Indexlet Data Structure for Scalable Cloud Indexing
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Solution Overview
Problem
Current systems face challenges in efficiently managing and analyzing large datasets with frequent updates, as existing index generation and data processing methods are computationally intensive and time-consuming, leading to suboptimal performance in handling large datasets with frequent appends.
Innovation Solution
The implementation of indexlets and a distributed indexation process that allows for scalable index generation and hypercube domain calculation, enabling efficient data management and analysis by partitioning data into smaller blocks and using bidirectional indexing to maintain associations among data points, thereby allowing for quick evaluation of expressions without recreating the hypercube upon data updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional index generation methods are used for large datasets with frequent updates, then data associations can be maintained, but computational intensity and processing time increase significantly
Solution Approach 1:
The patent divides the traditional monolithic index into multiple smaller, independent indexlets that can be generated and updated separately. Each indexlet corresponds to a specific dimension or attribute, allowing the system to process only the relevant portion of data during updates rather than regenerating the entire index structure.
Solution Approach 2:
The system dynamically generates indexlets on-demand based on query requirements and data updates, rather than maintaining a static comprehensive index. This allows the index structure to adapt to changing data conditions and query patterns, improving processing efficiency while maintaining data associations.
2Reliability
If the hypercube is recreated upon data updates, then data consistency is ensured, but processing time and computational resources increase
Solution Approach 1:
Instead of treating the hypercube as a monolithic structure that must be recreated, the patent segments it into multiple indexlets that can be updated independently. When data changes occur, only the affected indexlet(s) are regenerated and merged back into the hypercube, rather than regenerating the entire structure.
Solution Approach 2:
The system pre-partitions the data and index structure into manageable indexlets before updates occur. This preliminary segmentation allows for faster update processing by limiting the scope of regeneration operations to only the necessary indexlet portions rather than the entire hypercube.
3Measurement precision
If comprehensive indexing is performed on all data, then query accuracy is improved, but system complexity and overhead increase
Solution Approach 1:
The patent segments the comprehensive indexing task into multiple specialized indexlets, each handling a specific dimension or attribute. This segmentation reduces the complexity of individual index structures while collectively providing comprehensive coverage for accurate queries across all data dimensions.
Solution Approach 2:
Each indexlet is designed to serve multiple purposes: it can be used independently for queries related to its specific dimension, combined with other indexlets for multi-dimensional queries, and updated independently when data changes occur. This multi-functionality reduces overall system complexity while maintaining query accuracy.
Data Source
AI summary
Provided are methods and systems for improved data management, data analysis, and processing. A computing device may receive one or more global queries. Each of the global queries may be associated with one or more local components, such as individual nodes of a cloud network. The computing device may determine one or more indexlets associated with each local component. The computing device may then generate query results for the one or more global queries using a global query plan and the one or more indexlets.