MEU-miner: Bitset-guided multi-dimensional high-utility sequential pattern mining with parallelism and adaptive top-k

High-utility sequential pattern mining (HUSPM) aims to uncover valuable sequential behaviors to support decision making across domains such as e-commerce analytics and Web interaction modeling. Despite extensions to multi-dimensional settings, existing methods remain limited by context-agnostic pruning that inflates candidate sets and degrades runtime performance, list-based utility storage whose memory footprint balloons as dimensionality grows, and the absence of parallel execution and adaptive Top-k control, which together constrain scalability on large datasets. To address these challenges, we propose a family of methods-MEU-Miner, PMEU-Miner, and TKMEU-Miner-for scalable multi-dimensional HUSPM (MDHUSPM) on commodity hardware. MEU-Miner couples bitset-driven context matching with a dimension-level utility bound (DUB) to enable safe pruning and supports incremental updates/caching of matching vectors and bounds; DUB is a safe upper bound guaranteed by the anti-monotonicity of the context lattice, ensuring that no valid multi-dimensional high-utility pattern is ever pruned. PMEU-Miner adopts a shared-memory parallel design (thread pool with work stealing and fine-grained critical sections) to obtain substantial multi-core speedups; and TKMEU-Miner provides threshold-free Top-k mining via an adaptive heap with bound-guided early termination. Our experiments span 19 datasets, including 13 real-world and 6 synthetic databases. Extensive experiments on 19 datasets show that MEU-Miner achieves up to 66 × speedups and reduces candidates by up to 80% over state-of-the-art MDHUSPM baselines; PMEU-Miner accelerates mining by up to 180 × through multi-threaded search; and TKMEU-Miner outperforms Top- k competitors by 10 × to 40 × across different values of k . Our framework also maintains stable memory usage as dimensionality increases, advancing the state of the art and enabling fast, reproducible pattern discovery in context-rich sequential data.