Distributed KNN Server for Scalable Clustering
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Solution Overview
Problem
Current clustering techniques, such as HDBSCAN, face scalability issues due to computationally expensive local join operations for identifying k-nearest neighbors, particularly in high-dimensional data sets, limiting their effectiveness beyond a million data points.
Innovation Solution
A distributed framework for computing k-nearest neighbors using local join executors and a centralized or distributed KNN server, which performs parallel processing and communicates via indices to reduce time and bandwidth, enhancing the efficiency of KNN processing and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional HDBSCAN clustering algorithm is used to identify k-nearest neighbors, then clustering accuracy is maintained, but computational complexity and processing time increase exponentially with data size
Solution Approach 1:
The patent divides the data set into multiple partitions or blocks, and performs k-nearest neighbor search independently on each partition. This segmentation reduces the computational complexity from O(n^2) for the entire data set to O((n/m)^2) for each partition, where m is the number of partitions, while still achieving accurate clustering results through distributed computation.
Solution Approach 2:
The patent introduces a distributed computing dimension by deploying multiple worker nodes across different computational environments. Each worker node processes a subset of data in parallel, transforming the single-threaded computational problem into a multi-dimensional parallel processing problem, thereby reducing overall processing time while maintaining accuracy.
2Measurement precision
If traditional HDBSCAN clustering algorithm is used to identify k-nearest neighbors, then clustering accuracy is maintained, but processing time becomes prohibitive for large data sets
Solution Approach 1:
The patent performs preliminary actions by pre-computing and storing distance metrics or similarity scores between data points before the actual clustering process. This pre-processing step creates an index structure that allows rapid retrieval of k-nearest neighbors during clustering, significantly reducing processing time while preserving clustering accuracy.
Solution Approach 2:
The patent implements continuous useful action through iterative refinement of k-nearest neighbor results. The system performs multiple passes of kNN search with progressively tighter distance thresholds, continuously improving the quality of neighbor identification without requiring complete re-computation, thereby reducing total processing time while maintaining accuracy.
3Productivity
If distributed computing framework is implemented for k-nearest neighbor search, then processing speed and scalability improve, but system complexity and communication overhead increase
Solution Approach 1:
The patent introduces a coordinator node that acts as an intermediary between the master node and worker nodes. This intermediary manages task distribution, collects results, and handles coordination logic, thereby simplifying the complexity for individual worker nodes and enabling scalable distributed computation without requiring complex peer-to-peer communication protocols.
4Adaptability or versatility
If distributed computing framework is implemented for k-nearest neighbor search, then scalability to larger data sets is achieved, but communication bandwidth requirements increase
Solution Approach 1:
The patent extracts and processes data locally on each worker node's memory, minimizing the need to transfer large amounts of data across the network. Only essential results such as k-nearest neighbor indices and distance metrics are communicated back to the coordinator, significantly reducing communication bandwidth requirements while maintaining scalability to large data sets.
Data Source
AI summary
The present teaching relates to method, system, medium, and implementations for identifying k nearest neighbors. A plurality of combined neighborhoods are received from a plurality of local join executors. Each combined neighborhood represents a neighborhood of a source data point and has one or more pairs of neighbors, each of which includes the source data point, a neighbor of the source point, and a distance in-between. A plurality of KNN lists corresponding to a plurality of source data points are obtained. Each KNN list includes K neighbors to a corresponding source data point, each of which is represented by an index of the neighbor and a distance between the source data point and the neighbor. The plurality of KNN lists are updated based on the plurality of combined neighborhoods to generate updated KNN lists.