A Hash Data Representation and Query Method Based on Dynamic Counting Bloom Filter

Abstract

The invention discloses a hashing data representing and querying method based on a dynamic counting type Bloom filter. The hashing data representing and querying method comprises the following steps of inserting data elements; deleting the data elements; and querying the data elements. A DCBF (digital continuous beam former) is established by establishing a plurality of isomorphism CBF (continuous beam formers) and an ACBF (analog continuous beam former); by using the DCBF, data are queried efficiency in a big dataset environment without upper limit of the number of the elements; functions of the DCBF are more powerful than functions of the original CBF and functions of a DBF (digital beam former); adaptive dynamic big dataset showing performance is acquired while high-efficiency query is realized; and the querying efficiency can be improved and the network bandwidth can be saved when query is carried out in environments such as a P2P (peer-to-peer) network of a big dataset and cloud computing.

Description

[0001] 【Technical field】 [0002] The invention relates to the technical field of data representation and query methods, in particular to the technical field of representation methods based on Bloom filter hash data. [0003] 【Background technique】 [0004] Bloom filter is a powerful hash data representation method, which can improve query efficiency and save network bandwidth, and has been widely used in data query in the fields of database, network, peer-to-peer (P2P) and cloud computing. However, existing Bloom filters cannot simultaneously delete elements and adapt to the problem of dynamic no-set element upper limit in a large-scale P2P information retrieval environment. In order to solve this problem, we proposed to establish multiple isomorphic counting Bloom filters to adaptively solve the problem of uncertain node data volume and frequent dynamic changes of elements in the P2P network, that is, dynamic counting BloomFilter (hereinafter referred to as DCBF). [0005] 【...

AI Technical Summary

Problems solved by technology

However, in the large-scale P2P information retrieval environment, the existing Bloom filter cannot both delete elements and adapt itself dynamically without the upper limit of set elements.

In order to solve this problem, we propose to establish multiple isomorphic counting Bloom filters to adaptively solve the problem of uncertain data volume of nodes and frequent dynamic changes of elements in P2P networks, that is, dynamic counting BloomFilter (hereinafter referred to as DCBF).