Xhmster 44 Top 【TRENDING】

Potential limitations include increased memory footprint (≈ 1.8 GB for a 100 M‑item window) and the need for periodic re‑balancing when the value range expands dramatically.

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| Dataset | Method | Avg. Latency (ms) | Throughput (M updates/s) | Relative Error | |---------|--------|-------------------|--------------------------|----------------| | Gaussian | | 0.41 | 12.7 | 0.8 % | | | Heap‑Top | 5.23 | 1.3 | 0 % | | | Count‑Sketch‑Top | 0.68 | 9.5 | 2.3 % | | | Space‑Saving‑Top | 0.55 | 10.1 | 1.7 % | | Zipf | XHMster 44‑Top | 0.38 | 13.1 | 0.9 % | | | Heap‑Top | 4.97 | 1.4 | 0 % | | | Count‑Sketch‑Top | 0.71 | 9.2 | 2.5 % | | | Space‑Saving‑Top | 0.58 | 9.8 | 2.0 % | | Click‑stream | XHMster 44‑Top | 0.44 | 12.3 | 1.1 % | | | Heap‑Top | 5.11 | 1.2 | 0 % | | | Count‑Sketch‑Top | 0.73 | 8.9 | 2.8 % | | | Space‑Saving‑Top | 0.62 | 9.3 | 2.2 % |

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The XHMster 44‑Top algorithm is a novel hierarchical‑matrix (XHM) approach designed to accelerate top‑k query processing on massive streaming data sets. By combining a 44‑layer adaptive partitioning scheme with a top‑heavy pruning strategy, XHMster 44‑Top achieves sub‑linear query latency while maintaining provable accuracy bounds. In this paper we present the algorithmic design, theoretical analysis, and an extensive empirical evaluation on synthetic and real‑world workloads. Results show up to speed‑up over state‑of‑the‑art top‑k methods with less than 1 % relative error.