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.     

不确定Skyline查询技术研究

当前不确定数据广泛存在于诸如传感器网络、RFID网络、基于位置服务、移动对象管理网上购物和市场监控等各种实际应用中.不确定Skyline查询作为不确定数据管理的一个重要方面,由于其在决策制定、市场分析、环境监控和数据挖掘等方面的重要作用,近年来在数据库和网络计算领域受到广泛关注.首先,概述了各种不确定数据类型上的Skyline查询定义,包括离散、连续概率分布模型以及不完全数据上的Skyline查询定义;其次,分析了不确定Skyline查询的特点,并在此基础上综述了现有的各种不确定数据集上的集中式和分布式Skyline查询方法,重点分析了各种算法的原理和优缺点;再次,介绍了不确定数据流上的Skyline查询定义并综述了各种不确定数据流上的Skyline查询方法;最后,基于最新研究动态指出了未来不确定Skyline查询研究的趋势.     

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.     

垂直分布数据集上的安全Skyline查询算法

Skyline查询是指从多维数据集中筛选出不被其他任何数据点支配的数据点,是一种重要的数据分析方法。近年来,随着隐私保护需求的不断增长,分布式数据集上保护隐私的Skyline查询算法也受到越来越多关注。然而,现有的垂直分布数据集上的Skyline查询方案数据以明文存储,不能实现数据的隐私保护。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,提出了一种抗合谋攻击的多方垂直分布数据集上的Skyline查询协议。理论分析证明了提出协议的正确性和安全性。此外,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。     

RDF数据的Skyline优化查询

为解决海量RDF数据的Skyline查询问题,通过分析现有Skyline查询算法的优缺点,提出一种针对海量RDF数据的查询机制。对RDF数据的存储结构进行分析,根据RDF数据垂直存储结构,设计一种候选Skyline点筛选策略,提前修剪部分非Skyline元组,减少Skyline支配点计算的数据量;在筛选的基础上,给出基于MapReduce的Skyline并行化查询算法。实验结果表明,提前筛选能有效减小查询的数据集,并行化算法能够有效提高查询的效率。     

数据仓库中雪花模式的Skyline-Join查询

Skyline查询能够有效地实现多目标最优化,而数据仓库中的OLAP也是针对多维数据进行分析,因此,针对Skyline查询在数据仓库中的应用,提出了数据仓库中雪花模式的Skyline-Join查询算法.该算法首先将子维表M-Join父维表,然后渐进选择式地对事实表和父维表进行连接.每次连接之前都对事实表进行分组和组内Skyline计算,删除组内非Skyline元组,这样可以减少许多不必要的连接操作,使得查询效率大大提高.通过实验证明,在事实表元组数量逐渐变大和维表个数逐渐增多的情况下,提出的算法比先Join后Skyline计算的naive算法效率上有明显改善.     

数据流上自适应的稀疏Skyline挖掘

Skyline 查询的结果集为数据集中不被其他对象所``支配'的对象的全体. 近年来, 它在在线服务、决策支持和实时监测等领域的良好应用前景, 使其成为数据管理与数据挖掘领域的研究热点. 实际应用中, 用户通常期望快速、渐进地获得 Skyline 计算结果, 而流数据的连续、海量、高维等特性, 使得在确保查询质量损失受控的前提下挖掘稀疏 Skyline 集合成为一个极具价值和挑战性的问题. 本文首先提出一个新颖的概念: 稀疏 Skyline (Sparse-skyline), 它采用一个 Skyline 对象来代表其周围 ε-邻域内的所有 Skyline 对象; 接着, 给出了通过数据维度之间的相关性来自适应调整查询质量的两个在线算法; 最后, 理论分析和实验结果表明, 与现有的 Skyline 挖掘算法相比, 本文提出的方法具有良好的性能和效率, 更适合于数据流应用.     

基于曼哈顿距离的不确定移动对象概率Skyline查询

在众多应用中,由于受到测量仪器精度、更新延迟、网络带宽等限制,不同形式的数据不确定性广泛存在。目前,不确定数据中的信息查询受到数据库研究领域学者的关注,并且为不确定数据寻找高效的分析方法也成为了一个热门课题。本文针对基于曼哈顿距离的不确定移动对象概率Skyline查询问题,提出一个基于曼哈顿距离的概率Skyline模型用于求解不确定移动对象在某时刻是Skyline的概率,并得到一个p-t-Skyline结果集,此集合包含所有在t时刻Skyline概率至少是p的移动对象。在实际应用中,计算大量不确定移动对象的Skyline概率过程繁琐,代价高昂。为提高概率Skyline查询过程的计算效率,本文提出包含“采样-限定-修剪-精炼”4个步骤的解决方案。同时,为进一步减少Skyline运算开销,本文使用一个多维索引结构VCI树以加快数据检索的效率。实验结果表明该解决方案在不同数据规模以及维度的数据集上均具有较高的效率。     

无线传感器网络中基于聚簇结构的Skyline查询方法

现有的基于单服务器的Skyline查询算法已经不能很好地应用于无线传感器网络这类分布式多跳自组织网络中。基于聚簇结构的Skyline查询算法就是针对 这类特定的网络结构而提出的。该算法采用基于聚簇的路由结构,为了减少Skyline查询处理过程中传感器节点的通信开销,挑选具有最大支配力的数据元组作为全局过滤元组来过滤不满足Skyline条件的数据。同时,在Skyline查询处理过程中引入滑动窗口机制,该机制也能有效地降低通信开销。大量的仿真实验结果显示,所提Skyline查询算法在确保能耗的基础上仍然具有很好的性能。     

高效多子空间Skyline查询处理算法

随着Skyline查询应用的增多,子空间Skyline查询成为热点。针对实际应用中用户从多角度审视某一数据集的需求,充分研究了多子空间Skyline查询问题。在分析现有子空间Skyline查询算法解决该问题不足的基础上,提出了子空间立方体群(subspace skycube group,SSG)结构,并给出了基于该结构的同时计算任意多个子空间Skyline查询的MSSC(multiple subspace skycube)算法。该算法采用子空间候选集(subspace candidate sets,SCS),并充分利用了子空间立方体群结构中各子空间Skyline结果间的共享关系;在此基础上,算法采用求和过滤以及最大值过滤等方法,对数据集进行剪枝和过滤,从而进一步提高算法效率。最后,分别用人造数据和真实数据对算法进行实验,并与现有算法进行比较,结果表明MSSC算法可以高效地解决多子空间Skyline查询问题。     

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。     

Efficient mining of skyline objects in subspaces over data streams

Given a set of k-dimensional objects, the skyline query finds the objects that are not dominated by others. In practice, different users may be interested in different dimensions of the data, and issue queries on any subset of k dimensions in stream environments. This paper focuses on supporting concurrent and unpredictable subspace skyline queries over data streams. Simply to compute and store the skyline objects of every subspace in stream environments will incur expensive update cost. To balance the query cost and update cost, we only maintain the full space skyline in this paper. We first propose an efficient maintenance algorithm and several novel pruning techniques. Then, an efficient and scalable two-phase algorithm is proposed to process the skyline queries in different subspaces based on the full space skyline. Furthermore, we present the theoretical analyses and extensive experiments that demonstrate our method is both efficient and effective.     

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 Distributed Skyline Queries for Mobile Applications

In this paper, we consider skyline queries in a mobile and distributed environment, where data objects are distributed in some sites (database servers) which are interconnected through a high-speed wired network, and queries are issued by mobile units (laptop, cell phone, etc.) which access the data objects of database servers by wireless channels. The inherent properties of mobile computing environment such as mobility, limited wireless bandwidth, frequent disconnection, make skyline queries more complicated. We show how to efficiently perform distributed skyline queries in a mobile environment and propose a skyline query processing approach, called efficient distributed skyline based on mobile computing (EDS-MC). In EDS-MC, a distributed skyline query is decomposed into five processing phases and each phase is elaborately designed in order to reduce the network communication, network delay and query response time. We conduct extensive experiments in a simulated mobile database system, and the experimental results demonstrate the superiority of EDS-MC over other skyline query processing techniques on mobile computing.     

Efficient skyline query processing in wireless sensor networks

How to process a skyline query efficiently has received considerable attention in recent years. A skyline query identifies a set of non-dominated data records in a multidimensional dataset. Whereas most previous studies have resolved this problem in a centralized environment, this work considers it in a distributed sensor network environment. An algorithm, known as Skyline Sensor Algorithm (SkySensor), is presented to efficiently retrieve skyline results from a sensor network. A cluster-based architecture is designed in SkySensor to collect all sensor readings. A pruning method is then proposed to progressively sift out the skyline results from the sensor network. SkySensor avoids the need of collecting data from all sensors in the network, which is an extremely expensive action, when searching for the skyline results. The performance study indicates that SkySensor is highly efficient, and significantly outperforms previous methods in processing skyline queries.     

Parallelizing skyline queries over uncertain data streams with sliding window partitioning and grid index

Skyline query processing over uncertain data streams has attracted considerable attention in database community recently, due to its importance in helping users make intelligent decisions over complex data in many real applications. Although lots of recent efforts have been conducted to the skyline computation over data streams in a centralized environment typically with one processor, they cannot be well adapted to the skyline queries over complex uncertain streaming data, due to the computational complexity of the query and the limited processing capability. Furthermore, none of the existing studies on parallel skyline computation can effectively address the skyline query problem over uncertain data streams, as they are all developed to address the problem of parallel skyline queries over static certain data sets. In this paper, we formally define the parallel query problem over uncertain data streams with the sliding window streaming model. Particularly, for the first time, we propose an effective framework, named distributed parallel framework to address the problem based on the sliding window partitioning. Furthermore, we propose an efficient approach (parallel streaming skyline) to further optimize the parallel skyline computation with an optimized streaming item mapping strategy and the grid index. Extensive experiments with real deployment over synthetic and real data are conducted to demonstrate the effectiveness and efficiency of the proposed techniques.     

Efficient processing of top-k dominating queries in distributed environments

Due to the recent massive data generation, preference queries are becoming an increasingly important for users because such queries retrieve only a small number of preferable data objects from a huge multi-dimensional dataset. A top-k dominating query, which retrieves the k data objects dominating the highest number of data objects in a given dataset, is particularly important in supporting multi-criteria decision making because this query can find interesting data objects in an intuitive way exploiting the advantages of top-k and skyline queries. Although efficient algorithms for top-k dominating queries have been studied over centralized databases, there are no studies which deal with top-k dominating queries in distributed environments. The recent data management is becoming increasingly distributed, so it is necessary to support processing of top-k dominating queries in distributed environments. In this paper, we address, for the first time, the challenging problem of processing top-k dominating queries in distributed networks and propose a method for efficient top-k dominating data retrieval, which avoids redundant communication cost and latency. Furthermore, we also propose an approximate version of our proposed method, which further reduces communication cost. Extensive experiments on both synthetic and real data have demonstrated the efficiency and effectiveness of our proposed methods.     

Efficient Processing of Metric Skyline Queries

Skyline query is of great importance in many applications, such as multi-criteria decision making and business planning. In particular, a skyline point is a data object in the database whose attribute vector is not dominated by that of any other objects. Previous methods to retrieve skyline points usually assume static data objects in the database (i.e. their attribute vectors are fixed), whereas several recent work focus on skyline queries with dynamic attributes. In this paper, we propose a novel variant of skyline queries, namely metric skyline, whose dynamic attributes are defined in the metric space (i.e. not limited to the Euclidean space). We illustrate an efficient and effective pruning mechanism to answer metric skyline queries through a metric index. Most importantly, we formalize the query performance of the metric skyline query in terms of the pruning power, by a cost model, in light of which we construct an optimized metric index aiming to maximize the pruning power of metric skyline queries. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed pruning techniques as well as the constructed index in answering metric skyline queries.     

Z-SKY: an efficient skyline query processing framework based on Z-order

Given a set of data points in a multidimensional space, a skyline query retrieves those data points that are not dominated by any other point in the same dataset. Observing that the properties of Z-order space filling curves (or Z-order curves) perfectly match with the dominance relationships among data points in a geometrical data space, we, in this paper, develop and present a novel and efficient processing framework to evaluate skyline queries and their variants, and to support skyline result updates based on Z-order curves. This framework consists of ZBtree, i.e., an index structure to organize a source dataset and skyline candidates, and a suite of algorithms, namely, (1) ZSearch, which processes skyline queries, (2) ZInsert, ZDelete and ZUpdate, which incrementally maintain skyline results in presence of source dataset updates, (3) ZBand, which answers skyband queries, (4) ZRank, which returns top-ranked skyline points, (5) k-ZSearch, which evaluates k-dominant skyline queries, and (6) ZSubspace, which supports skyline queries on a subset of dimensions. While derived upon coherent ideas and concepts, our approaches are shown to outperform the state-of-the-art algorithms that are specialized to address particular skyline problems, especially when a large number of skyline points are resulted, via comprehensive experiments.