Real-Time Data Cubes for No-Code Decisional Aggregation
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Solution Overview
Problem
Traditional data warehousing approaches are costly, inflexible, and provide delayed access to decisional data, making it difficult for organizations to make timely and efficient decisions.
Innovation Solution
A server-side approach using a no-code toolset and engine that aggregates and summarizes organizational data into real-time queryable cubes, enabling real-time data transformations and summaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data warehousing approaches are used to aggregate and summarize organizational data, then decisional data can be provided, but the system becomes costly to build and maintain, inflexible to organizational changes, and provides delayed access to data
Solution Approach 1:
The patent creates simplified copies of data warehouse functionality through in-memory data structures (cubes, matrices, arrays) that replicate aggregation and summarization capabilities without the complexity of traditional data warehouses. These in-memory structures provide the same decisional data access needs but with reduced complexity and cost.
Solution Approach 2:
The patent uses lightweight, temporary in-memory data structures instead of permanent, complex data warehouse installations. These in-memory cubes and matrices are created on-demand, used for immediate analysis, and then discarded, eliminating the need for expensive, persistent data warehouse infrastructure while providing the same analytical capabilities.
2Reliability
If traditional data warehousing approaches are used, then data aggregation is achieved, but the system is costly to implement and modify as organizational needs evolve
Solution Approach 1:
The patent implements dynamic, in-memory data structures that can be created, modified, and reconfigured in real-time based on changing organizational needs. Unlike static data warehouses, these in-memory cubes and matrices can be instantly adapted to new requirements without re-engineering, providing both aggregation capability and adaptability.
Solution Approach 2:
The patent changes the fundamental parameters of data storage from persistent disk-based structures to volatile in-memory structures. This parameter change enables rapid creation and modification of data models, allowing the system to adapt to organizational changes while maintaining aggregation capabilities through in-memory computational structures.
3Reliability
If traditional data warehousing approaches are used, then summarized views of organizational data are provided, but real-time access is rarely available due to loading and refresh delays
Solution Approach 1:
The patent maintains continuous, real-time access to summarized data through in-memory structures that are continuously updated as source data changes. Unlike batch-refresh data warehouses, these in-memory cubes provide continuous availability of accurate summarized data without interruption or delay, enabling real-time decision-making.
Solution Approach 2:
The patent replaces the mechanical, batch-oriented data loading and refreshing mechanisms of traditional data warehouses with in-memory computational structures that provide instantaneous access. This substitution eliminates the time delays inherent in mechanical data transfer and processing while maintaining data accuracy through in-memory computational integrity.
4Reliability
If traditional data warehousing approaches are used, then decisional data is provided, but extensive coding and re-engineering are required when data structure or organizational workings change
Solution Approach 1:
The patent creates universal in-memory data structures that can serve multiple functions and adapt to various organizational needs without requiring custom coding. These in-memory cubes and matrices provide a unified approach to data aggregation that works across different scenarios, eliminating the need for extensive coding and re-engineering when requirements change.
Data Source
AI summary
Various embodiments of the present technology generally relate to generating decisional data from organizational data. More specifically, some embodiments relate to aggregating and summarizing organizational data into cubes of decisional data in a no-code development environment. In one embodiment of the present technology, a method of providing decisional data to endpoints comprises aggregating organizational data into a cube of decisional data, wherein the cube is a multidimensional dataset comprising the decisional data. The method further comprises receiving a cube query from an endpoint, wherein the cube query is related to at least a portion of the decisional data in the cube and comprises metadata related to the cube, generating cube query results, wherein the cube query results comprise a summarized portion of the decisional data in the cube, and returning the cube query results to the endpoint.