We focus on the problem of using contiguous SPM to extract succinct, redundancy controlled patterns from large vehicle trajectories. Although there exist several techniques to reduce the contiguous sequential pattern output such as closed and max SPM, they still produce massive redundant pattern outputs when the input sequence database is sufficiently large and homogeneous — as is often the case for vehicle trajectories. Therefore, in this work we propose DC-SPAN: a distinct contiguous SPM algorithm. DC-SPAN mines a set of sequential patterns where the maximum redundancy of the pattern output is controlled by a user-specified parameter. Through various experiments using real world trajectory datasets we show DC-SPAN effectively controls the redundancy of the pattern output with trade-offs in pattern distinctness. Additionally, our experiments also indicate that DC-SPAN efficiently computes these patterns, incurring only a marginal running time cost over existing state-of-the-art contiguous SPM approaches. Lastly, due to the less redundant and more succinct pattern output we also briefly explore visualisation as a useful technique to interpret the discovered vehicle routes.

我们关注于使用连续SPM从大型车辆轨迹中提取简洁，冗余控制的模式的问题。尽管存在几种减少连续顺序模式输出的技术，例如闭合和最大SPM，但当输入序列数据库足够大且同质时，它们仍会产生大量的冗余模式输出，这通常是车辆轨迹的情况。因此，在这项工作中，我们提出了DC-SPAN：一种独特的连续SPM算法。DC-SPAN挖掘一组顺序模式，其中模式输出的最大冗余度由用户指定的参数控制。通过使用现实世界轨迹数据集的各种实验，我们显示DC-SPAN有效地控制了图案输出的冗余，并在图案清晰度方面进行了权衡。另外，我们的实验还表明，DC-SPAN有效地计算了这些模式，与现有的现有连续SPM方法相比，仅产生了很小的运行时间成本。最后，由于输出的冗余少且简洁，我们还简要介绍了可视化作为解释发现的车辆路线的有用技术。