Interest-Driven Data Pipeline for Low-Latency BI
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Solution Overview
Problem
Traditional business intelligence systems face limitations in handling large volumes of machine-generated data, leading to high latency and poor interactivity, requiring significant labor from trained engineers and analysts to build and maintain data pipelines, with no active updating of in-memory datasets.
Innovation Solution
Interest-driven Business Intelligence systems dynamically compile and reconfigure data pipelines based on reporting requirements, using an intermediate processing layer to automatically generate reporting data and aggregate data, allowing for real-time updates and user-driven exploration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional business intelligence systems store and process large volumes of machine-generated data using data warehouses, then data storage capacity is improved, but system latency increases and interactivity deteriorates
Solution Approach 1:
The patent segments the data processing system into multiple components: a data warehouse for bulk storage, an intermediate layer for active data, and in-memory computing resources for rapid analysis. This segmentation allows different types of data to be stored and processed in optimal locations, resolving the contradiction between storage capacity and access speed.
Solution Approach 2:
The patent introduces a new dimensional layer (in-memory computing layer) above the traditional data warehouse structure. This adds a temporal and spatial dimension to data access, enabling fast retrieval of frequently accessed data without compromising the storage capacity of the underlying data warehouse.
2Stability of the object's composition
If traditional business intelligence systems use static data pipelines, then system stability is improved, but adaptability to changing reporting requirements deteriorates
Solution Approach 1:
The patent implements dynamic data pipelines that can automatically adjust their configuration based on changing reporting requirements. The system uses metadata-driven approaches and automated code generation to modify pipeline behavior without requiring manual intervention, thus maintaining stability while enabling adaptability.
Solution Approach 2:
The system employs self-service mechanisms where the data pipeline automatically reconfigures itself based on detected changes in reporting requirements. This self-adaptation capability allows the system to maintain stability through automated processes while being highly adaptable to new requirements.
3Manufacturing precision
If manual tuning of data pipelines by trained engineers is performed, then data processing accuracy is improved, but labor requirements and operational complexity increase
Solution Approach 1:
The patent implements self-tuning data pipelines that automatically optimize their own configuration and parameters. The system uses automated algorithms to adjust processing logic, filter criteria, and aggregation rules based on data characteristics and reporting requirements, eliminating the need for manual tuning by engineers while maintaining high accuracy.
Solution Approach 2:
The patent replaces manual mechanical tuning processes with automated computational systems. Machine learning algorithms and automated code generation tools substitute for human engineers in optimizing data pipeline performance, thereby maintaining accuracy while reducing operational complexity.
4Speed
If in-memory datasets are pre-computed and stored, then query response speed is improved, but data freshness and active updating capability deteriorate
Solution Approach 1:
The patent implements dynamic in-memory datasets that can be automatically updated based on changes in the underlying data warehouse. The system uses change detection mechanisms and incremental loading to maintain data freshness while preserving fast query response times through selective caching and memory management.
Data Source
AI summary
Interest-driven Business Intelligence (BI) systems in accordance with embodiments of the invention are illustrated. In one embodiment of the invention, a data processing system includes raw data storage containing raw data, metadata storage containing metadata that describes the raw data, and an interest-driven data pipeline that is automatically compiled to generate reporting data using the raw data, wherein the interest-driven data pipeline is compiled based upon reporting data requirements automatically derived from at least one report specification defined using the metadata.