基于编码频繁模式树的序列模式挖掘算法

提出了同时适用于一维和多维序列数据的统一存储结构——编码频繁模式树(CFP-tree),并通过渐进的前缀序列搜索方式来发现频繁序列模式,避免了在挖掘过程中递归地产生大量的中间子序列。实验证明,该算法在大规模数据的处理上比现有序列模式挖掘算法有更好的性能。     

Order statistics-based parametric classification for multi-dimensional distributions

Traditionally, in the field of Pattern Recognition (PR), the moments of the class-conditional densities of the respective classes have been used to perform classification. However, the use of phenomena that utilized the properties of the Order Statistics (OS) were not reported. Recently, in [10], [8], we proposed a new paradigm named CMOS, Classification by the Moments of Order Statistics, which specifically used these quantifiers. It is fascinating that CMOS is essentially “anti”-Bayesian in its nature because the classification is performed in a counter-intuitive manner, i.e., by comparing the testing sample to a few samples distant from the mean, as opposed to the Bayesian approach in which the task is based on the central points of the distributions. In our initial works, we proposed the foundational theory of CMOS for the uni-dimensional Uniform and some other distributions. These results were extended for various symmetric and asymmetric uni-dimensional distributions within the exponential family in [8]. In this paper, we generalize these results for multi-dimensional distributions. The multi-dimensional generalization is particularly non-trivial because there is no well-established method for achieving the ordering of multi-dimensional data specified in terms of its uni-dimensional components. The strategy is analogous to a Naïve-Bayes' approach, although it really is of an anti-Naïve-Bayes' paradigm. We provide here the analytical and experimental results for the 2-dimensional Uniform, Doubly Exponential and Gaussian distributions, and also clearly specify the way by which one should extend the results for higher dimensions. The analogous results for the other distributions in the exponential family, which were discussed in [10], [8] are alluded to, but omitted to avoid repetition.     

Automated exploration of datapath and unrolling factor during power–performance tradeoff in architectural synthesis using multi-dimensional PSO algorithm

A novel algorithm for automated simultaneous exploration of datapath and Unrolling Factor (UF) during power–performance tradeoff in High Level Synthesis (HLS) using multi-dimensional particle swarm optimization (PSO) (termed as ‘M-PSO’) for control and data flow graphs (CDFGs) is presented. The major contributions of the proposed algorithm are as follows: (a) simultaneous exploration of datapath and loop UF through an integrated multi-dimensional particle encoding process using swarm intelligence; (b) an estimation model for computation of execution delay of a loop unrolled CDFG (based on a resource configuration visited) without requiring to tediously unroll the entire CDFG for the specified loop value in most cases; (c) balancing the tradeoff between power–performance metrics as well as control states and execution delay during loop unrolling; (d) sensitivity analysis of PSO parameter such as swarm size on the impact of exploration time and Quality of Results (QoR) of the proposed design space exploration (DSE) process. This analysis presented would assist the designer in pre-tuning the PSO parameters to an optimum value for achieving efficient exploration results within a quick runtime; (e) analysis of design metrics such as power, execution time and number of control steps of the global best particle found in every iteration with respect to increase/decrease in unrolling factor.The proposed approach when tested on a variety of data flow graphs (DFGs) and CDFGs indicated an average improvement in QoR of >28% and reduction in runtime of >94% compared to recent works.     

An efficient cache conscious multi-dimensional index structure

Recently, to relieve the performance degradation caused by the bottleneck between CPU and main memory, cache conscious multi-dimensional index structures have been proposed. The ultimate goal of them is to reduce the space for entries so as to widen index trees and minimize the number of cache misses. The existing index structures can be classified into two approaches according to their entry reduction methods. One approach is to compress MBR keys by quantizing coordinate values to the fixed number of bits. The other approach is to store only the sides of minimum bounding regions (MBRs) that are different from their parents partially. The second approach works well when the size of a node is small and the number of entries is small. In this paper, we investigate the existing multi-dimensional index structures for main memory database systems through experiments under the various work loads. Then, we propose a new index structure that exploits the properties of the both techniques. We implement existing multi-dimensional index structures and the proposed index structure. We perform various experiments to show that our approach outperforms others.     

ISNet－严格的多维NMR谱解析理论（I）

ＩＳＮｅｔ（独立自旋偶合网络,ＩｎｄｅｐｅｎｄｅｎｔＳｐｉｎＣｏｕｐｌｉｎｇＮｅｔｗｏｒｋ）是作者为了严格地从多维ＮＭＲ谱推导出溶液状态中复杂有机物的分子结构而建立的新概念。从数学上讲,ＩＳＮｅｔ可被视为分子结构连接性的另一种表达形式,是一种图论表达。与分子结构图的共价键表达不同之处在于,ＩＳＮｅｔ中的每一条边在理论上是二维ＮＭＲ谱图上的一个可观察的峰。本文建立ＩＳＮｅｔ概念,并讨论从实验波谱,运用ＩＳＮｅｔ分析理论导出各类分子结构的原理及其图论、模糊数学基础。     

QCS:一种OLAP预防多维推理方法的研究

针对目前多数联机分析处理(OLAP)推理控制方法计算复杂性高、实用性不强的问题,在前人研究基础上,提出一种改进的基于查询单元集QCS(Query Cells Set)的OLAP预防多维推理方法.该方法把OLAP查询的多维推理威胁预防检测放在查询涉及到的底层不相交的单元集(即QCS),而不是单个单元上,从而降低了推理威胁检测算法的计算复杂性,这更符合OLAP的查询处理要求.同时给出QCS方法的有效性证明和算法的实现,并用实例进行说明.与以往的推理控制方法相比,QCS方法不仅可有效保护OLAP系统的隐私信息,而且具有较高的计算效率,能满足OLAP系统的实用性要求.     

多维图结构聚类的社交关系挖掘算法

社交关系的数据挖掘一直是大图数据研究领域中的热门问题。图聚类算法如SCAN（Structural clustering algorithm for networks）虽可迅速地从海量图数据中获得关系紧密的社区结构,但这类社区往往只表示了社交对象的聚集,无法反馈对象间的真实社交关系,如家庭成员、同事、同学等。要获取对象间真实的社交关系,需要更多维度地挖掘现实中社交对象间复杂的交互关系。对象间的交互维度很多,例如：通话、见面、微信、Email等,而传统SCAN等聚类算法仅能够挖掘单维度的交互数据。本文在研究社交对象间的多维社交关系图数据与传统图结构聚类算法的基础上,提出了一种有效的子空间聚类算法SCA（Subspace Cluster Algorithm）,首次对多维度下子空间的图结构聚类进行研究,目的是探索如何通过图数据挖掘发现对象间真实的社交关系。SCA算法遵循自底向上的原则,能够发现社交图数据中所有子空间的聚类集。为了提升SCA的运行速度,我们利用其子空间聚类单调性进行了性能优化,进而提出了剪枝算法SCA+。最后,我们进行了大规模的性能测试实验,以及真实数据的案例研究,其结果验证了算法的效率和效用。     

HD Tree: A novel data structure to support multi-dimensional range query for P2P networks

There are two basic concerns for supporting multi-dimensional range query in P2P overlay networks. The first is to preserve data locality in the process of data space partitioning, and the second is the maintenance of data locality among data ranges with an exponentially expanding and extending rate. The first problem has been well addressed by using recursive decomposition schemes, such as Quad-tree, K-d tree, Z-order, and Hilbert curve. On the other hand, the second problem has been recently identified by our novel data structure: HD Tree. In this paper, we explore how data locality can be easily maintained, and how range query can be efficiently supported in HD Tree. This is done by introducing two basic routing strategies: hierarchical routing and distributed routing. Although hierarchical routing can be applied to any two nodes in the P2P system, it generates high volume traffic toward nodes near the root, and has very limited options to cope with node failure. On the other hand, distributed routing concerns source and destination pairs only at the same depth, but traffic load is bound to some nodes at two neighboring depths, and multiple options can be found to redirect a routing request. Because HD Tree supports multiple routes between any two nodes in the P2P system, routing in HD Tree is very flexible; it can be designed for many purposes, like fault tolerance, or dynamic load balancing. Distributed routing oriented combined routing (DROCR) algorithm is one such routing strategy implemented so far. It is a hybrid algorithm combining advantages from both hierarchical routing and distributed routing. The experimental results show that DROCR algorithm achieves considerable performance gain over the equivalent tree routing at the highest depth examined. For supporting multi-dimensional range query, the experimental results indicate that the exponentially expanding and extending rate have been effectively controlled and minimized by HD Tree overlay structure and DROCR routing.     

有限的土地无限的景观--荷兰高柏伙伴公司多维景观规划

荷兰土地十分紧缺,多维的规划和利用昂贵的土地是可持续发展的关键.荷兰高柏伙伴公司通过"多维景观规划" 的设计哲学,在有限的土地上创造出无限的景观.他们并不单纯局限于土地的高效功能规划,同时还关注于多样化、人性化、文化延续、可持续发展、人工与自然和谐等多维景观层面.     

Self-tuning management of update-intensive multidimensional data in clusters of workstations

Contemporary applications continuously modify large volumes of multidimensional data that must be accessed efficiently and, more importantly, must be updated in a timely manner. Single-server storage approaches are insufficient when managing such volumes of data, while the high frequency of data modification render classical indexing methods inefficient. To address these two problems we introduce a distributed storage manager for multidimensional data based on a Cluster-of-Workstations. The manager addresses the above challenges through a set of mechanisms that, through selective on-line data reorganization, collectively maintain a balanced load across a cluster of workstations. With the help of both a highly efficient and speedy self-tuning mechanism, based on a new data structure called stat-index, as well as a query aggregation and clustering algorithm, our storage manager attains short query response times even in the presence of massive modifications and highly skewed access patterns. Furthermore, we provide a data migration cost model used to determine the best data redistribution strategy. Through extensive experimentation with our prototype, we establish that our storage manager can sustain significant update rates with minimal overhead.