基于Skyline的态势拖放中一种减少轨迹变化延时方法研究

利用Skyline提供的3D控件技术，容易实现战场态势的三维显示和回放，在回放过程中，用户希望能够进行任意时刻的拖放，以便能够快速查看到用户关心的态势，但在实现拖放时，不仅需要对目标的位置和状态进行更新，而且还需要对划过的轨迹进行快速增补或者擦除，拖动的范围增大，因增补或擦除目标轨迹的延时增加。本文提出一种轨迹分段预处理的方法，将大大减少拖放延时。     

CBD天际线之新视野

随着城市化进程的推进,高品质CBD的天际线正极大地改变着城市的面貌,天际线越来越被看做是一个城市的象征性符号,起着展现城市形象的关键性作用。该文通过树立天际线外郭,天际线立面与天际线层次的三个属性概念,同时结合目前国内天际线发展的最新进展,由此来剖析天际线特征,得出相关规律与结论。     

存在级不确定数据上的概率Skyline计算

概率Skyline计算是在不确定对象集合中找出Skyline概率大于给定阈值的对象,在多目标决策应用中有重要价值.现有的存在级不确定数据上的概率Skyline算法均需要预先建立索引,在数据量很大、维度很高或数据频繁更新时,建立索引往往不可行或者不会带来性能的提升,因此有必要设计通用的非索引算法.提出了存在级不确定数据上概率Skyline的首个非索引算法,用已扫描的数据动态地维护一个概率约束空间,未来落入该空间的对象可以被安全地裁剪.在标准的模拟数据集上维度不超过4时裁剪比率超过99.8%,相比不用裁剪规则的基本算法,查询时间节省50%以上.     

道路网环境下K-支配空间Skyline查询方法

为了弥补已有的研究成果无法直接处理道路网环境下K-支配空间Skyline查询问题的不足,提出了基于网络Voronoi图的道路网环境下K-支配空间Skyline查询方法.该方法将K-支配应用到道路网Skyline查询中以处理多属性数据对象,在实际应用中可以用来解决道路网环境下多目标查询和决策问题.方法主要包括道路网中约减数据集过程和K-支配检查过程.首先基于空间数据点构建网络Voronoi图,并对查询点建立查询凸包,通过网络Voronoi图的性质与查询区域的位置关系对数据集约减,从而优化数据集并且有效地减少查询点重复搜索的现象;然后对候选集的非空间属性进行K-支配检查得到道路网精炼集合;最后对精炼集合进行支配检查得到最终的空间Skyline集合.理论研究和实验表明所提出的方法具有较高的效率,可较好地处理道路网环境下K-支配空间Skyline查询问题.     

Efficient distributed skyline computation using dependency-based data partitioning

Skyline queries, together with other advanced query operators, are essential in order to help identify sets of interesting data points buried within huge amount of data readily available these days. A skyline query retrieves sets of non-dominated data points in a multi-dimensional dataset. As computing infrastructures become increasingly pervasive, connected by readily available network services, data storage and management have become inevitably more distributed. Under these distributed environments, designing efficient skyline querying with desirable quick response time and progressive returning of answers faces new challenges. To address this, in this paper, we propose a novel skyline query scheme termed MpSky. MpSky is based on a novel space partitioning scheme, employing the dependency relationships among data points on different servers. By grouping points of each server using dependencies, we are able to qualify a skyline point by only comparing it with data on dependent servers, and parallelize the skyline computation among non-dependent partitions that are from different servers or individual servers. By controlling the query propagation among partitions, we are able to generate skyline results progressively and prune partitions and points efficiently. Analytical and extensive simulation results show the effectiveness of the proposed scheme.     

On efficient reverse skyline query processing

Given a D-dimensional data set P and a query point q, a reverse skyline query (RSQ) returns all the data objects in P whose dynamic skyline contains q. It is important for many real life applications such as business planning and environmental monitoring. Currently, the state-of-the-art algorithm for answering the RSQ is the reverse skyline using skyline approximations (RSSA) algorithm, which is based on the precomputed approximations of the skylines. Although RSSA has some desirable features, e.g., applicability to arbitrary data distributions and dimensions, it needs for multiple accesses of the same nodes, incurring redundant I/O and CPU costs. In this paper, we propose several efficient algorithms for exact RSQ processing over multidimensional datasets. Our methods utilize a conventional data-partitioning index (e.g., R-tree) on the dataset P, and employ precomputation, reuse, and pruning techniques to boost the query performance. In addition, we extend our techniques to tackle a natural variant of the RSQ, i.e., constrained reverse skyline query (CRSQ), which retrieves the reverse skyline inside a specified constrained region. Extensive experimental evaluation using both real and synthetic datasets demonstrates that our proposed algorithms outperform RSSA by several orders of magnitude under all experimental settings.     

Faster output-sensitive skyline computation algorithm

We present the second output-sensitive skyline computation algorithm which is faster than the only existing output-sensitive skyline computation algorithm [1] in worst case because our algorithm does not rely on the existence of a linear time procedure for finding medians.     

k-dominant Skyline query algorithm for dynamic datasets

At present,most k-dominant Skyline query algorithms are oriented to static datasets,this paper proposes a k-dominant Skyline query algorithm for dynamic datasets.The algorithm is recursive circularly.First,we compute the dominant ability of each object and sort objects in descending order by dominant ability.Then,we maintain an inverted index of the dominant index by k-dominant Skyline point calculation algorithm.When the data changes,it is judged whether the update point will afect the k dominant Skyline point set.So the k-dominant Skyline point of the new data set is obtained by inserting and deleting algorithm.The proposed algorithm resolves maintenance isue of a frequently updated database by dynamically updating the data sets.The experimental results show that the query algorithm can efectively improve query eficiency.     

道路网环境下K-支配空间Skyline查询方法

已有的天际线（Skyline）查询主要聚焦于单用户场景,并基于单用户模型进行Skyline计算,而较少考虑道路网环境下多用户情况. 为了弥补已有方法无法解决道路网络环境下多用户偏好和权重Top-k Skyline查询问题的不足,提出了一种基于道路网环境下多用户偏好Top-k Skyline（multi-user preference Top-k Skyline,MUP-TKS）查询方法. 在道路网环境下考虑多用户的不同偏好和权重进行Skyline查询,可以快速得到符合查询用户群偏好和权重的结果集,提供用户群更好的决策支持.MUP-TKS首先通过所提的G_DBC算法,利用道路网中数据点与查询点之间的位置关系和新的索引结构Vor-R^*-DHash剪枝、过滤数据点,从而得到距离较优集;再利用静态Skyline集不变的性质,预先计算、保存该集合;然后通过所提的新支配关系对距离较优集与静态Skyline集取并集后的集合S进行放松支配;最后利用所提TK_DC算法对经过放松支配后的候选结果集打分,依据数据点得分情况,排序输出Top-k个结果集返回用户群. 理论研究与实验表明,所提方法具有较好的效率与可靠性.

     

一种分布式网络中轮廓推荐的有效方法

当底层数据的容量以及轮廓推荐指令个数增大时,轮廓推荐的时间代价将呈指数级增长,从而严重影响其推荐效率。为此,基于超对等分布式网络(SPA),该文提出预存储w个轮廓快照来高效处理系统中u个轮廓推荐指令的分布式网络轮廓推荐算法(EMSRDN)。EMSRDN算法充分考虑SPA网络的数据存储和通信特性,利用map/reduce分布式计算模型,通过初始快照集启发式构造来快速产生最优w个轮廓快照。理论分析和仿真实验表明,该算法具有有效性和实用性。