概率数据上基于EMD距离的并行Top-k相似性连接算法

以无线传感器网络为代表的新型数据应用和以图像处理为基础的传统数据应用都产生了大规模的概率数据.在概率数据的管理中,Top-k相似性连接操作返回最相似的k 对概率数据,具有重要应用价值.直方图是最常用的概率数据模型之一,而EMD（Earth Mover’s Distance）距离因其较强的鲁棒性可更准确地量化直方图概率数据之间的相似性.然而EMD距离的计算却具有三次方的时间复杂度,给基于EMD距离的Top-k 相似性连接带来巨大挑战.基于流行的MapReduce并行处理框架,利用EMD距离对偶线性规划问题的优良特性,提出了两种大规模概率数据上基于EMD距离的Top-k相似性连接算法.首先提出基于块嵌套循环连接思想的基本解决方法,命名为Top-k BNLJ算法.进而改进数据划分策略,提出基于数据局部性进行数据划分的Top-k DLPJ 算法,有效降低了MapReduce作业执行过程中的数据传输量.使用大规模真实数据集对两种算法进行评估,证实了本文提出的Top-k DLPJ算法的高效性和处理大规模数据集时的良好扩展性.     

带间隔约束的Top-k对比序列模式挖掘

对比序列模式能够表达序列数据集合间的差异,在商品推荐、用户行为分析和电力供应预测等领域有广泛的应用.已有的对比序列模式挖掘算法需要用户设定正例支持度阈值和负例支持度阈值.在不具备足够先验知识的情况下,用户难以设定恰当的支持度阈值,从而可能错失一些对比显著的模式.为此,提出了带间隔约束的top-k对比序列模式挖掘算法kDSP-Miner(top-k distinguishing sequential patterns with gap constraint miner).kDSP-Miner中用户只需设置期望发现的对比最显著的模式个数,从而避免了直接设置对比支持度阈值.相应地,挖掘算法更容易使用,并且结果更易于解释.同时,为了提高算法执行效率,设计了若干剪枝策略和启发策略.进一步设计了kDSP-Miner的多线程版本,以提高其对高维序列元素情况的处理能力.通过在真实世界数据集上的详实实验,验证了算法的有效性和执行效率.     

用户群体满意度最大化的Top-k在线服务评价

考虑用户评价准则不一致的在线服务评价通常以服务的完整排序作为评价结果,而不是选择出使用户群体满意度最大的Top-k在线服务集合,使评价结果难以满足Top-k在线服务评价场景的合理性和公平性需求.为此,提出了一种用户群体满意度最大化的Top-k在线服务评价方法.该方法首先定义用户群体满意度指标,以衡量选择的k个在线服务的合理性;其次,考虑用户评价准则不一致及用户偏好信息不完整的情况,采用Borda规则将用户对在线服务的偏好关系构造为用户-服务满意度矩阵;然后借鉴Monroe比例代表思想,将Top-k在线服务评价问题建模为寻找最大化用户群体满意度的在线服务集合的优化问题;最后采用贪心算法对该优化问题进行求解,将得到的在线服务集合作为Top-k评价结果.通过理论分析和实验验证了该方法的合理性和有效性.理论分析表明,该方法满足Top-k在线服务评价所需的比例代表性和公平性.同时,实验结果也表明,该方法能够在合理的时间内获得接近用户群体满意度理想上界的评价结果,可以有效地辅助用户群体做出正确的服务选择决策.另外,该方法还可以在用户偏好不完整的情况下实现Top-k在线服务评价.     

一种云环境下的大数据Top-K查询方法

Top-K查询在搜索引擎、电子商务等领域有着广泛的应用.Top-K查询从海量数据中返回最符合用户需求的前K个结果,主要目的是消除信息过载带来的负面影响.大数据背景下的Top-K查询,给数据管理和分析等方面带来新的挑战.结合MapReduce的特点,从数据划分、数据筛选等方面对云环境下的大数据Top-K查询问题进行深入研究.实验结果表明,该方法具有良好的性能和扩展性.     

面向移动对象连续k近邻查询的双层索引结构

移动对象连续k近邻（CKNN）查询是指给定一个连续移动的对象集合,对于任意一个k近邻查询q,实时计算查询q的k近邻并在查询有效时间内对查询结果进行实时更新.现实生活中,交通出行、社交网络、电子商务等领域许多基于位置的应用服务都涉及移动对象连续k近邻查询这一基础问题.已有研究工作解决连续k近邻查询问题时,大多需要通过多次迭代确定一个包含k近邻的查询范围,而每次迭代需要根据移动对象的位置计算当前查询范围内移动对象的数量,整个迭代过程的计算代价占查询代价的很大部分.为此,提出了一种基于网络索引和混合高斯函数移动对象分布密度的双重索引结构（grid GMM index,GGI）,并设计了移动对象连续k近邻增量查询算法（incremental search for continuous k nearest neighbors,IS-CKNN）.GGI索引结构的底层采用网格索引对海量移动对象进行维护,上层构建混合高斯模型模拟移动对象在二维空间中的分布.对于给定的k近邻查询q,IS-CKNN算法能够基于混合高斯模型直接确定一个包含q的k近邻的查询区域,减少了已有算法求解该区域的多次迭代过程;当移动对象和查询q位置发生变化时,进一步提出一种高效的增量查询策略,能够最大限度地利用已有查询结果减少当前查询的计算量.最后,在滴滴成都网约车数据集以及两个模拟数据集上进行大量实验,充分验证了算法的性能.     

基于图压缩的k可达查询处理

研究了基于图压缩的k可达查询处理,提出了一种支持k可达查询的图压缩算法k-RPC及无需解压缩的查询处理算法,k-RPC算法在所有基于等价类的支持k-reach查询的图压缩算法中是最优的.由于k-RPC算法是基于严格的等价关系,因此进一步又提出了线性时间的近似图压缩算法k-GRPC.k-GRPC算法允许从原始图中删除部分边,然后使用k-RPC获得更好的压缩比.提出了线性时间的无需解压缩的查询处理算法.真实数据上的实验结果表明,对于稀疏的原始图,两种压缩算法的压缩比分别可以达到45%,对于稠密的原始图,两种压缩算法的压缩比分别可以达到75%和67%;与在原始图上直接进行查询处理相比,两种基于压缩图的查询处理算法效率更好,在稀疏图上的查询效率可以提高2.5倍.     

改进k值自动获取VDBSCAN聚类算法

针对DBSCAN聚类算法不能对变密度分布数据集进行有效聚类,VDBSCAN算法借助k-dist图来自动获取各个密度层次的数据对象的邻域半径,解决了具有不同密度层次分布数据集的聚类问题. k-VDBSCAN算法通过对k值的自动获取,减小了VDBSCAN中参数k对最终聚类结果的影响. 针对k值的自动获取,在原有的k-VDBSCAN聚类算法基础上,依据数据集本身,利用数据对象间距离的特征,提出了一种k值改进自动获取聚类算法. 理论分析与实验结果表明,新的改进算法能够有效的自动获得参数k的值,并且在聚类结果、时间效率方面都有明显的提高.     

一种对时空信息的kNN查询处理方法

互联网上每天都会产生大量的带地理位置标签和时间标签的信息,比如微博、新闻、团购等等,如何在众多的信息中找到在时间和空间地理位置上都满足用户查询需求的信息十分重要.针对这一需求,提出了一种对地理位置和时间信息的k近邻查询（ST-kNN查询）处理方法.首先,利用时空相似度对数据对象的地理位置变量和时间变量进行映射变换,将数据对象映射到新的三维空间中,用三维空间中两点之间的距离相似度来近似代替两个对象之间实际的时空相似度;然后,针对这个三维空间设计了一种ST-Rtree（spatial temporal rtree）索引,该索引综合了空间因素和时间因素,保证在查询时每个对象至多遍历1次;最后,在该索引的基础上提出了一种精确的k近邻查询算法,并通过一次计算确定查询结果范围,从而找到前k个结果,保证了查询的高效性.基于大量数据集的实验,证明了该查询处理方法的高效性.     

基于邻域k-核的社区模型与查询算法

现实生活中的网络通常存在社区结构,社区查询是图数据挖掘的基本任务.现有研究工作提出了多种模型来识别网络中的社区,如基于k-核的模型和基于k-truss的模型.然而,这些模型通常只限制社区内节点或边的邻居数量,忽略了邻居之间的关系,即节点的邻域结构,从而导致社区内节点的局部稠密性较低.针对这一问题,本文将节点的邻域结构信息融入k-核稠密子图中,提出一种新的基于邻域连通k-核的社区模型,并定义了社区的稠密度.基于这一新模型,研究了最稠密单社区搜索问题,即返回包含查询节点集且具有最高稠密度的社区.在现实生活图数据中,一组查询节点可能会分布在多个不相交的社区中.为此,本文进一步研究了基于稠密度阈值的多社区搜索问题,即返回包含查询节点集的多个社区,且每个社区的稠密度不低于用户指定的阈值.针对最稠密单社区搜索和基于稠密度阈值的多社区搜索问题,首先定义了边稠密度的概念,并提出了基于边稠密度的基线算法.为了提高搜索效率,设计了索引树和改进索引树结构,能够支持在多项式时间内返回查询结果.通过与基线算法在多组数据集上的对比,验证了基于邻域连通k-核的社区模型的有效性和所提出查询算法的效率.     

k-Median近似计算复杂度与局部搜索近似算法分析

k-Median问题的近似算法研究一直是计算机科学工作者关注的焦点,现有研究结果大多是关于欧式空间和Metric空间的,一般距离空间k-Median的结果多年来一直未见.考虑一般距离空间k-Median问题,设d_max/d_min表示k-Median实例中与客户点邻接的最长边长比最短边长的最大者.首先证明d_max/d_min≤ω+ε的k-Median问题不存在近似度小于1+ω-1/e的多项式时间近似算法,除非,由此推出Metric k-Median问题不可近似到1+2/e,除非NP(∈)DTME(N^{O(log logn)}).然后给出k-Median问题的一个局部搜索算法,分析表明,若有d_max/d_min≤ω,则算法的近似度为1+ω-1/2.该结果亦适用于Metric k-Median,ω≤5时,局部搜索算法求解Metric k-Median的近似度为3,好于现有结果3+2/P.通过计算机实验,进一步研究了k-Median局部搜索求解算法的实际计算效果和该算法的改进方法.     

Supporting top-<Emphasis Type="Italic">k</Emphasis><Emphasis Type="Italic">join</Emphasis> queries in relational databases

Ranking queries, also known as top-k queries, produce results that are ordered on some computed score. Typically, these queries involve joins, where users are usually interested only in the top-k join results. Top-k queries are dominant in many emerging applications, e.g., multimedia retrieval by content, Web databases, data mining, middlewares, and most information retrieval applications. Current relational query processors do not handle ranking queries efficiently, especially when joins are involved. In this paper, we address supporting top-k join queries in relational query processors. We introduce a new rank-join algorithm that makes use of the individual orders of its inputs to produce join results ordered on a user-specified scoring function. The idea is to rank the join results progressively during the join operation. We introduce two physical query operators based on variants of ripple join that implement the rank-join algorithm. The operators are nonblocking and can be integrated into pipelined execution plans. We also propose an efficient heuristic designed to optimize a top-k join query by choosing the best join order. We address several practical issues and optimization heuristics to integrate the new join operators in practical query processors. We implement the new operators inside a prototype database engine based on PREDATOR. The experimental evaluation of our approach compares recent algorithms for joining ranked inputs and shows superior performance.Received: 23 December 2003, Accepted: 31 March 2004, Published online: 12 August 2004Edited by: S. AbiteboulExtended version of the paper published in the Proceedings of the 29th International Conference on Very Large Databases, VLDB 2003, Berlin, Germany, pp 754-765     

A top-k spatial join querying processing algorithm based on spark

Aiming at the problem of top-k spatial join query processing in cloud computing systems, a Spark-based top-k spatial join (STKSJ) query processing algorithm is proposed. In this algorithm, the whole data space is divided into grid cells of the same size by a grid partitioning method, and each spatial object in one data set is projected into a grid cell. The Minimum Bounding Rectangle (MBR) of all spatial objects in each grid cell is computed. The spatial objects overlapping with these MBRs in another spatial data set are replicated to the corresponding grid cells, thereby filtering out spatial objects for which there are no join results, thus reducing the cost of subsequent spatial join processing. An improved plane sweeping algorithm is also proposed that speeds up the scanning mode and applies threshold filtering, thus greatly reducing the communication and computation costs of intermediate join results in subsequent top-k aggregation operations. Experimental results on synthetic and real data sets show that the proposed algorithm has clear advantages, and better performance than existing top-k spatial join query processing algorithms.     

The <Emphasis Type="Italic">k</Emphasis>-Nearest Neighbour Join: Turbo Charging the KDD Process

The similarity join has become an important database primitive for supporting similarity searches and data mining. A similarity join combines two sets of complex objects such that the result contains all pairs of similar objects. Two types of the similarity join are well-known, the distance range join, in which the user defines a distance threshold for the join, and the closest pair query or k-distance join, which retrieves the k most similar pairs. In this paper, we propose an important, third similarity join operation called the k-nearest neighbour join, which combines each point of one point set with its k nearest neighbours in the other set. We discover that many standard algorithms of Knowledge Discovery in Databases (KDD) such as k-means and k-medoid clustering, nearest neighbour classification, data cleansing, postprocessing of sampling-based data mining, etc. can be implemented on top of the k-nn join operation to achieve performance improvements without affecting the quality of the result of these algorithms. We propose a new algorithm to compute the k-nearest neighbour join using the multipage index (MuX), a specialised index structure for the similarity join. To reduce both CPU and I/O costs, we develop optimal loading and processing strategies.     

一种避免数据偏斜的动态Hash连接方法

本文提出了一种新的动态Hash连接方法──DHJ（dynamichash　join），以解决并行数据库连接操作中的数据偏斜现象.为避免目前某些算法提出的预处理中隐含的高额费用，该方法在划分阶段通过增添附加桶的方法来平衡输出，然后依据计算确认哪些附加桶被映射到处理器上并确定处理器分配，在最后阶段完成连接.本文最后给出了该算法的性能分析.     

Anytime measures for top-k algorithms on exact and fuzzy data sets

Top-k queries on large multi-attribute data sets are fundamental operations in information retrieval and ranking applications. In this article, we initiate research on the anytime behavior of top-k algorithms on exact and fuzzy data. In particular, given specific top-k algorithms (TA and TA-Sorted) we are interested in studying their progress toward identification of the correct result at any point during the algorithms’ execution. We adopt a probabilistic approach where we seek to report at any point of operation of the algorithm the confidence that the top-k result has been identified. Such a functionality can be a valuable asset when one is interested in reducing the runtime cost of top-k computations. We present a thorough experimental evaluation to validate our techniques using both synthetic and real data sets.     

Distributed top-<Emphasis Type="Italic">k</Emphasis> aggregation queries at large

Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network.     

Fast similarity join for multi-dimensional data

The efficient processing of multidimensional similarity joins is important for a large class of applications. The dimensionality of the data for these applications ranges from low to high. Most existing methods have focused on the execution of high-dimensional joins over large amounts of disk-based data. The increasing sizes of main memory available on current computers, and the need for efficient processing of spatial joins suggest that spatial joins for a large class of problems can be processed in main memory. In this paper, we develop two new in-memory spatial join algorithms, the Grid-join and EGO*-join, and study their performance. Through evaluation, we explore the domain of applicability of each approach and provide recommendations for the choice of a join algorithm depending upon the dimensionality of the data as well as the expected selectivity of the join. We show that the two new proposed join techniques substantially outperform the state-of-the-art join algorithm, the EGO-join.     

Extracting k most important groups from data efficiently

We study an important data analysis operator, which extracts the k most important groups from data (i.e., the k groups with the highest aggregate values). In a data warehousing context, an example of the above query is “find the 10 combinations of product-type and month with the largest sum of sales”. The problem is challenging as the potential number of groups can be much larger than the memory capacity. We propose on-demand methods for efficient top-k groups processing, under limited memory size. In particular, we design top-k groups retrieval techniques for three representative scenarios as follows. For the scenario with data physically ordered by measure, we propose the write-optimized multi-pass sorted access algorithm (WMSA), that exploits available memory for efficient top-k groups computation. Regarding the scenario with unordered data, we develop the recursive hash algorithm (RHA), which applies hashing with early aggregation, coupled with branch-and-bound techniques and derivation heuristics for tight score bounds of hash partitions. Next, we design the clustered groups algorithm (CGA), which accelerates top-k groups processing for the case where data is clustered by a subset of group-by attributes. Extensive experiments with real and synthetic datasets demonstrate the applicability and efficiency of the proposed algorithms.