increase
Solution Approach 1:
The patent creates a universal analytical data mart structure that serves multiple applications simultaneously. The standardized dimensional and fact table design, combined with unified ETL processes, enables different analytical applications to access and analyze data according to their specific needs without requiring separate manual management of business logic rules, thereby reducing time consumption and error rates while maintaining adaptability.
3Productivity
If data is processed and stored in a standardized analytical data mart structure, then data accessibility and analysis efficiency are improved, but data processing complexity increases
Solution Approach 1:
The patent implements preliminary action by pre-processing data into standardized dimensional and fact tables during the ETL phase. Data is transformed, cleaned, and organized into the analytical data mart structure in advance, making it readily accessible for various analytical applications. This preliminary processing improves analysis efficiency while managing processing complexity through automated ETL workflows.
Data Source
AI summary
A device and method are described for a universal analytical data mart and data structure for same. The analytical data mart (ADM) associated data structure is designed to allow data from disparate sources to be integrated, enabling streamlined business intelligence, reporting and ad hoc analysis. Conceptually, the ADM enables analytics and business intelligence from multiple frames of reference including people, such as parties and actors including individuals and organizations, places, such as addresses with geographic information at various levels of view, objects, such as insured properties, automobiles and machinery, and events, milestones which happen at points in time and provide analytical/business value.