Discovery of Hot Region in Trajectory Databases
Kui-En LIU, Jun-Chao XIAO, Zhiming Ding, Mingshu Li · Journal of Software · 2014
发现被移动对象频繁造访的热门区域是从轨迹数据库中挖掘运动模式的重要前提,而合理约束热门区域的大小是提高轨迹模式的精确表达能力的关键.研究如何从轨迹数据库找出热门区域及如何限制其大小.定义了带有覆盖范围约束的热门区域,并采用过滤-精炼策略发现热门区域.在过滤阶段,设计了一种基于网格的密集区域发现近似算法以提高发现效率;在精炼阶段,提出了基于趋势和差异性的度量指标,实现了对应区域重构算法及重构参数启发性选择算法,保证了从密集区域中有效提取出符合覆盖范围约束的热门区域.在真实数据集上验证了该工作的有效性.;Mining of the enclosed regions that are visited frequently by moving objects (i.e. hot region) is a critical premise for the discovery of movement patterns from trajectory databases, and restricting their coverage is the key to promote precision and efficiency for representation of trajectory patterns. Given a trajectory database, this paper studies how to discover these hot regions and how to constraint their size. A definition of hot region query with coverage constraints is presented with a filter-refinement framework to construct them. In the filter step, the study introduces a grid-based approximate schema to construction the dense regions efficiently; and in the refinement step, the study proposes two trend-based and dissimilarity-based measures, and designs corresponding algorithms and heuristic parameter selection method to rationally reconstruct the regions under the coverage constraints. Experiments on practical datasets validate the effectiveness of this work.