Search mechanism for data contents based on bloom filter and tree hybrid structure in system wide information management

Abstract

The system wide information management (SWIM) system infrastructure layer uses information‐centric networking (ICN) to implement the cache routing and sharing of air traffic information data. The SWIM network searches and forwards routes on the basis of content names. The variable length, hierarchical name structure, and routing updates caused by the frequent publication and deletion of content make the implementation of fast name routing lookup algorithms a significant but arduous task. To address this problem, this study designed a name matching mechanism on the basis of the hybrid structure of the Bloom filter and the Tree. Compared with the traditional Bloom filter search scheme, this search mechanism reduces the quantity of Bloom filter insertion entries and reduces the possibility of hash collisions. Compared with the traditional Tree search scheme, the proposed mechanism can effectively solve the problem of numerous memory accesses caused by extremely long name prefixes, improving the query efficiency. In addition, the proposed composite structure is compared with several methods, such as DIPIT and NPT, by analyzing the effects of layering, global delay and search speed. The experimental results show that the proposed structure has a favourable performance in terms of storage overhead and search speed.

PSBF: p-adic Integer Scalable Bloom Filter

“…To address the aforementioned issues, previous studies have put forth over 60 enhanced bloom filters with the aim of enhancing their adaptability and performance [ 4 ]. These enhanced bloom filters exhibit enhanced flexibility and performance benefits in various application scenarios, including content caching [ 5 , 6 , 7 , 8 , 9 , 10 ], data storage [ 11 , 12 , 13 , 14 , 15 ], and other domains. By reducing false positives, optimizing implementation, augmenting collection diversity, and enriching functionalities, these enhanced bloom filters offer improved adaptability and efficacy, thereby rendering bloom filters a more potent tool for handling extensive datasets.…”