Stream Sampling Method for Signed Weighted Data Updates
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Solution Overview
Problem
Existing sampling techniques are limited in handling signed weighted updates and are unsuitable for modern applications where data is constantly changing, as they primarily focus on unweighted or positively weighted samples, and are not efficient for real-time systems.
Innovation Solution
A stream sampling method that analyzes signed weighted updates, maintains a cache of keys with adjustable sampling thresholds, and uses random number generation to determine key inclusion, ensuring unbiased estimates with bounded variance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If random sampling is used on large datasets, then querying efficiency is improved, but the ability to handle signed weighted updates and real-time changes deteriorates
Solution Approach 1:
The sampling algorithm is made dynamic by allowing the sampling probability to change over time based on the weight of updates. Each element's sampling probability is adjusted according to its current weight, enabling the system to adapt to real-time changes while maintaining efficient sampling. This resolves the contradiction by making the sampling process both efficient and adaptable to signed weighted updates.
Solution Approach 2:
The invention changes the parameter of sampling probability from a fixed value to a dynamic value that depends on the weight of each element. By parameterizing the sampling probability based on element weights, the system can efficiently handle signed weighted updates while maintaining querying efficiency. This resolves the contradiction by transforming the sampling mechanism to accommodate both efficiency and adaptability requirements.
2Measurement precision
If complete database scans are performed, then measurement precision is improved, but time consumption and productivity deteriorate
Solution Approach 1:
The invention extracts a weighted random sample from the large dataset, selecting only the necessary portion of data needed for accurate statistics. By extracting elements with probability proportional to their weight, the system achieves measurement precision comparable to complete scans but with significantly reduced time consumption. This resolves the contradiction by extracting only the essential data needed for accurate measurements.
Solution Approach 2:
Instead of scanning the entire database, the invention creates a representative copy through weighted random sampling. This sampled copy contains enough information to accurately reflect the characteristics of the full dataset, providing measurement precision without the time cost of complete scans. This resolves the contradiction by creating a simplified copy that preserves statistical accuracy while reducing processing time.
3Productivity
If sampling rate is increased, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The invention changes the sampling approach from uniform random sampling to weighted random sampling, where each element's sampling probability is proportional to its weight. This parameter change allows the system to maintain high productivity by sampling fewer elements while achieving better measurement precision because the sampled elements are more representative of the important characteristics of the dataset.
Solution Approach 2:
The invention applies local quality by making different elements have different sampling probabilities based on their weights. Important elements with higher weights are more likely to be sampled, while less important elements are sampled less frequently. This resolves the contradiction by optimizing the sampling process to achieve high productivity while maintaining measurement precision through differentiated sampling strategies.
Data Source
AI summary
Methods, apparatus, and articles of manufacture are disclosed to sample signed weighted updates. Example methods disclosed herein include identifying a first key associated with a data update obtained by sampling a stream of data received from a plurality of nodes in a network, the first key being representative of a first node in the plurality of nodes, adjusting a first value associated with the first key based on a weight associated with the data update, the weight being positive when the data update corresponds to opening of a new data connection with the first node, the weight being negative when the data update corresponds to closing of an existing data connection with the first node, and estimating a status of the first node based on the first value.