A design method and device for a three-dimensional sketch structure

Abstract

The present application relates to a design method, device, computer equipment and storage medium of a three-dimensional sketch structure. The method includes: constructing a three-dimensional sketch structure for storing flow data information, dividing each bucket in the three-dimensional bucket array into three areas, obtaining the flow data information of the graph structure, and dividing the flow data according to hash functions corresponding to different depths. The data information is updated to the three-dimensional sketch structure. The present invention combines representative key reservation and majority voting, and the three-dimensional sketch structure constructed maintains the structure information and weight information of graph data at the same time, only updates one flow data once, has a constant probability error range guarantee, and uses a majority The voting algorithm selects the stored keys to record the most representative graph edges, which can narrow the error range and achieve reversibility to improve query efficiency.

Description

technical field [0001] The present application relates to the technical field of computer networks, in particular to a design method, device, computer equipment and storage medium for a three-dimensional sketch structure. Background technique [0002] Over the past decade, graph structures have been heavily used to model complex structured data in interactive applications, such as web traffic and social networks. As a time-varying data sequence, streaming data that retains graph structure can continuously describe entities (such as social media users) and connections between entities (such as interactions between users), and become an important tool for network anonymity detection, community discovery, etc. basis of the service. However, analyzing massive streaming data in real time is quite challenging. For example, each link in a large ISP or data center processes approximately millions of packets per second. Faced with such a situation, traditional data structures such...

AI Technical Summary

Problems solved by technology

gSketch is designed for edge frequency, but it can only answer queries based on edge frequency, not more complex graph structure queries

TCM and GSS are designed to preserve the graph structure and support various types of queries, but they cannot perform reverse hash queries, they are recorded by traversing the entire key space to obtain edges or nodes of interest, or storing other index tables key and its hash

gMatrix recovers keys by pruning the keyspace using modular hashing, but the process of recovering keys using modular hashing requires enumerating the items of the subkeyspace, which is computationally expensive

[0005] Therefore, the existing technology cannot take into account the problems of estimation accuracy and query efficiency

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