Exploring context in spatio-temporal data

探索时空数据中的上下文

• 批准号: 227082-2011
• 负责人: Nascimento, Mario
• 金额: $ 2.11万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=478384
• 关键词: Exploring; context; spatio; temporal; data

Humans and animals often present movement behavior that can modeled as patterns. The long-term objective of this research is concerned with such movement patterns, i.e., trajectory patterns that can observed, grouped, summarized and indexed for further analysis. Each of those tasks pose interesting short- to medium-term research challenges. To give but a simple example application, assume that customary routes taken by a set of drivers are known and consider the event of an accident or road construction. Being able to determine in an efficient way which drivers would be affected and therefore proactively advising them of the potential impact of that events has a clear practical value. Note that checking every stored trajectory against each event of potential interest is clearly not practical. As well, handling only the spatial component of trajectories is not enough, e.g., if an accident happens in the morning it is very unlikely to affect those who drive through that location every evening. A different application domain where knowing trajectory patterns is useful is that of routing queries on-demand through mobile objects. For instance, in many application domains, such as farming, having the objects broadcast their data continuously is either too expensive (in terms of energy cost) or unfeasible (due to distances between objects and a central location). We envision a model of query dissemination, where objects are used as the transportation means to take queries from source to destination and back. In this case being able to take advantage of trajectory patterns is clearly an advantage in terms of query routing optimization. Finally, both application scenarios above become much more complex if probabilistic trajectories are considered instead of deterministic ones. We plan to start our research program by considering deterministic trajectories and move naturally towards probabilistic trajectories.

人类和动物经常表现出可以建模为模式的运动行为。 这项研究的长期目标是关注这种运动模式，即，轨迹模式，可以观察，分组，总结和索引，以进一步分析。 这些任务中的每一项都提出了有趣的中短期研究挑战。为了给出一个简单的示例应用，假设一组驾驶员所采取的习惯路线是已知的，并且考虑事故或道路施工的事件。 能够以有效的方式确定哪些驾驶员将受到影响，从而主动告知他们这些事件的潜在影响，具有明显的实用价值。注意，针对每个潜在感兴趣的事件检查每个存储的轨迹显然是不实际的。 同样，仅处理轨迹的空间分量是不够的，例如，如果事故发生在早上，则不太可能影响每天晚上驾车经过该地点的人。 知道轨迹模式是有用的另一个应用领域是通过移动的对象按需路由查询。 例如，在许多应用领域中，例如农业，使对象连续广播其数据要么太昂贵（就能量成本而言），要么不可行（由于对象与中心位置之间的距离）。 我们设想了一个查询传播模型，其中对象被用作交通工具，以查询从源到目的地和回来。 在这种情况下，能够利用轨迹模式在查询路由优化方面显然是一个优势。 最后，如果考虑概率轨迹而不是确定性轨迹，则上述两种应用场景变得更加复杂。 我们计划通过考虑确定性轨迹开始我们的研究计划，并自然地转向概率轨迹。