The National Center for Biotechnology Information (NCBI) Taxonomy is extensively used in biomedical and ecological research. Typical demands include computing the lowest common ancestor, determining descendant relationships, and listing the descendants of a node. However, existing tools often suffer from inefficient runtime performance. To address this challenge, our paper introduces a novel indexing method, BIT, designed specifically for tree-like data. BIT first encodes the tree-like structure into a bit-vector using the Polychotomic encoding algorithm, subsequently storing the bit-vector in a bitmap. By employing parallel bit operations, BIT significantly accelerates the speed of typical computational tasks. Experimental results on public datasets demonstrate that BIT outperforms baseline systems in task execution performance.

As data grows exponentially, its diversity also increases, including both structured forms and unstructured forms like audio, images, and videos. Advances in AI have improved our ability to analyze unstructured data, leading to the use of multimodal hybrid queries that blend structured and unstructured data. However, database systems struggle due to the lack of adequate data models for multimodal data and languages for these hybrid queries. This paper extends the property graph model to represent multimodal data and their semantic information, introducing essential functions for hybrid graph queries. A high-level graph query language, CypherPlus, is presented, capable of expressing hybrid queries like “Give me the friends of the friends of Mary, who have blond hair and are younger than 30 years old.” A Neo4j-based implementation and experiments over synthetic and real-world datasets demonstrate the approach’s plausibility.

Graph model are increasingly popular among modern applications for its ability to model complex relationships between entities. Users tend to query the data as a graph with graph operations (e.g., graph navigation and exploration). However, a large fraction of the data resides in relational databases or other storage systems. Challenges arise in uniformly querying multiple heterogeneous data sources as a graph. Traditional solutions are limited by time-consuming data integration, expensive development effort, and incomplete query requirements. Thus, we developed Lynx, a general graph query framework, to simplify querying graph data by converting complex statements into basic graph operations. Instead of connecting directly to the data sources, Lynx retrieves data through user-implemented interfaces for those graph operations. We demonstrate Lynx's capabilities through real-world scenarios, showcasing Lynx's ability to process graph queries on multiple heterogeneous data sources and also to be used as a generic graph query engine development framework.