基于TPU-tree的Skyline分支定界查询算法(英文)

提出了一种新的限定性skyline查询理念,并给出了高效的处理技术。分支定界方法是当前skyline查询处理效率较高的技术之一,在一种不确定移动对象的索引策略TPU-tree之上,基于分支定界方法提出了B2CPS可限定性skyline查询处理算法。实验结果表明,提出的基于TPU-tree的B2CPS算法可以很大程度地提高限定性skyline查询的效率,在移动对象频繁更新的情况下亦能保持较高的查询性能,因此具有较好的实用价值。     

基于位置范围的道路网skyline查询

随着无线通信和定位技术的发展,道路网skyline查询在基于位置的服务等方面越来越重要。考虑到现今道路网中位置隐私保护和定位设备的精度问题,用户在道路网上的位置通常用一个范围来表示。但是,已有的道路网skyline研究都是基于单一查询点。针对这一问题,研究了一种新的查询——基于位置范围的道路网skyline查询(RNS),提出了一种基于边界点替换的有效查询处理算法。另外,针对已有的道路网skyline查询中复杂的道路网距离计算对查询效率的影响问题,通过计算兴趣点在道路网上的有效skyline路段,将其与道路网信息融合,建立了道路网skyline模型。基于该模型设计了一种能有效支持RNS查询的道路网skyline索引SSR-tree,提出了基于索引的RNS查询处理算法。通过大量实验验证了所提方法的有效性,并比较了基于索引的算法在查询效率和精度上的提高。     

一种使用简化预排序的k-支配Skyline查询算法

近年来,Skyline查询在多目标决策、数据挖掘、数据库可视化等方面得到广泛应用.然而在高维空间环境下,skyline查询因为返回的结果集过大而不能提供有用的信息.因此,学术界提出了七-支配skyline查询的概念.它通过弱化数据点之间的支配关系,使数据点间更容易产生支配关系,从而使结果集的大小保持在一个合适的范围内.现有七-支配skyline查询算法分为建立索引和不建立索引两种类型.其中不建立索引的算法在高维空间,反相关数据和渐近输出等方面表现比较差,而基于索引的算法花费大量时间去建立索引,整体性能都不高.本文提出一种基于简化预排序的七-支配skyline查询算法(SPA),实现用O(n)的时间复杂度对数据进行简化预排序.理论论证和实验数据都显示了SPA算法远比国内外现有的最好算法更加高效.     

一种分布式环境中的二分式多层网格skyline算法

skyline计算在数据挖掘、多标准决策和数据库可视化等领域有着非常重要的作用,这些年已经得到了广泛的关注,以往对于skyline查询的研究大多集中在处理集中的数据集上,即集中式skyline查询,已经得到了很多的研究成果。然而,实际情况是：相关数据几乎分散在几个不同的服务器上,因此在分布式环境中的skyline查询计算需要从各个服务器收集大量的数据;现有的在分布式环境中的skyline查询方法有两个主要问题：一是skyline查询的处理时间较慢;二是在网络中服务器之间传输了很多不必要的重叠数据。提出了一种二分式多层网格法（DMLG）,可以有效地处理在分布式环境中的skyline查询。该方法利用网格的方法,借鉴二分法,最大限度地减少了不必要的重叠数据传输,基于不同的数据集的实验表明,这种方法优于现有的方法。     

无线传感器网络中一种能量高效的skyline查询算法

针对分布式无线传感器网络环境下的skyline查询问题,提出了KSkySenor算法,有效地从传感器网络中获取更有意义的skyline结果;KSkySenor算法对感知数据进行预处理计算每个元组的支配能力,按照支配能力与各维度值之和对其进行排序,设计了一个基于聚簇的结构用于收集所有传感器读数,提出了一个剪枝方法用于渐进的从传感器网中获取skyline结果集;实验中分别改变传感器网络规模大小、数据维度、支配属性个数k,对KSkySensor算法进行测试,实验结果表明KSkySenor算法与先前的skyline查询处理算法相比具有很高效率,减少了无线传感器网络中的数据传输量,延长了网络生命周期。     

有效预处理P2P 网络中的子空间skyline 查询

多维空间的skyline 查询处理是近年来数据库领域的一个研究重点和热点.Vlachou 等人首次考虑如何在P2P 网络中有效进行子空间上的skyline 查询,并提出“扩展skyline 集合”的概念来减少预处理时的网络传输量.然而实验评估表明,扩展skyline 集合只能有限地减少子空间skyline 查询预处理的数据传输量.基于此,提出一种缩减处理时数据传输量的有效方法TPAOSS(three-phase algorithm for optimizing skyline scalar).TPAOSS 算法根据全空间skyline 集合与子空间skyline 集合间的语义关系分3 个阶段来传输必要的数据,其中第1 阶段发送全空间skyline 对象;第2 阶段接收种子skyline 对象;而第3 阶段基于Bloom filter 技术发送种子skyline 对象在子空间上的重复对象.为了降低第2 阶段的数据传输量,给出两种接收种子skyline 对象的有效策略.理论分析和实验评估结果表明,所给出的算法具有有效性和实用性.     

双流模式下高吞吐量移动对象范围查询算法

随着位置感知移动设备的出现及通信技术和GPS系统的不断发展,基于位置的查询在数据库领域得到了广泛的关注.研究了基于快照的空间范围查询,即,查询在某个时间段位于某个查询范围内的移动对象.范围查询是其他空间查询的基础,例如KNN查询和反KNN查询等,很容易在范围查询的基础上得到.国内外的研究者针对移动对象的范围查询问题提出了一系列的算法,然而这些算法要么关注于解决移动对象的快速更新问题,要么关注于解决范围查询的快速处理问题.在大数据的背景下,查询和更新大量涌入时,不仅要求查询算法有较快的响应速度,还要求它们能够实现较高的吞吐量,但已有算法不能很好地解决高吞吐量的问题.针对移动对象更新数据流和查询数据流,提出一种基于内存的高吞吐量移动对象范围查询算法——双向流连接(DSJ)算法.双向流连接算法采用基于快照的模式,通过在每个快照中重新构建索引的方式,以避免复杂的索引维护操作,充分发挥了硬件的性能;通过每次执行一组查询的方式,增加了数据的局部性,提高了算法的效率;在执行过程中,通过使用SIMD技术以加速查询处理过程.基于以上几点,双向流连接算法能够确保整个系统具有很高的吞吐量.在基于德国路网生成的数据集上对算法进行了测试,实验结果表明,双向流连接算法具有很好的性能表现.     

基于垂直划分的隐私保护skyline查询

Skyline查询是一种重要的数据分析方法,在推荐系统中有着广泛的应用。近年来,随着隐私保护需求的不断增长,分布式数据集上的隐私保护skyline查询问题受到越来越多的关注。然而,现有的分布式数据集上的隐私保护skyline查询方案大多只适用于水平分布数据集,不能满足垂直分布数据集上的skyline查询需求。为此,深入研究了垂直分布式数据集上保护隐私的skyline查询问题,提出了一种基于保序加密的垂直分布数据集上的隐私保护skyline查询算法,可以在保护数据隐私的同时,有效支持skyline查询过程。理论分析证明了提出协议的正确性和安全性,并通过理论分析和模拟实验对协议运行效率进行了评估,结果显示新方案具有较高的运行效率。     

基于Flink的k-支配skyline体并行求解算法

k-支配skyline算法弱化了数据点之间的支配关系,更适合高维数据。k-支配skyline体适应于多名用户使用k-支配skyline算法查询,而现有的求解算法在时间效率和代码扩展性方面都有待提高。因此,提出了面向多用户的k-支配skyline体求解优化算法MKSSOA,该算法对每名用户的候选集和中间集分别进行存储,同时在k-支配检查过程中利用2集合中数据点出现的先后次序将候选集中的非k-支配skyline点存储到对应用户的中间集中,以便下一名用户筛选使用,这样可以减少数据点之间的比较次数,避免重复计算,从而提升查询效率。同时,提出了面向多用户的k-支配skyline体并行求解算法MKSPSA,通过Apache Flink并行处理框架有效减少了数据点的比较时间。理论研究和实验结果显示,提出的算法具有较高的效率,能很好地处理多用户k-支配skyline问题。     

动态数据集环境下的强邻近对查询

数据集中的强邻近对查询在空间数据挖掘、大数据处理、空间数据库、地理信息系统、数据的相似分析和推理等方面具有重要的作用。已有的数据查询方法无法有效处理动态数据集中的强邻近对查询问题,针对动态数据集中的强邻近对查询的特点和复杂性,基于Voronoi图和R树空间索引结构提出了处理初始数据环境下的双数据集中的强邻近对查询算法VR＿SNP 。针对分布区域不规则且数据点分布密度差异较大的情况利用Voronoi图进行计算查询,反之,则利用R树进行查询。通过对初始强邻近对集和候选邻近对集进行二次判断计算,筛选出有效结果,给出了数据集动态增加和动态减少环境下的强邻近对查询算法VR＿SNP＿DA和算法VR＿SNP＿DE 。进一步提出了移动点位置变化情况下的强邻近对查询算法VR＿SNP＿DL 。理论研究和实验比较表明在数据集的数据量、新增点集和删除点集的规模较大、移动点的位置变化次数较多等情况下,所提出的算法具有较为明显的查询优势。     

基于位置的路网Skyline查询处理研究

基于位置的路网Skyline查询可根据用户的需求及用户所处的位置,从大量数据中快速返回给用户期望的数据,但已有的道路网络技术需要计算大量的路网距离及数据点间支配关系的运算,导致查询效率较低。提出一种基于路网数据点的倒排索引查询算法DSR。通过计算少量数据点的路网距离求得最终结果,减小路网距离计算的代价,从而加快数据点间支配关系的判定,提升查询效率。在此基础上,在数据点更新情况下给出算法的动态维护,仅通过维护少量数据,DSR即可以快速地计算出Skyline集合。实验结果表明,与SSI、BSS等算法相比,该算法具有较高的查询效率,且时间性能明显提升。     

Optimized skyline queries on road networks using nearest neighbors

Skyline queries are used with data extensive applications, such as mobile location-based services, to support multi-criteria decision-making and to prune the data space by returning the most “interesting” data points. Most interesting data points are the points, which are not dominated by any other point. Spatial network skyline query is a subset of the skyline query problem where data points are nodes in a road network and the attributes of the data points are network distance relative to a set of query points. Spatial network skyline query’s problem is the need to calculate the attributes with an expensive distance calculation operation. Previous works (Deng et al. Proceedings of the 23th international conference on data engineering, 796–805, 2007), Sharifzadeh et al. Proceedings of the 32nd international conference on very large databases, 751–762, 2009) that addressed this problem involved extensive network distance calculation between the query points and data points. A new algorithm that requires a remarkably less number of network distance calculations is proposed in this work. Our approach uses a progressive nearest neighbor algorithm to minimize the set of candidates then evaluates those candidates by only comparing them to a subset of discovered skyline points. Experiments showed the effectiveness of our algorithm compared to previous works.     

Efficient skyline query processing in SpatialHadoop

This paper studies the problem of computing the skyline of a vast-sized spatial dataset in SpatialHadoop, an extension of Hadoop that supports spatial operations efficiently. The problem is particularly interesting due to advent of Big Spatial Data that are generated by modern applications run on mobile devices, and also because of the importance of the skyline operator for decision-making and supporting business intelligence. To this end, we present a scalable and efficient framework for skyline query processing that operates on top of SpatialHadoop, and can be parameterized by individual techniques related to filtering of candidate points as well as merging of local skyline sets. Then, we introduce two novel algorithms that follow the pattern of the framework and boost the performance of skyline query processing. Our algorithms employ specific optimizations based on effective filtering and efficient merging, the combination of which is responsible for improved efficiency. We compare our solution against the state-of-the-art skyline algorithm in SpatialHadoop. The results show that our techniques are more efficient and outperform the competitor significantly, especially in the case of large skyline output size.     

Continuous Probabilistic Subspace Skyline Query Processing Using Grid Projections

As an important type of multidimensional preference query, the skyline query can find a superset of optimal results when there is no given linear function to combine values for all attributes of interest. Its processing has been extensively investigated in the past. While most skyline query processing algorithms are designed based on the assumption that query processing is done for all attributes in a static dataset with deterministic attribute values, some advanced work has been done recently to remove part of such a strong assumption in order to process skyline queries for real-life applications, namely, to deal with data with multi-valued attributes （known as data uncertainty）, to support skyline queries in a subspace which is a subset of attributes selected by the user, and to support continuous queries on streaming data. Naturally, there are many application scenarios where these three complex issues must be considered together. In this paper, we tackle the problem of probabilistic subspace skyline query processing over sliding windows on uncertain data streams. That is, to retrieve all objects from the most recent window of streaming data in a user-selected subspace with a skyline probability no smaller than a given threshold. Based on the subtle relationship between the full space and an arbitrary subspace, a novel approach using a regular grid indexing structure is developed for this problem. An extensive empirical study under various settings is conducted to show the effectiveness and efficiency of our PSS algorithm.     

Efficient k-dominant skyline query over incomplete data using MapReduce

Skyline queries are extensively incorporated in various real-life applications by filtering uninteresting data objects. Sometimes, a skyline query may return so many results because it cannot control the retrieval conditions especially for highdimensional datasets. As an extension of skyline query, the kdominant skyline query reduces the control of the dimension by controlling the value of the parameter kto achieve the purpose of reducing the retrieval objects. In addition, with the continuous promotion of Bigdata applications, the data we acquired may not have the entire content that people wanted for some practically reasons of delivery failure, no power of battery, accidental loss, so that the data might be incomplete with missing values in some attributes. Obviously, the k-dominant skyline query algorithms of incomplete data depend on the user definition in some degree and the results cannot be shared. Meanwhile, the existing algorithms are unsuitable for directly used to the incomplete big data. Based on the above situations, this paper mainly studies k-dominant skyline query problem over incomplete dataset and combines this problem with the distributed structure like MapReduce environment. First, we propose an index structure over incomplete data, named incomplete data index based on dominate hierarchical tree (ID-DHT). Applying the bucket strategy, the incomplete data is divided into different buckets according to the dimensions of missing attributes. Second, we also put forward query algorithm for incomplete data in MapReduce environment, named MapReduce incomplete data based on dominant hierarchical tree algorithm (MR-ID-DHTA). The data in the bucket is allocated to the subspace according to the dominant condition by Map function. Reduce function controls the data according to the key value and returns the k-dominant skyline query result. The effective experiments demonstrate the validity and usability of our index structure and the algorithm.     

Skyline查询处理数据立方体代数

维空间的Skyline查询处理技术是近年来数据库技术领域的一个研究重点和热点.目前所有的研究工作都是直接在原始数据表上执行关系查询代数操作来获得最终的结果集,然而,随着原始数据表的数据量和维目标个数的增大,这些研究工作将不再适用.基于此,首次研究Skyline集合上的查询代数操作,使得Skyline查询处理的输入数据来自于小规模的Skyline结果集,而非海量的原始数据表.并且,首次给出一个集成多维对象集合和该对象集合上的Skyline结果集的形式化模型,该模型适合目前Skyline查询计算的应用,并在该模型的实例上研究Skyline集合的查询代数操作.同时,给出查询代数体系的代价评估模型.实验表明,给出的数据模型和查询代数体系具有有效性和实用性.     

Effective Data Placement for Wireless Broadcast

This paper investigates how to place data objects on air for wireless broadcast such that mobile clients can access the data in short latency. We first define and analyze the problem of wireless data placement, and also propose a measure, named Query Distance (QD), which represents the coherence degree of data set accessed by a query. We show that the problem is NP-complete, and then propose an effective data placement method that constructs the broadcast schedule by appending each query's data set in greedy way. We show through performance experiments that the proposed method reduces the access time of mobile query.     

基于高斯模型的不确定数据流Skyline查询方法

由于数据的动态性及不确定性等特征,使得不确定数据流上Skyline查询研究面临挑战.不确定对象一般采用多元概率密度函数(PDF)表示,现有的不确定数据流Skyline查询方法均采用离散型随机变量建模.然而不确定数据流中的对象可能是连续变化的,离散模型对连续性随机变量难以适用.针对连续PDF建模的不确定数据流Skyline查询进行了研究,提出了基于高斯模型的不确定数据流Skyline查询方法(SGMU),该方法包含2个过程:1)动态高斯建模算法(DGM):对滑动窗口采样并建立高斯模型,将原始的数据流转化为不确定对象PDF的参数流;2)提出了基于高斯树的查询算法(GTS)以建立空间索引结构和执行Skyline查询.实验结果表明,SGMU算法不仅能够对连续型不确定对象进行有效建模以辅助Skyline查询,而且能够有效地减少查询对象个数,提高Skyline查询效率.     

Research on K Maximum Dominant Skyline and E-GA Algorithm Based on Data Stream Environment

With the continuous development of database technology, the data volume that can be stored and processed by the database is increasing. How to dig out information that people are interested in from the massive data is one of the important issues in the field of database research. This article starts from the user demand analysis, and makes an in-depth study of various query expansion problems of skylines. Then, according to different application scenarios, this paper proposes efficient and targeted solutions to effectively meet the actual needs of people. Based on k- representative skyline query problem in the data stream environment, a k-representative skyline selection standard k-LDS is presented which is applicable for data stream environment. k-LDS hopes to select the skyline subset with the largest dominant area (containing k skyline tuples only) as k- representative skyline set in data stream. And for the 3-dimensionalal and multidimensional k-LDS problems, this paper also proposes the approximation algorithm, namely GA algorithm. Finally, through the experiment, it is proved that k-LDS is more suitable for the data stream environment, and the algorithm proposed can effectively solve k-LD problems under the data stream environment.