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An efficient location update mechanism for continuous queries over moving objects

机译：一种有效的位置更新机制，可对移动对象进行连续查询

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摘要

In a moving-object database system that supports continuous queries (CQ), an important problem is to keep the location data consistent with the actual locations of the entities being monitored, in order to produce correct query results. This goal is often difficult to achieve due to limited network resources. However, if an object is not required by any query, its value need not be refreshed. Based on this observation, we redefine the notion of temporal consistency of data items with respect to the query result, where only data items that are relevant to the CQs need to be fresh. To exploit this correctness definition, we develop an adaptive time-based update technique called query-result update (QRU). The advantage of this technique is that it identifies objects with different levels of significance to the correctness of query results. Locations of objects that have more impact to the query result are acquired more frequently than the ones that do not. To achieve this objective, queries are classified into rank-based (i.e., ranks of objects are critical to query results) and non-rank-based. For each query class, QRU decides the time instant that an object should send a location update based on the predicted impact of the object to the query result. Moreover, the location update frequency of each object is continuously adjusted in order to adapt to the accuracy of the prediction model used. We evaluate the effectiveness of QRU by simulating execution of CQs over synthetic and real data sets. We also compare QRU experimentally with existing location update policies, namely the distance-based method, the time-based method, the speed dead-reckoning method, as well as the safe region strategy.
机译：在支持连续查询（CQ）的移动对象数据库系统中，一个重要的问题是保持位置数据与被监视实体的实际位置一致，以便产生正确的查询结果。由于网络资源有限，通常很难实现该目标。但是，如果任何查询都不需要对象，则无需刷新其值。基于此观察，我们重新定义了数据项相对于查询结果的时间一致性的概念，其中仅需要与CQ相关的数据项是新鲜的。为了利用此正确性定义，我们开发了一种称为查询结果更新（QRU）的基于时间的自适应更新技术。该技术的优势在于，它可以识别对查询结果的正确性具有不同重要性级别的对象。与不影响查询结果的对象相比，获取对查询结果影响更大的对象的位置的频率更高。为了实现该目标，将查询分为基于等级的（即，对象的等级对查询结果至关重要）和基于非等级的查询。对于每个查询类，QRU会根据对象对查询结果的预测影响来决定对象应发送位置更新的时间。此外，连续地调整每个对象的位置更新频率，以适应所使用的预测模型的准确性。我们通过模拟在综合和真实数据集上执行CQ来评估QRU的有效性。我们还通过实验将QRU与现有的位置更新策略进行了比较，这些策略分别是基于距离的方法，基于时间的方法，速度死区重击方法以及安全区域策略。

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