Analytic Platform Causal Bitmap Data Compression
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Solution Overview
Problem
Current data analysis systems are inflexible and computationally complex, making it difficult to manage and integrate market and consumer data, leading to sub-optimal insights and slow analysis processes due to rigid data cubes and ad hoc analytic tools that lack on-demand access and granular integration capabilities.
Innovation Solution
An analytic platform that enables rapid and flexible manipulation of data sets through pre-aggregation, dynamic projections, and real-time processing, using a causal bitmap fake technique to reduce data complexity and improve query performance, while allowing for customizable hierarchies and releasability rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional OLAP methods with fixed aggregation points are used, then data complexity is reduced, but flexibility and adaptability to change are lost
Solution Approach 1:
The patent implements dynamic aggregation points that can be modified at query-time rather than being fixed a priori. The system allows analysts to change aggregation levels and dimensions on-the-fly without requiring complex restructuring, making the OLAP system adaptive to changing analytical needs while maintaining manageable data complexity through controlled dynamic adjustments.
Solution Approach 2:
The system pre-configures the data cube structure and dimension hierarchies in advance, but reserves the ability to dynamically adjust aggregation points during query execution. This preliminary setup provides a foundation that reduces initial complexity while allowing flexibility to be introduced when needed through dynamic modifications to the pre-established structure.
2Ease of operation
If a priori decisions are made for data projection and aggregation, then query processing is simplified, but the ability to adapt to changing requirements is reduced
Solution Approach 1:
The patent enables dynamic modification of aggregation points and projection parameters at query-time. Analysts can adjust the level of aggregation and dimension combinations based on specific query requirements without being constrained by fixed a priori decisions, thus maintaining ease of operation while significantly improving adaptability to changing analytical needs.
Solution Approach 2:
The system allows parameters such as aggregation levels, dimension selections, and projection methods to be changed dynamically during query execution. This parameter flexibility enables the system to adapt to different analytical scenarios while maintaining simplified query processing through a unified dynamic framework rather than requiring multiple fixed configurations.
3Reliability
If extensive manual setup is performed for releasability rules, then data security and compliance are improved, but system brittleness and inability to adapt to changes increase
Solution Approach 1:
The patent implements dynamic releasability rules that can be adjusted in response to changing data, dimensions, third parties, and query requirements. Rather than relying on static manual configurations that become brittle over time, the system dynamically evaluates releasability conditions based on current state, maintaining security and compliance while adapting to changes without extensive manual reconfiguration.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor changes in data, dimensions, third parties, and query contexts, and automatically adjust releasability rules accordingly. This feedback-driven approach maintains data security and compliance by continuously evaluating current conditions against releasability criteria, eliminating the brittleness associated with static manual setups.
4Device complexity
If rigid data cubes with fixed hierarchies are used, then data integration is simplified, but navigation flexibility and analysis speed are reduced
Solution Approach 1:
The patent implements dynamic data cube structures where hierarchies and aggregation points can be adjusted at query-time. This dynamic approach maintains simplified data integration through a unified cube structure while significantly improving analysis speed by allowing the system to optimize query execution paths and aggregation strategies based on specific analytical needs rather than being constrained by fixed rigid hierarchies.
Data Source
AI summary
Systems and methods are presented that may involve receiving a causal fact dataset including facts relating to items perceived to cause actions, wherein the causal fact dataset includes a data attribute that is associated with a causal fact datum. It may also involve pre-aggregating a plurality of the combinations of a plurality of causal fact data and associated data attributes in a causal bitmap. It may also involve selecting a subset of the pre-aggregated combinations based on suitability of a combination for the analytic purpose. It may also involve storing the subset of pre-aggregated combinations to facilitate querying of the subset.