Query Execution Staging for Data Analytics Productivity
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Solution Overview
Problem
Current data analytics approaches require significant manual effort for data preparation and transformation, leading to inefficiencies and a lack of reuse of intelligence across different business use cases, resulting in lengthy development cycles and missed business opportunities.
Innovation Solution
A system and method for rapid development and deployment of reusable analytic code, utilizing a centralized environment for pre-processing steps and signal generation, allowing for modular integration with various platforms and reducing software development complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data preparation and transformation processes are used, then data can be processed and analyzed, but significant time and effort are expended on tedious manual processes
Solution Approach 1:
The patent segments the data processing workflow into distinct components: data preparation, signal generation, and analytics execution. By separating these functions into modular units, the system enables automated execution of data preparation tasks while allowing data scientists to focus on higher-value analytics work, thereby reducing time expenditure on manual data preparation.
Solution Approach 2:
The patent implements preliminary action by pre-defining data preparation templates, signal generation logic, and transformation rules before analytics execution. These pre-configured elements are stored in a repository and automatically applied during query execution, eliminating the need for manual data preparation for each new analytics task and significantly improving productivity.
2Adaptability or versatility
If intelligence is developed for each specific use case from scratch, then the analytics solution is tailored to the specific problem, but the same intelligence is recreated multiple times across different use cases
Solution Approach 1:
The patent creates a universal analytics platform where signals, data preparation templates, and transformation logic can be reused across multiple use cases. The system allows analytics intelligence to be defined once and then applied universally to different business problems through parameterization and configuration, eliminating redundant development while maintaining use case-specific adaptability.
Solution Approach 2:
The patent implements copying by enabling the replication and reuse of analytics intelligence artifacts (signals, templates, transformation rules) across different use cases. Instead of recreating the same analytical logic for each new problem, the system allows these artifacts to be copied and adapted to new contexts, significantly reducing development time while preserving analytical accuracy.
3Measurement precision
If complex data transformations and calculations are performed manually, then detailed analytics can be achieved, but the process is lengthy and prevents rapid deployment
Solution Approach 1:
The patent replaces manual mechanical data transformation processes with an automated computational system. The platform uses programmed data preparation templates, automated signal generation, and machine-executable transformation rules to perform complex data transformations and calculations, thereby maintaining analytical precision while dramatically increasing deployment speed through automation.
Solution Approach 2:
The patent enables parameter changes by allowing the configuration of data transformation and calculation parameters without requiring manual re-execution of the entire analytics pipeline. Users can modify parameters such as time windows, aggregation levels, and transformation rules, and the system automatically re-executes only the affected portions, maintaining precision while accelerating deployment through selective reprocessing.
Data Source
AI summary
A sequence of queries is divided into stages, where each stage involves one pass over the data, such that the sequence of queries can be executed using the minimum number of passes over the data. The sequence of queries may be processed into a functional dependency graph that represents the relationships between query parameters (signals) and query operations, and the functional dependency graph then may be processed to divide the queries into a number of successive stages such that each stage includes queries that can be executed based on data that exists prior to execution of that stage. A sequence of queries may, and often does, require that one or more intermediate values or datasets be generated using an aggregate function.