Mining frequent neighboring class sets in spatial databases
Mining frequent neighboring class sets in spatial databases
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DOI：
10.1145/502512.502564
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发表时间：
2001-08
期刊：
影响因子：
--
通讯作者：
Y. Morimoto
中科院分区：
文献类型：
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作者：
Y. Morimoto
我们考虑寻找邻近类集的问题。相邻类集的每个实例的对象使用它们彼此之间的欧几里得距离进行分组。最近，基于位置的服务随着移动计算基础设施（如蜂窝电话和pda）的发展而增长。因此，我们期望看到空间数据库的发展，其中包含非常大量的访问记录，包括位置信息。最典型的类型是点对象的数据库。对象的记录可能包括“请求的服务名称”、“传输的数据包数量”以及指示请求来自何处的x和y坐标值。本文提出的算法可以有效地找到空间数据库中频繁接近的“服务名称”集。例如，它可能会找到一个频繁相邻的类集，其中“车票”和“时间表”经常被请求彼此靠近。通过认识到这一点，基于位置的服务提供商可以为访问“时间表”的客户推广“票”服务。
We consider the problem of finding neighboring class sets. Objects of each instance of a neighboring class set are grouped using their Euclidean distances from each other. Recently, location-based services are growing along with mobile computing infrastructure such as cellular phones and PDAs. Therefore, we expect to see the development of spatial databases that contains very large number of access records including location information. The most typical type would be a database of point objects. Records of the objects may consist of "requested service name," "number of packet transmitted" in addition to x and y coordinate values indicating where the request came from. The algorithm presented here efficiently finds sets of "service names" that were frequently close to each other in the spatial database. For example, it may find a frequent neighboring class set, where "ticket" and "timetable" are frequently requested close to each other. By recognizing this, location-based service providers can promote a "ticket" service for customers who access the "timetable."