Approximate membership query (AMQ) data structures can approximately determine whether an element is in the set with high efficiency. They are widely used in distributed systems, database systems, bioinformatics, IoT applications, data stream mining, etc. However, the memory consumption of AMQ data structures grows rapidly as the data scale grows, which limits the system's ability to process a massive amount of data. The emerging persistent memory provides a close-to-DRAM access speed and terabyte-level capacity, facilitating AMQ data structures to handle massive data. Nevertheless, existing AMQ data structures perform poorly on persistent memory due to intensive random accesses and/or sequential writes. Therefore, we propose a novel AMQ data structure called wormhole filter, which achieves high performance on persistent memory by reducing random accesses and sequential writes. In addition, we reduce the number of log records for lower recovery overhead. Theoretical analysis and experimental results show that wormhole filters significantly outperform competitive state-of-the-art AMQ data structures. For example, wormhole filters achieve 23.26× insertion throughput, 1.98× positive lookup throughput, and 8.82× deletion throughput of the best competing baseline.

Complex event recognition (CER) refers to identifying specific patterns composed of several primitive events in event stores. Since full-scanning event stores to identify primitive events holding query constraint conditions will incur costly I/O overhead, a mainstream and practical approach is using index techniques to obtain these events. However, prior index-based approaches suffer from significant I/O and sorting overhead when dealing with high predicate selectivity or long query window (common in real-world applications), which leads to high query latency. To address this issue, we propose ACER, a Range Bitmap-based index, to accelerate CER. Firstly, ACER achieves a low index space overhead by grouping the events with the same type into a cluster and compressing the cluster data, alleviating the I/O overhead of reading indexes. Secondly, ACER builds Range Bitmaps in batch (block) for queried attributes and ensures that the events of each cluster in the index block are chronologically ordered. Then, ACER can always obtain ordered query results for a specific event type through merge operations, avoiding sorting overhead. Most importantly, ACER avoids unnecessary disk access in indexes and events via two-phase filtering based on the window condition, thus alleviating the I/O overhead further. Our experiments on six real-world and synthetic datasets demonstrate that ACER reduces the query latency by up to one order of magnitude compared with SOTA techniques.