Trie and LOUDS hybrid model for efficient e-commerce processing in cloud environment

Abstract

Set superset query is widely used in e-commerce processing and many other domains, particularly in cloud computing environments. Indexing is an efficient way to model e-commerce data. Many existing indexes, however, primarily focus on enhancing either query performance or space efficiency, often neglecting the need to strike a balance between these two factors. We have observed that upper nodes closer to the root of a tree are frequently accessed, while lower nodes near the leaves tend to entail expensive storage costs. To address this issue, we introduce TLI model, a trie and level-ordered unary degree sequence (LOUDS) hybrid model. The upper part of TLI is a trie, which is optimized for superior query performance. The lower part of TLI uses the LOUDS structure. TLI strikes a good balance between query performance and space utilization. To seamlessly integrate these two parts, we have developed efficient connecting strategies. Our simulation results on localhost demonstrate that TLI outperforms its competitors in terms of both space and time efficiency. Remarkably, it enhances query performance by up to 1.89 times, with a modest 6.72% increase in space overhead compared to LOUDS-based alternatives.