Mining top k best libraries in metaverse with random access and sequential access

In recent years, the metaverse has gained significant attention as a virtual reality space where users can interact with each other and explore various virtual environments. As the popularity of the metaverse continues to rise, the need for efficient library querying algorithms becomes crucial. In this paper, we compare the top k best library querying algorithm with random access and sequential access with the linear querying algorithm. Experimental results demonstrate that our proposed top k best library querying algorithm with random and sequential access outperforms the linear querying algorithm by being approximately 73.844% faster in range querying when k is 5. Furthermore, the relationship between the time consumption of the two querying algorithms and the value of k is discussed. The conditions where our proposed algorithm outperforms the baseline one are also indicated.