Scaling up the DBSCAN Algorithm for Clustering Large Spatial Databases Based on Sampling Technique

Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recognition, image processing, and etc. We combine sampling technique with DBSCAN algorithm to cluster large spatial databases, and two sampling based DBSCAN (SDBSCAN) algorithms are developed. One algorithm introduces sampling technique inside DBSCAN, and the other uses sampling procedure outside DBSCAN. Experimental results demonstrate that our algorithms are effective and efficient in clustering large scale spatial databases.

DCAD:a Dual Clustering Algorithm for Distributed Spatial Databases

Spatial objects have two types of attributes： geometrical attributes and non-geometrical attributes, which belong to two different attribute domains （geometrical and non-geometrical domains）. Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clustering： local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted clustering is obtained based on those features fective and efficient. Second, local features from each site are sent to a central site where global Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient.

CMA:an efficient index algorithmof clustering supporting fast retrieval oflarge image databases

To realize content-hased retrieval of large image databases, it is required to develop an efficient index and retrieval scheme. This paper proposes an index algorithm of clustering called CMA, which supports fast retrieval of large image databases. CMA takes advantages of k-means and self-adaptive algorithms. It is simple and works without any user interactions. There are two main stages in this algorithm. In the first stage, it classifies images in a database into several clusters, and automatically gets the necessary parameters for the next stage-k-means iteration. The CMA algorithm is tested on a large database of more than ten thousand images and compare it with k-means algorithm. Experimental results show that this algorithm is effective in both precision and retrieval time.

A recovery method using recently updated record information in shared-nothing spatial database cluster

A shared nothing spatial database cluster is system that provides continuous service even if some system failure happens in any node. So, an efficient recovery of system failure is very important. Generally, the existing method recovers the failed node by using both cluster log and local log. This method, however, cause several problems that increase communication cost and size of cluster log. This paper proposes novel recovery method using recently updated record information in shared nothing spatial database cluster. The proposed technique utilizes update information of records and pointers of actual data. This makes a reduction of log size and communication cost. Consequently, this reduces recovery time of failed node due to less processing of update operations.

An on-line scaling method for improving scalability of a database cluster

The explosive growth of the Internet and database applications has driven database to be more scalable and available, and able to support on line scaling without interrupting service. To support more client’s queries without downtime and degrading the response time, more nodes have to be scaled up while the database is running. This paper presents the overview of scalable and available database that satisfies the above characteristics. And we propose a novel on line scaling method. Our method improves the existing on line scaling method for fast response time and higher throughputs. Our proposed method reduces unnecessary network use, i.e., we decrease the number of data copy by reusing the backup data. Also, our on line scaling operation can be processed parallel by selecting adequate nodes as new node. Our performance study shows that our method results in significant reduction in data copy time.

Density-based clustering method in the moving object database

With the rapid advance of wireless communication, tracking the positions of the moving objects is becoming increasingly feasible and necessary. Because a large number of people use mobile phones, we must handle a large moving object database as well as the following problems. How can we provide the customers with high quality service, that means, how can we deal with so many enquiries within as less time as possible? Because of the large number of data, the gap between CPU speed and the size of main memory has increasing considerably. One way to reduce the time to handle enquiries is to reduce the I/O number between the buffer and the secondary storage.An effective clustering of the objects can minimize the I/O cost between them. In this paper, according to the characteristic of the moving object database, we analyze the objects in buffer, according to their mappings in the two dimension coordinate, and then develop a density based clustering method to effectively reorganize the clusters. This new mechanism leads to the less cost of the I/O operation and the more efficient response to enquiries.

A New Database Intrusion Detection Approach Based on Hybrid Meta-Heuristics

A new secured database management system architecture using intrusion detection systems(IDS)is proposed in this paper for organizations with no previous role mapping for users.A simple representation of Structured Query Language queries is proposed to easily permit the use of the worked clustering algorithm.A new clustering algorithm that uses a tube search with adaptive memory is applied to database log files to create users’profiles.Then,queries issued for each user are checked against the related user profile using a classifier to determine whether or not each query is malicious.The IDS will stop query execution or report the threat to the responsible person if the query is malicious.A simple classifier based on the Euclidean distance is used and the issued query is transformed to the proposed simple representation using a classifier,where the Euclidean distance between the centers and the profile’s issued query is calculated.A synthetic data set is used for our experimental evaluations.Normal user access behavior in relation to the database is modelled using the data set.The false negative(FN)and false positive(FP)rates are used to compare our proposed algorithm with other methods.The experimental results indicate that our proposed method results in very small FN and FP rates.

Selection and Research for Online Registration System’s Database System

Based on the Online Registration System (ORS) characteristics and key technology analysis, this paper points out that that a good performance and high stability of the ORS lies in the choice of the system database. Database clustering technology which has merits such as concurrent processing, easy expansion, and high security is proposed to achieve database subsystem of ORS, and the design of the database cluster system framework is available in this paper. Finally, we also explore the database load balancing of the cluster system, heterogeneous database replication technology.

A New Approach for Database Fragmentation and Allocation to Improve the Distributed Database Management System Performance

The efficiency and performance of Distributed Database Management Systems (DDBMS) is mainly measured by its proper design and by network communication cost between sites. Fragmentation and distribution of data are the major design issues of the DDBMS. In this paper, we propose new approach that integrates both fragmentation and data allocation in one strategy based on high performance clustering technique and transaction processing cost functions. This new approach achieves efficiently and effectively the objectives of data fragmentation, data allocation and network sites clustering. The approach splits the data relations into pair-wise disjoint fragments and determine whether each fragment has to be allocated or not in the network sites, where allocation benefit outweighs the cost depending on high performance clustering technique. To show the performance of the proposed approach, we performed experimental studies on real database application at different networks connectivity. The obtained results proved to achieve minimum total data transaction costs between different sites, reduced the amount of redundant data to be accessed between these sites and improved the overall DDBMS performance.

SUN CLUSTER数据库双机热备研究

介绍了双机热备的工作原理,Sun cluster的系统需求,构造Sun cluster系统的基本步骤以及在Solaris环境下,采用Sun cluster配置Oracle10g数据库双机热备系统的情况并对双机切换进行了测试。应用表明,采用了双机热备的数据库系统能有效地避免数据库服务器宕机而导致的应用系统拒绝服务,从而使应用系统运行更加稳定、可靠。

Preferred alignments of angular momentum vectors of galaxies in six dynamically unstable Abell clusters

A spatial orientation of angular momentum vectors of galaxies in six dynamically unstable Abell clusters（S1171, S0001, A1035, A1373, A1474 and A4053） is studied. For this, twodimensional observed parameters（e.g., positions, diameters and position angles） are converted into three-dimensional（3D） rotation axes of the galaxy using the ＇position angle-inclination＇ method. The expected isotropic distribution curves for angular momentum vectors are obtained by performing random simulations. The observed and expected distributions are compared using several statistical tests.No preferred alignments of angular momentum vectors of galaxies are noticed in all six dynamically unstable clusters, supporting the hierarchy model of galaxy formation. These clusters have a larger value of velocity dispersion. However, local effects are noticed in the clusters that have substructures in the1D-3D number density maps.

MySQL Cluster集群数据库误操作恢复方法研究

本文介绍了应对MySQL Cluster集群数据库误操作的一些预防措施和误操作后进行数据恢复的三种方案:通过解析二进制日志生成逆向操作脚本进行恢复、通过全库备份及二进制日志进行恢复、通过延迟备库及二进制日志进行恢复,并给出这三种方案的适用场景。

On Clustering Algorithms for Biological Data

Age of knowledge explosion requires us not only to have the ability to get useful information which represented by data but also to find knowledge in information. Human Genome Project achieved large amount of such biological data, and people found clustering is a promising approach to analyze those biological data for knowledge hidden. The researches on biological data go to in-depth gradually and so are the clustering algorithms. This article mainly introduces current broad-used clustering algorithms, including the main idea, improvements, key technology, advantage and disadvantage, and the applications in biological field as well as the problems they solve. What’s more, this article roughly introduces some database used in biological field.

New open clusters found by manual mining of data based on Gaia DR2

The physical nature of a series of 20 new open clusters is confirmed employing existing data on putative star members,mainly from the second Gaia Data Release(DR2).The clusters were discovered as overdensities of stars by visual inspection of either photographic DSS plates or proper motion plots of random source fields.The reported objects are not present in the most comprehensive or recent catalogs of stellar clusters and associations.For all of them,clumps of comoving stars are revealed in the proper motion space.The parallaxes of the clumped stars are compatible with the real existence of open clusters over narrow ranges of distances.Surface density calculations,free of most noise from non-member sources,allow differentiating a cluster core and an extended cluster corona in some instances.Color-magnitude diagrams generally show a definite main sequence that allows confirmation of the physical existence of the clusters and some of their characteristics.Two of the new clusters seem to form a double system with a common origin.Several of the new clusters challenge the claim of near completeness of the known OC population in the distance range from 1.0 to 1.8 kpc from the Sun(Kharchenko et al.).

一种实现低成本数据库冗余阵列的Clustered JDBC方法

文章介绍了低成本数据库冗余阵列的基本模型和体系结构,并重点分析了使用低成本数据库冗余阵列实现Clustered JDBC的基本原理。

First PPMXL photometric analysis of open cluster Ruprecht 15

We present the first in a series studying the astrophysical parameters of open clusters using the PPMXL＊ database whose data are applied to study Ruprecht 15. The astrophysical parameters of Ruprecht 15 have been estimated for the first time.

基于人工智能技术的分布式数据库重复记录自动检测系统设计

以人工智能技术为基础前提的分布式数据库重复记录自动检测的方式,以提高数据库查询时的准确率以及查询效率。设计系统首先对数据信息进行对应的特征提取,而后通过权衡函数对样本信息进行整合,通过自适应分解得到相应的目标函数并求解,结合灰狼算法以及Shingle完成数据查询。经过算例验证,改进设计方式准确率均超过90%,平均耗时在35 s以内,满足自动查询快速精确的要求。

基于级联碰撞缺陷数据库的源项对辐照微结构演化影响团簇动力学模拟研究

团簇动力学(CD)方法是模拟核材料在高能粒子辐照下微观结构演化的重要方法之一,源项是团簇动力学方法的关键输入。经典CD方法中源项通常采用经验拟合得到,未能充分利用原子尺度获得的初始缺陷信息。随着分子动力学等方法的发展,级联碰撞缺陷数据库大为丰富,结合初级离位原子(PKA)能谱足以得到更为合理的源项。由于级联碰撞缺陷数据库的能量值数量相对于准连续PKA能谱仍然偏少,本文提出了5种从准连续PKA能谱得到级联能量分立值的抽样算法,并基于团簇动力学方法模拟低剂量中子辐照纯钨实验对算法进行了验证和比较。

一种基于改进差分进化的K-Means聚类算法研究

为改进传统K-Means聚类算法中因随机选取初始聚类中心而导致聚类结果不稳定且效率低的缺点,提出一种基于改进差分进化的K-Means聚类算法(AGDE-KM)。首先,设计自适应操作算子来提升算法前期的全局搜索能力和后期的收敛速度;其次,设计多变异策略并引入权重系数,在算法的不同进化阶段发挥不同变异策略的优势,平衡算法的全局和局部搜索能力,加快算法的收敛速度;最后,提出一种基于当前种群最佳个体的高斯扰动交叉操作,为个体提供更优进化方向的同时保持种群在“维”上的多样性,避免算法陷入局部最优。将算法停止执行时输出的最优解作为初始聚类中心替代传统K-Means随机选取的聚类中心。将提出算法在UCI公共数据库中的Vowel、Iris、Glass数据集和合成数据集Jcdx上进行对比实验,误差平方和(SSE)相对于传统K-Means分别减小5.65%、19.59%、13.31%、6.1%,聚类时间分别减少83.03%、81.33%、77.47%、92.63%。实验结果表明,提出的改进算法具有更快的收敛速度和更好的寻优能力,显著提升了聚类的效果、效率和稳定性。

海洋环境气候新闻大数据挖掘和空间聚类模型研究

以GDELT(global database of event,language,tone)数据库为例,讨论使用数据源路径爬取相关新闻文档。利用改进的AC自动机进行多模关键词匹配完成初步的数据清洗;对过滤好的文档数据进行主题数量评估,再利用LDA模型对其进行主题分类和关键词提取。根据分类结果,对海洋环境与气候主题新闻数据及相关指标建立空间聚类模型,最终形成一个对海量文档数据进行抓取、清洗、主题挖掘、空间聚类及可视化呈现的分析模型。