Reverse k nearest neighbor （RNNk） is a generalization of the reverse nearest neighbor problem and receives increasing attention recently in the spatial data index and query. RNNk query is to retrieve all the data po...Reverse k nearest neighbor （RNNk） is a generalization of the reverse nearest neighbor problem and receives increasing attention recently in the spatial data index and query. RNNk query is to retrieve all the data points which use a query point as one of their k nearest neighbors. To answer the RNNk of queries efficiently, the properties of the Voronoi cell and the space-dividing regions are applied. The RNNk of the given point can be found without computing its nearest neighbors every time by using the rank Voronoi cell. With the elementary RNNk query result, the candidate data points of reverse nearest neighbors can he further limited by the approximation with sweepline and the partial extension of query region Q. The approximate minimum average distance （AMAD） can be calculated by the approximate RNNk without the restriction of k. Experimental results indicate the efficiency and the effectiveness of the algorithm and the approximate method in three varied data distribution spaces. The approximate query and the calculation method with the high precision and the accurate recall are obtained by filtrating data and pruning the search space.展开更多
K-th number query是计算机算法中的一个基础问题,被广泛作为很多算法实现的重要步骤。对该问题进行了深入研究,并找到了单询问渐近时间复杂度最优的算法。目前一般对于多询问的K-th number query问题使用平衡二叉树解决,询问的时间复...K-th number query是计算机算法中的一个基础问题,被广泛作为很多算法实现的重要步骤。对该问题进行了深入研究,并找到了单询问渐近时间复杂度最优的算法。目前一般对于多询问的K-th number query问题使用平衡二叉树解决,询问的时间复杂度为O(lbn)。但该算法实现比较复杂,并且常系数较大,提出了基于Bit Indexed Tree数据结构的算法解决,在同等时间复杂度的前提下,实现简单,隐含的常系数很小。最后进行了实验测试,分析显示该新算法不论在时间上还是空间上都优于现有的算法。展开更多
We propose an influential set based moving k keyword query processing model, which avoids the shortcoming of safe region-based approaches that the update cost and update frequency cannot be optimized simultaneously. B...We propose an influential set based moving k keyword query processing model, which avoids the shortcoming of safe region-based approaches that the update cost and update frequency cannot be optimized simultaneously. Based on the model, we design a parallel query processing method and a parallel validation method for multicore processing platforms. The time complexity of the algorithms is O((log|D|+p.k)/p.k)?and O(log p.k), respectively, which are all O(1/k) times the time complexity of the state-of-the-art method. The experiment result confirms the superiority of our algorithms over the state-of-the-art method.展开更多
在车联网场景中,现有基于位置服务的隐私保护方案存在不支持多种类型K近邻兴趣点的并行查询、难以同时保护车辆用户和位置服务提供商(Location-Based Service Provider,LBSP)两方隐私、无法抵抗恶意攻击等问题。为了解决上述问题,提出...在车联网场景中,现有基于位置服务的隐私保护方案存在不支持多种类型K近邻兴趣点的并行查询、难以同时保护车辆用户和位置服务提供商(Location-Based Service Provider,LBSP)两方隐私、无法抵抗恶意攻击等问题。为了解决上述问题,提出了一种保护两方隐私的多类型的路网K近邻查询方案MTKNN-MPP。将改进的k-out-of-n不经意传输协议应用于K近邻查询方案中,实现了在保护车辆用户的查询内容隐私和LBSP的兴趣点信息隐私的同时,一次查询多种类型K近邻兴趣点。通过增设车载单元缓存机制,降低了计算代价和通信开销。安全性分析表明,MTKNN-MPP方案能够有效地保护车辆用户的位置隐私、查询内容隐私以及LBSP的兴趣点信息隐私,可以保证车辆的匿名性,能够抵抗合谋攻击、重放攻击、推断攻击、中间人攻击等恶意攻击。性能评估表明,与现有典型的K近邻查询方案相比,MTKNN-MPP方案具有更高的安全性,且在单一类型K近邻查询和多种类型K近邻查询中,查询延迟分别降低了43.23%~93.70%,81.07%~93.93%。展开更多
基金Supported by the National Natural Science Foundation of China (60673136)the Natural Science Foundation of Heilongjiang Province of China (F200601)~~
文摘Reverse k nearest neighbor （RNNk） is a generalization of the reverse nearest neighbor problem and receives increasing attention recently in the spatial data index and query. RNNk query is to retrieve all the data points which use a query point as one of their k nearest neighbors. To answer the RNNk of queries efficiently, the properties of the Voronoi cell and the space-dividing regions are applied. The RNNk of the given point can be found without computing its nearest neighbors every time by using the rank Voronoi cell. With the elementary RNNk query result, the candidate data points of reverse nearest neighbors can he further limited by the approximation with sweepline and the partial extension of query region Q. The approximate minimum average distance （AMAD） can be calculated by the approximate RNNk without the restriction of k. Experimental results indicate the efficiency and the effectiveness of the algorithm and the approximate method in three varied data distribution spaces. The approximate query and the calculation method with the high precision and the accurate recall are obtained by filtrating data and pruning the search space.
文摘K-th number query是计算机算法中的一个基础问题,被广泛作为很多算法实现的重要步骤。对该问题进行了深入研究,并找到了单询问渐近时间复杂度最优的算法。目前一般对于多询问的K-th number query问题使用平衡二叉树解决,询问的时间复杂度为O(lbn)。但该算法实现比较复杂,并且常系数较大,提出了基于Bit Indexed Tree数据结构的算法解决,在同等时间复杂度的前提下,实现简单,隐含的常系数很小。最后进行了实验测试,分析显示该新算法不论在时间上还是空间上都优于现有的算法。
文摘We propose an influential set based moving k keyword query processing model, which avoids the shortcoming of safe region-based approaches that the update cost and update frequency cannot be optimized simultaneously. Based on the model, we design a parallel query processing method and a parallel validation method for multicore processing platforms. The time complexity of the algorithms is O((log|D|+p.k)/p.k)?and O(log p.k), respectively, which are all O(1/k) times the time complexity of the state-of-the-art method. The experiment result confirms the superiority of our algorithms over the state-of-the-art method.
文摘在车联网场景中,现有基于位置服务的隐私保护方案存在不支持多种类型K近邻兴趣点的并行查询、难以同时保护车辆用户和位置服务提供商(Location-Based Service Provider,LBSP)两方隐私、无法抵抗恶意攻击等问题。为了解决上述问题,提出了一种保护两方隐私的多类型的路网K近邻查询方案MTKNN-MPP。将改进的k-out-of-n不经意传输协议应用于K近邻查询方案中,实现了在保护车辆用户的查询内容隐私和LBSP的兴趣点信息隐私的同时,一次查询多种类型K近邻兴趣点。通过增设车载单元缓存机制,降低了计算代价和通信开销。安全性分析表明,MTKNN-MPP方案能够有效地保护车辆用户的位置隐私、查询内容隐私以及LBSP的兴趣点信息隐私,可以保证车辆的匿名性,能够抵抗合谋攻击、重放攻击、推断攻击、中间人攻击等恶意攻击。性能评估表明,与现有典型的K近邻查询方案相比,MTKNN-MPP方案具有更高的安全性,且在单一类型K近邻查询和多种类型K近邻查询中,查询延迟分别降低了43.23%~93.70%,81.07%~93.93%。
文摘动态路网k近邻(kNN)查询是许多基于位置的服务(LBS)中的一个重要问题。针对该问题,提出一种面向动态路网的移动对象分布式kNN查询算法DkNN(Distributed kNN)。首先,将整个路网划分为部署于集群中不同节点中的多个子图;其次,通过并行地搜索查询范围所涉及的子图得到精确的kNN结果;最后,优化查询的搜索过程,引入查询范围剪枝策略和查询终止策略。在4个道路网络数据集上与3种基线算法进行了充分对比和验证。实验结果显示,与TEN~*-Index(Tree dEcomposition based kNN~*Index)算法相比,DkNN算法的查询时间减少了56.8%,路网更新时间降低了3个数量级。DkNN算法可以快速响应动态路网中的kNN查询请求,且在处理路网更新时具有较低的更新成本。
