无锡传统街区天际线景观特征研究

对传统街区艺术魅力的着墨集中于建筑、空间、院落、街区、城镇等解析,却鲜有对天际线的研究.在对无锡荣巷、惠山直街、周山浜、上下河塘等几处传统街区调研后,该文试从屋顶、马头墙、老虎窗、屋脊等构筑的两次轮廓线入手,分析无锡老街天际线的景观特征.     

多因素时间序列Skyline的快速查询算法

在给出多因素时间序列Skyline(MFTS)形式化定义的基础上,提出一种MFTS快速查询算法,获得并存储各时间点上的多因素值,计算每个最小时间区间的准MFTS,从而得到指定区间的MFTS。实验分析结果表明,在时间序列重合较少的情况下,该算法具有较好的查询效率。     

基于Skyline的兰州削山造地监测三维系统设计与实现

基于三维技术的地理信息系统能以立体、直观、全方位的角度进行可视化表达和空间分析。文中介绍了基于Skyline的兰州北山区域削山造地监测三维系统的建设背景和方法,阐述了系统的开发路线与技术架构、数据库以及功能模块的设计,简要分析了数据自动渲染、多球联动比对分析关键技术,并对削山造地活动的三维监管模式进行了探讨。     

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.     

Skyframe: a framework for skyline query processing in peer-to-peer systems

This paper looks at the processing of skyline queries on peer-to-peer (P2P) networks. We propose Skyframe, a framework for efficient skyline query processing in P2P systems, which addresses the challenges of quick response time, low network communication cost and query load balancing among peers. Skyframe consists of two querying methods: one is optimized for network communication while the other focuses on query response time. These methods are different in the way in which the query search space is defined. In particular, the first method uses a high dominating point that has a large dominating region to prune the search space to achieve a low cost in network communication. On the other hand, the second method relaxes the search space in order to allow parallel query processing to speed up query response. Skyframe achieves query load balancing by both query load conscious data space splitting/merging during the join/departure of nodes and dynamic load migration. We further show how to apply Skyframe to both the P2P systems supporting multi-dimensional indexing and the P2P systems supporting single-dimensional indexing. Finally, we have conducted extensive experiments on both real and synthetic data sets over two existing P2P systems: CAN (Ratnasamy in A scalable content-addressable network. In: Proceedings of SIGCOMM Conference, pp. 161–172, 2001) and BATON (Jagadish et al. in A balanced tree structure for peer-to-peer networks. In: Proceedings of VLDB Conference, pp. 661–672, 2005) to evaluate the effectiveness and scalability of Skyframe.     

Efficient continuous skyline computation

In a number of emerging streaming applications, the data values that are produced have an associated time interval for which they are valid. A useful computation over such streaming data is to produce a continuous and valid skyline summary. Previous work on skyline algorithms have only focused on evaluating skylines over static data sets, and there are no known algorithms for skyline computation in the continuous setting. In this paper, we introduce the continuous time-interval skyline operator, which continuously computes the current skyline over a data stream. We present a new algorithm called LookOut for evaluating such queries efficiently, and empirically demonstrate the scalability of this algorithm. In addition, we also examine the effect of the underlying spatial index structure when evaluating skylines. Whereas previous work on skyline computations have only considered using the R^∗-tree index structure, we show that for skyline computations using an underlying quadtree has significant performance benefits over an R^∗-tree index.     

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.     

基于离散粒子群算法的动态Web服务组合

随着互联网中Web服务数量急剧增加,如何从大量候选服务中快速、动态地选择出满足用户QoS需求的服务组合是亟待解决的关键问题.提出一种基于离散粒子群智能优化算法的DDPSO算法,以解决动态Web服务组合问题.首先引入Skyline技术来剔除冗余候选服务,以降低服务选择时空开销.其次针对PSO易陷入早熟收敛状态,使用Trimming Operators保证粒子群多样性,增强全局搜索能力.最后通过真实数据集和随机数据集的大量仿真实验验证了本算法的可行性和有效性.     

无线传感器网络中一种近似Skyline 查询处理算法

由于无线传感器网络的能源有限,且在许多应用中Skyline 查询的部分结果即可满足用户需求,提出了一种近似Skyline 查询处理算法,在满足用户查询需求的前提下最大化地节省能量.该算法仅需无线传感器网络中的部分传感器节点回传其感知数据即可计算出Skyline 查询的一个近似结果集.由于该算法在处理查询时,每个传感器节点只需考察自身数据信息即可决定是否回传其感知数据,而无须与其他传感器节点的感知数据进行比较,因此可以避免大量的网内通信开销,从而节省网络能源.模拟环境下的大量实验结果表明,该算法可以根据用户的应用需求,节能地处理传感器网络中的近似skyline 查询.     

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.