Distributed Data Histogram Tree for Real-Time Query Precision
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Solution Overview
Problem
Obtaining exact answers to queries on massive datasets consumes significant compute resources and time, making stochastic stream algorithms with HDR histograms inefficient for real-time evaluation.
Innovation Solution
Utilizing a tree data structure to store sampled data objects, where each node includes sufficient statistics and values, allowing for faster computation of histogram information by merging and updating nodes efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional HDR histogram algorithms are used to process massive datasets, then value precision and configurable range are maintained, but computation time becomes unacceptable and real-time evaluation becomes difficult
Solution Approach 1:
The patent segments the massive dataset processing into multiple parallel workers that each handle partitions of the data. Each worker independently processes its partition and contributes to the final histogram, enabling parallel computation that reduces overall processing time while maintaining precision through coordinated aggregation of results across all workers.
Solution Approach 2:
The patent introduces a new dimensional approach by organizing histogram computation across multiple dimensions: data partitions (spatial dimension), processing workers (computational dimension), and hierarchical aggregation levels (organizational dimension). This multi-dimensional structure enables parallel processing while preserving the precision requirements of HDR histogram algorithms.
2Measurement precision
If exact answers are sought for basic queries on streaming data and massive datasets, then accuracy is improved, but compute resources consumption increases significantly
Solution Approach 1:
The patent implements partial action by allowing users to specify approximation parameters that control the trade-off between accuracy and resource consumption. The system can perform exact computation when needed but also supports approximate queries that consume fewer resources, enabling selective application of computational effort based on query requirements.
Solution Approach 2:
The patent changes key parameters of the histogram algorithm including precision levels, partition sizes, and aggregation strategies to optimize the balance between accuracy and resource usage. By adjusting these parameters, the system can adapt to different query requirements and resource availability, providing exact answers when resources permit and approximate answers when resources are constrained.
3Ease of operation
If large amounts of streaming data are stored in arrays for HDR histogram evaluation, then data accessibility is improved, but the data structure becomes difficult to evaluate in real time
Solution Approach 1:
The patent segments the large dataset into smaller partitions that can be processed independently and in parallel. This segmentation maintains data accessibility by organizing it in a structured format while dramatically improving real-time evaluation speed through parallel processing of multiple partitions simultaneously, rather than sequentially processing a single large array.
Solution Approach 2:
The patent performs preliminary actions by pre-processing and organizing data into optimized partition structures before actual histogram evaluation. This preliminary organization enables faster real-time queries by having data ready in an optimal format for parallel processing, reducing the computational burden during actual evaluation operations.
Data Source
AI summary
A tree data structure that includes an exponential root node and a plurality of regular child nodes arranged in hierarchical levels is generated. A number of the hierarchical levels corresponds to a specified precision value. A numerical data value is received. The numerical data value is truncated according to the specified precision value to generate a truncated value. Portions of the truncated value are stored in the tree data structure by storing an exponent of the truncated value in the exponential root node, and storing each significant digit of the truncated value in a respective regular child node based on significance level. Statistical information is computed for each node storing a portion of the truncated value. The statistical information is provided in response to a query.