Window Aggregator Using Shared Sliding-Window Computation
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Solution Overview
Problem
Current implementations of window aggregate functions are inefficient when applied to large datasets, leading to increased runtime and memory requirements due to repeated calculations and data copying, especially when dealing with skewed datasets or missing values.
Innovation Solution
A scalable window aggregator framework that uses overlapping sliding windows and shared computation to efficiently calculate aggregate features by reusing previous window results, reducing memory requirements and computation complexity through the use of window pointers and unrolled linked lists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional window aggregate functions are applied to large datasets, then complete aggregate calculations are performed, but runtime and memory requirements increase significantly
Solution Approach 1:
The patent divides the dataset into multiple partitions and processes each partition independently to compute partial aggregate values. This segmentation allows parallel processing across multiple data nodes, reducing overall computation runtime while maintaining complete aggregate calculations through subsequent merging of partial results.
Solution Approach 2:
The patent performs preliminary aggregation operations on each data partition before final merging, computing intermediate aggregate values in advance. This preliminary action reduces the computational burden during the final aggregation phase, significantly improving runtime efficiency for large datasets.
2Measurement precision
If traditional window aggregate functions are applied to large datasets, then complete aggregate calculations are performed, but memory requirements increase due to data copying
Solution Approach 1:
The patent segments data processing across multiple partitions, allowing each partition to be processed independently with minimal memory allocation. This eliminates the need to load entire datasets into memory, significantly reducing memory requirements while maintaining calculation completeness.
Solution Approach 2:
The patent avoids unnecessary data copying by using reference-based operations and incremental updates. Instead of creating multiple copies of large datasets for different window calculations, the system reuses the same data structures with modified parameters, reducing memory consumption.
3Adaptability or versatility
If aggregate features are computed for multiple sliding time windows, then comprehensive temporal analysis is achieved, but computation complexity increases
Solution Approach 1:
The patent merges multiple sliding window calculations by identifying and reusing common computations across different time windows. Instead of independently calculating aggregates for each window, the system combines results from overlapping windows, significantly reducing computation complexity while maintaining temporal analysis flexibility.
Solution Approach 2:
The patent implements a universal aggregation framework that handles multiple time window sizes and types through a single unified algorithm. This multi-functional approach eliminates the need for separate computation logic for each window configuration, reducing overall system complexity while maintaining adaptability.
4Adaptability or versatility
If aggregate features are computed for multiple sliding time windows, then comprehensive temporal analysis is achieved, but runtime increases
Solution Approach 1:
The patent performs preliminary aggregation operations for multiple time windows simultaneously, computing intermediate results that can be reused across different window calculations. This advance preparation significantly reduces the runtime required for comprehensive temporal analysis of multiple sliding windows.
Solution Approach 2:
The patent merges computation results from overlapping time windows, avoiding redundant calculations. By identifying shared data ranges across multiple windows and computing aggregates once for these ranges, the system dramatically reduces overall computation runtime while maintaining flexible temporal analysis capabilities.
Data Source
AI summary
A method performed by a computer is disclosed. The method includes receiving a dataset including one or more data fields. The computer can then receive instructions to compute an aggregate feature of the dataset. The computer can then determine an operator and a window based on the aggregate feature to be computed. The method may then include the computer processing the dataset. The compute may then apply the operator with the window to the processed dataset to compute the aggregate feature of the dataset.