Quantile Estimation Engine for Distributed Data Bins
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Solution Overview
Problem
Estimating quantiles in large data sets stored in distributed systems is challenging due to the complexity of accessing and processing data across multiple nodes, where different components have varying data portions.
Innovation Solution
A method is implemented where a quantile estimation engine identifies minimum and maximum data values, defines data bins across the range, determines bin counts, and iteratively refines these bins to converge on the specified quantile, allowing for precise estimation within a predetermined precision level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantile estimation is performed on large data sets stored in distributed systems, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent divides the distributed data into multiple bins across different nodes, where each node stores and processes a segment of the data. The quantile estimation is performed locally at each node for its segment, and then the results are combined. This segmentation allows precise quantile estimation without requiring all nodes to process the entire data set, thus reducing overall system complexity while maintaining measurement precision.
Solution Approach 2:
The patent introduces an intermediary coordination mechanism that facilitates communication between nodes for exchanging bin information and quantile estimates. This intermediary layer simplifies the complexity of direct node-to-node coordination by providing a standardized interface for data exchange and result aggregation, enabling precise quantile estimation across distributed systems without proportionally increasing device complexity.
2Productivity
If data is distributed across multiple nodes, then productivity is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent segments the quantile measurement task across multiple nodes, where each node independently processes its local data segment and computes quantile estimates for its portion. This segmentation enables parallel processing that improves productivity while making measurement easier at each node. The final quantile is determined by combining the local estimates, which is a simple aggregation operation that does not significantly increase measurement difficulty.
Solution Approach 2:
The patent creates copies of bin information and quantile estimates at different nodes. Each node maintains local copies of the data segments and intermediate results, enabling independent processing and improving productivity. The copying mechanism allows each node to work with identical data structures and measurement methods, simplifying the detection and measurement process while distributing the computational workload to improve throughput.
3Measurement precision
If iterative refinement of data bins is performed, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The patent applies partial action by performing iterative refinement only for the portions of data that are necessary to achieve the desired precision. Instead of uniformly processing all data at full precision, the system refines bins selectively based on the quantile position and data distribution. This partial refinement approach improves measurement precision for the target quantile while reducing the total computational energy consumption compared to full refinement of all data.
Solution Approach 2:
The patent dynamically changes parameters such as bin width, number of bins, and refinement level based on the current state of quantile estimation and data distribution. As the estimation converges, the system adjusts these parameters to reduce computational effort. This parameter adaptation allows the system to achieve high measurement precision when needed while minimizing energy consumption during early iterations or for less critical quantiles, resolving the contradiction between precision and energy use.
Data Source
AI summary
In accordance with the teachings described herein, systems and methods are provided for estimating quantiles for data stored in a distributed system. In one embodiment, an instruction is received to estimate a specified quantile for a variate in a set of data stored at a plurality of nodes in the distributed system. A plurality of data bins for the variate are defined that are each associated with a different range of data values in the set of data. Lower and upper quantile bounds for each of the plurality of data bins are determined based on the total number of data values that fall within each of the plurality of data bins. The specified quantile is estimated based on an identified one of the plurality of data bins that includes the specified quantile based on the lower and upper quantile bounds.