Garden: a real-time processing framework for continuous top- k trajectory similarity search

Continuous top- k trajectory similarity Search (C k Search) is now commonly required in real-time large-scale trajectory analysis, enabling the distributed stream processing engines to discover various dynamic patterns. As a fundamental operator, C k Search empowers various applications, e.g., contact tracing during an outbreak and smart transportation. Although extensive efforts have been made to improve the efficiency of non-continuous top- k search, they do not consider dynamic capability of indexing ( R1 ) and incremental capability of computing ( R2 ). Therefore, in this paper, we propose a generic C k Search-oriented framework for distributed real-time trajectory stream processing on Apache Flink, termed as Garden . To answer R1 , we design a sophisticated distributed dynamic spatial index called Y-index, which consists of a real-time load scheduler and a two-layer indexing structure. To answer R2 , we introduce a state reusing mechanism and index-based pruning methods that significantly reduce the computational cost. Empirical studies on real-world data validate the usefulness of our proposal and prove the huge advantage of our approach over state-of-the-art solutions in the literature.