Indexlet-Based Aggregation for Scalable Hypercube Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data management and analysis systems face challenges in efficiently processing large datasets with numerous dimensions, particularly in generating and updating multi-dimensional cube data structures, as they often require extensive computational resources and are not scalable for frequent data additions and updates.
Innovation Solution
The proposed system employs a distributed architecture with indexlet generation and bi-directional indexing, allowing for parallel processing and efficient data traversal, using indexlets to partition data and distribute computations across multiple workers, thereby reducing computational overhead and enabling scalable index generation and hypercube evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data management systems process large datasets with numerous dimensions, then comprehensive data analysis is achieved, but computational resources are excessively consumed and scalability is poor
Solution Approach 1:
The patent divides the data model into multiple partitions using indexlets, which are smaller, manageable segments of the overall data structure. Each partition can be processed independently by different workers, reducing the computational burden on any single processing unit and enabling parallel processing across multiple nodes.
Solution Approach 2:
The patent introduces a new dimensional structure by organizing data into indexlets with bi-directional indexing capabilities. This creates an additional layer of organization that allows efficient traversal and aggregation without requiring full hypercube reconstruction, effectively adding a dimensional approach to data access and processing.
2Reliability
If traditional systems update data frequently, then data freshness is maintained, but the need to recreate hypercubes increases computational overhead
Solution Approach 1:
The patent pre-organizes data into indexlet partitions with bi-directional indexes before updates occur. This preliminary structuring allows the system to handle data additions and modifications by updating only the affected indexlet partitions rather than recreating the entire hypercube structure, significantly reducing update time and computational overhead.
Solution Approach 2:
The patent implements a dynamic data structure where indexlet partitions can be independently modified, added, or removed based on data changes. This dynamic organization allows the system to adapt to frequent updates without requiring static, full-hypercube reconstruction, enabling incremental updates that maintain data freshness while minimizing processing time.
3Adaptability or versatility
If distributed processing is implemented, then scalability is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal indexlet structure that can be applied across different distributed nodes with consistent behavior. The bi-directional indexing mechanism provides multi-functional capabilities, allowing the same data structure to serve both as a partitioning mechanism and an access optimization structure, reducing the need for node-specific implementations and simplifying distributed system management.
Data Source
AI summary
Provided are methods and systems for traversing a data model with a plurality of workers to generate a plurality of virtual records for use in evaluating an expression by a plurality of aggregators. Each aggregator may receive a respective virtual record and evaluate a hypercube function and determine a respective hash based on the respective virtual record. The virtual records based on the respective hash values of the respective virtual records may be stored by the plurality of aggregators.