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A load balancing algorithm for multi-root ONS

Journal of Computational Information systems 2014年第7期10卷 2735-2742页

作者： Hao, Fengzhai Liu, Yongshan Chen, Wei College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao 066004 China Data Industry Research Lab. Qinhuangdao 066004 China

In order to solve the overload problem of root ONS in the EPC network, a load balancing algorithm based on multi-root ONS is proposed. Based on the proposed load balancing ONS (LB ONS) architecture, the ONS Root is defined to have two states, idle or busy. According to CPU utilization and memory utilization of the ONS Root and in combination with threshold controlling to periodically update the ONS Root states, which makes the requests coming from client applications be transferred to an ONS Root which is idle. Experiment results show that the algorithm can reduce query hops and query delays effectively and offer lookup service efficiently. © 2014 Binary Information Press.

关键词： Internet of things

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Angle historgam model of directional relationship in 3D based on Double-projection and Rounded-subdivision

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Journal of Information and Computational Science 2014年第18期11卷 6471-6478页

作者： Kong, Dehan Liu, Yongshan Ma, Junlei Department of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China Data Industry Research Lab Qinhuangdao China

The formal model of spatial directional relations is one of the most important parts in spatial relation research. The most of models are based on Minimum Bounding Rectangle (MBR), and they are not compliant with the regular pattern of human cognition. In order to get a closer conclusion to human cognition on directional relationship, Angle Histogram model based on Double-projection and Rounded-subdivision (AHDPRS) is proposed in this paper. The model uses the maximum inscribed circles to find out the maximum parts of the object, and calculates the directional relationship between the centers of the circles. This model ignores the inessential details to ensure the result which will be closer to human cognition. The experiments show that this model is feasible.

关键词： Graphic methods

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Subspace clustering over high-dimensional data stream based on grid density and attribute relativity

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Advances in Information Sciences and Service Sciences 2012年第17期4卷 91-99页

作者： Huang, Guoyan Miao, Liyun Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao City 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao City 066004 China

The traditional clustering algorithms often fail to detect meaningful clusters in high-dimensional data space. To improve the above shortcoming, we propose GDRH-Stream, a clustering method based on the attribute relativity and grid density for high-dimensional data stream, which consists of an online component and an offline component. First, the algorithm filters out redundant attributes by computing the relative entropy. Then we define a weighted attribute relativity measure and estimate the relativity of the non-redundant attributes, and form the attribute triple. At last, the best interesting subspaces are searched by the attribute triple. On the online component, GDRH-Stream maps each data object into a grid and updates the characteristic vector of the grid. On the offline component, when a clustering request arrives, the best interesting subspaces will be generated by attribute relativity. Then the original grid structure is projected to the subspace and a new grid structure is formed. The clustering will be performed on the new grid structure by adopting an approach based on the density grid. Experimental results show that GDRH-Stream algorithm has better quality and scalab.lity.

关键词： Relativity

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An improved algorithm for community discovery in weighted social networks

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Journal of Information and Computational Science 2015年第18期12卷 6873-6881页

作者： Guo, Jingfeng Liu, Miaomiao Liu, Linlin College of Information Science and Engineering Yanshan University QinhuangdaoHebei China Northeast Petroleum University DaqingHeilongjiang China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province QinhuangdaoHebei China

The problems of AGMA (Automatic Graph Mining Algorithm) are improved and a novel algorithm, namely CRMA (Clustering Re-clustering Merging Algorithm) is proposed which can realize more reasonable community division for weighted social networks. Firstly, when re-clustering nodes, AGMA neglected the clustering situation of its neighbors and didn't take into account the weight of the edge connecting the current node and its unclustered neighbor, which led to unreasonable clustering. Aiming to this, the concept of the connection compactness is introduced. Then according to the node weight, the edge weight and the friend coefficient, each node would be clustered into the community has the largest connection compactness with itself. Secondly, for community division in some unweighted networks and signed networks, if there are many nodes whose clustered neighbors is less than its unclustered neighbors, the division results of AGMA tend to produce many small communities, which would result in the lower modularity. In view of this problem, the concepts of positive edge weight density, the cluster density and the connection coefficient between clusters are proposed. Lastly, we merge communities based on the above three concepts and the modularity is effectively enhanced finally. The higher correctness and better generality of the improved algorithm are verified through experiments. © 2015 by Binary Information Press.

关键词： Clustering algorithms

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An algorithm based on bit complementary tree for mining closed frequent itemsets

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International Journal of Advancements in Computing technology 2012年第22期4卷 427-435页

作者： Ren, Jiadong Yang, Xuan Dong, Jun College of Information Science and Engineering Yanshan University Qinhuangdao City066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao City 066004 China

In recent years, closed frequent itemsets mining has become a hot topic. In this paper, we present an algorithm BCTCF, which is based on Bit complementary tree (BCTree) in order to mine closed frequent itemsets efficiently. First we adopt bit vectors to compress the database and define a novel structure, BCTree, in which a node stores two bit vectors that are complementary and each path is given a prime value. Based on the left-most bit in the bit vectors we adopt a divide-and-conquer strategy which handles the itemsets separately and then according to the prime unique feature we can get the closed frequent itemsets quickly and it makes us need not to mine all the frequent itemsets first. Both the divide-and-conquer strategy and prime unique can decrease the runtime. Experiment results show that BCTCF is very effective and scalab.e.

关键词： Forestry

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A clustering algorithm for data stream based on spatial directed graph with core

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Journal of Convergence Information technology 2012年第19期7卷 131-139页

作者： Dong, Jun Yuan, Xuemei Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao City 066004 China Key Laboratory for Computer Virtual Technology System Integration of Hebei Province Qinhuangdao City 066004 China

Due to the randomness of the partition of grids, the edge points of clusters might be partitioned into the sparse grids. These points would become noise information out of clusters when we cluster data stream by grid-density based algorithm. A data stream clustering algorithm based on spatial directed graph with core, SDGCStream, is proposed. It uses the spatial directed graph and the orthocenter of the sparse grids to handle the edge points of clusters. At first, the algorithm defines a structure SDGC (Spatial Directed Graph with Core) to store the summary statistics of data stream. The vertices of SDGC are maintained as the stream arriving. When the clustering quest comes, the edge information is generated. The initial clustering results are got through clustering on SDGC, then we judge whether the points of sparse grids which are adjacent to the border of a cluster belong to the cluster according to the orthocenter information and the border vertices of SDGC. At last, a strategy based on the distance between clusters is presented to adjust the clustering results after handling the border of clusters. The experimental results on synthetic and real datasets show the better validity of SDGCStream on handling the edge data points of clusters, and the scalab.lity as the increasing of the length and dimensions of data stream.

关键词： Clustering algorithms

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An algorithm based on temporary table for mining top-k closed frequent patterns in data streams

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content technology and its Applications 2012年第20期6卷 582-589页

作者： Wang, Peilong Yang, Xuan Zhang, Yuanyuan Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao City 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao City 066004 China

In this paper, we present an algorithm TKBT(top-k closed frequent mining based on TKTT) to mine top-k closed frequent itemsets in data streams efficiently. First according to the consecutive and changeable characteristics of the data from data streams in sliding window, a novel structure, BWT(bit-vector window table) is defined. In BWT horizontal direction we use bit vectors to express the transactions, record the count of items in the oldest, the newest window and all the windows a t current time, which decreases the calculating time of the items count when a new window slides in. In BWT vertical direction we set window partition, which makes us just need replace the oldest window information with the corresponding newest window when a new window comes. The construction of TKTT (top-k temporary table) is based on BWT. The itemsets in TKTT are ranked in a descending count order. TKBT can get top-k closed frequent itemsets by connecting the candidates in TKTT using top-down strategy. The candidate number is reduced by using closed itemset displace its subset and less connection times are contributed to the less runtime. Experiment results show that TKBT is very effective and scalab.e.

关键词： Closed frequent item sets Data streams Sliding window Top-K

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A clustering algorithm for heterogeneous objects based on information dissimilarity

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Journal of Convergence Information technology 2012年第22期7卷 93-100页

作者： Chen, Ziyang Wang, Xuefeng Ren, Jiadong College of Information Science and Engineering Yanshan University Qinhuangdao City 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao City 066004 China

Due to the introduction of weight of attributes, most of existing dissimilarity measures can not accurately reflect the difference between two heterogeneous objects, and then clustering quality was decreased. In this paper, we present HIDK-means, an approach for clustering heterogeneous data based on information dissimilarity. At first, the algorithm defines heterogeneous information dissimilarity between two heterogeneous objects based on Kolmogorov information theory, and calculates approximately heterogeneous information dissimilarity by a universal probability of an object. Then, in the clustering process, the algorithm selects the initial cluster centers by maximum sum of dissimilarity. After that, each remaining object is assigned to a cluster center which has the smallest dissimilarity with it and the criterion function is calculated. Iteratively, cluster centers are updated and the process is ceased until the criterion function converges or the iteration number reaches the pre-set threshold. The experimental results show that the proposed algorithm HIDK-means is effective in clustering heterogeneous objects and also scalab.e to large datasets.

关键词： Clustering algorithms

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Research on load balancing strategy based on middleware

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ICIC Express Letters 2014年第9期8卷 2655-2661页

作者： Gu, Xiaoying Liu, Yongshan Man, Yubao Department of Information Science and Engineering Qinhuangdao 066004 China The Key Lab. for Computer Virtual Tech. and System Integration of Hebei Province Yanshan University No. 438 Hebei Ave Qinhuangdao 066004 China Data Industry Research Lab Qinhuangdao 066004 China

According to the former research achievements about load balancing strategy, considering the load distribution, the load condition of service node and the number of user connections, we propose a load balancing strategy based on middleware. According to the cluster service load and the number of user connections, the requests from user will be reasonably allocated to each service node by using middleware technology, then service node and requested user establish a temporary connection, after all the requests of this user are processed by this node. Experiments show that, this strategy reduces the number of requests for allocation, improves the resource utilization rate of cluster system and load balancing degree, and effectively suppresses the load jitter of system. © 2014 ISSN 1881-803X.

关键词： Middleware

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ICRA: An improved community detection algorithm on signed social networks

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Journal of Computational Information systems 2015年第11期11卷 4091-4099页

作者： Hu, Xinzhuan Guo, Jingfeng Zhao, Yue Liu, Yuanying School of Economics and Management Yanshan University Qinhuangdao China College of Information Science and Engineering Yanshan University Qinhuangdao China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

The study on signed social networks community detection has been paid more and more attention. Research shows that two-phase signed social networks community detection algorithm can not correctly divide the network. To solve the problem, we propose a new algorithm: ICRA (Improved Clustering Re-clustering Algorithm). In the process of community division, the assignment of vertices with negative edges shall be determined by comparing the value of network positive density with community positive density. Our algorithm solves the problems existing in CRA, including network division failure and wrong division starting from different vertices. ©, 2015, Binary Information Press. All right reserved.

关键词： Clustering algorithms

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