Interest-Driven Business Intelligence System for Geo-Spatial Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current business intelligence systems face challenges in efficiently processing and analyzing large volumes of geo-spatial data, particularly in generating reporting data that meets specific user requirements, due to limitations in data filtering, aggregation, and visualization.
Innovation Solution
An interest-driven business intelligence system is implemented, which includes a raw data storage layer, an intermediate processing layer that performs extract, transform, and load (ETL) processes, and a data mart for metadata storage. This system automatically generates metadata, compiles interest-driven data pipelines, and produces geo-spatial data by filtering and bounding raw data based on metadata, enabling dynamic data visualization and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional business intelligence systems process large volumes of geo-spatial data, then data analysis capability is improved, but processing efficiency and system performance deteriorate
Solution Approach 1:
The patent segments the monolithic data processing system into multiple specialized components: a data ingestion layer for raw data intake, a metadata generation layer for data characterization, a filtering layer for data reduction, and a visualization layer for data presentation. This segmentation allows each component to process only the data it needs, improving overall processing efficiency while handling large volumes of geo-spatial data.
Solution Approach 2:
The system performs preliminary actions by automatically generating metadata that describes the raw geo-spatial data before the actual analysis occurs. This pre-characterization of data includes creating data models, identifying spatial relationships, and establishing data quality metrics, which enables faster and more efficient subsequent processing and filtering operations.
2Loss of information
If comprehensive metadata is generated to describe raw data, then data visibility and analysis capability are improved, but system complexity and processing overhead increase
Solution Approach 1:
The metadata generation component serves multiple functions simultaneously: it characterizes data structure, validates data quality, identifies spatial relationships, and creates indexing information for fast retrieval. This multi-functional approach provides comprehensive data visibility without requiring separate systems for each function, thereby managing complexity while enhancing analysis capability.
3Manufacturing precision
If data filtering and bounding operations are performed on raw geo-spatial data, then relevant reporting data quality is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary filtering and bounding operations during the metadata generation phase, before the actual reporting requirements are finalized. By pre-identifying relevant spatial boundaries and filtering criteria based on metadata analysis, the system reduces the volume of data that needs to be processed during report generation, thereby improving data quality while minimizing additional processing time.
Data Source
AI summary
Systems and methods for interest-driven business intelligence systems including geo-spatial data in accordance with embodiments of the invention are illustrated. An interest-driven business intelligence system including raw data storage and perform extract, transform, and load processes, a data mart, and an intermediate processing layer, wherein the intermediate processing layer is configured to automatically generate metadata describing the raw data, derive reporting data requirements, and compile an interest-driven data pipeline based upon the reporting data requirements, where compiling the interest-driven data pipeline includes generating ETL processing jobs to generate geo-spatial data from the raw data, determining bounding data, bounding the filtered raw data based on the bounding data, generating geo-spatial data, and storing the geo-spatial data, generating reporting data including data satisfying the reporting data requirements based on the geo-spatial data, and storing the reporting data in the data mart for exploration by an interest-driven data visualization system.