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A novel grouping aggregation algorithm for online analytical processing

2012 National Conference on Information technology and computer Science, CITCS 2012

作者： Chen, Wei Liu, Yongshan Wang, Ning College of Information Science and Engineering Yanshan University Qinhuangdao China Key Laboratory for Computer Virtual Technology and System Integration of HeBei Province Qinhuangdao China

ISBN: (纸本)9789491216381

As regard to improve the efficiency of grouping aggregation calculation, the data is compressed by using binary encoding, and the dimension hierarchical grouping attribute set encodings of each dimension table are calculated by using the dimension hierarchy tree. Then different encodings are put into temporary table to be sorted and grouped, and the grouping sequence numbers of fact table records which satisfy the query conditions are computed. Finally, the buffer is located by the grouping sequence number and the grouping aggregation calculation is completed. Theoretical analysis and experimental results show that the proposed algorithm can significantly improve the efficiency of grouping aggregation calculation. © 2012. The authors - Published by Atlantis Press.

关键词： Efficiency

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Multi-resolution terrain rendering seamlessly by function curve fitting

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Journal of Computational Information systems 2012年第24期8卷 10399-10409页

作者： Nie, Junlan Guo, Dongliang Kong, Lingfu Wang, Yanfen The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao 066004 China

During the process of real-time multi-resolution terrain rendering, avoiding cracks between LOD (Level of Detail) has always been a tedious task that requires extra complicated handling. In this paper, we propose a novel algorithm called Function Curve Fitting (FCF) to repair T-junction cracks of regular data-based LODs. FCF, as a time-efficient algorithm, uses regular grid as rendering element, could be conveniently integrated with existing algorithms and achieve seamless meshes with excellent image quality and frame rates. Compared with other existing LOD crack-repairing algorithms based on terrain rendering, the experiments on surface quality and rendering performance demonstrate the effectiveness of our FCF approach. © 2011 by Binary Information Press.

关键词： Curve fitting

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Kainate induced gamma oscillations and spike-gamma relationship analysis

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Advances in Information Sciences and Service Sciences 2012年第21期4卷 606-612页

作者： Wen, Dong Peng, Ce Li, Xiaoli Lu, Chengbiao The Key Laboratory for Computer Virtual Technology System Integration of HeBei Province Yanshan University Qinhuangdao Hebei 066004 China Institue of Electrical Engineering Yanshan University Qinhuangdao Hebei 066004 China

Neuronal oscillations in the gamma frequency range and spikes have been reported in many cortical areas in the information processing, but the role of spikes play in cortical processing remains unclear. The aim of this study was to examine the role of kainate in the generation of oscillatory field activity at gamma frequency in the hippocampal slice prepared from rat brain, and to determine the phase relationship between spikes and gamma oscillations. The kainate induced large amplitude field population spiking activity, which is correlated linearly with the field gamma oscillations. The relationship between timing of spikes and the phase of gamma oscillation was determined by an analysis of circular statistics. Further analysis with Rayleigh test revealed that the relationship of phase-locking between spikes and gamma rhythm is of statistical significance. These data demonstrate that kainate sensitive neuronal networks within hippocampus are able to generate gamma oscillations which are modulated by large amplitude population spikes and phase-locking of spikes to the gamma rhythm suggest the role of memory enhancement in the presence of Kainate receptor activation.

关键词： Locks (fasteners)

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An algorithm based on compressed tree and bit vector table for mining frequent patterns on uncertain data streams

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Journal of Convergence Information technology 2012年第20期7卷 389-398页

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

Most of algorithms based on tree structure for mining frequent pattern on uncertain data streams always store a large number of tree nodes, and record the corresponding information of data streams which can cause massive information storages. In this paper, an algorithm CTBVT based on compressed tree and bit vector table for mining frequent patterns on uncertain data streams, is proposed. The uncertain data streams are initialized to probability-vector table, in the table, the items are represented by transactions, unlike other bit vector tables the occurrence probabilities of items are stored in it. When the window slides, all the columns in probability-vector table are left shift m bits at the same time and m is the number of transactions in the window. We also propose compressed tree in which the items with different probabilities are stored in the same tree nodes, which will reduce the number of tree nodes significantly, then the items and its probability in the tree node correspond to the bit vector table are converted into binary bit vector, the number of 1s in the binary bit vector is the frequency of the tree node. Afterwards, each leaf node of the tree is connected to an array which is used to store the combination of all items and their expected support in the path. The leaf nodes are stored in the LeafList. Finally, we scan the arrays that are linked to the leaf nodes in the LeafList and compare the expected support that is stored in the array with a minimum support threshold minSup to get all the frequent itemsets, mining time will reduce dramatically. Experiment results show that CTBVT is very efficiency and scalable.

关键词： Probability

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Mining weighted closed sequential patterns by memory indexing

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content technology and its Applications 2012年第19期6卷 432-440页

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

There exist two major problems in weighted closed sequential patterns mining. The first is that only the weights of items are considered and they ignore the time-interval information of data elements during the mining process;the second is that the existing weighted closed sequential pattern mining algorithms need to scan the sequence database many times or to construct numerous intermediate databases. To address these problems, we propose a memory-based algorithm, MIWCSpan (Memory Indexing for Weighted Closed Sequential pattern mining), for weighted closed sequential pattern mining. In the algorithm, we define a novel sequence weighting approach to find more interesting sequential patterns. Both the weight of sequence items and the time-interval of the data elements are considered in this approach. Moreover, an improved index set based on time-interval, p-iidx, is defined. The structure is a set of triples which store the pointer pointing to the sequence containing p, the time-interval of p in the sequence and the position where p occurs. In the mining process, MIWCSpan first scans the sequence database to read the database into memory. Then it adopts the find-then-index technique recursively to find the items which can constitute a weighted closed sequential pattern and construct p-iidx for the possible weighted closed sequential pattern. At last, the algorithm uses the close-detecting to mine the weighted closed sequential patterns efficiently. The experimental results show that MIWCSpan is better on running time, and it has good scalability.

关键词： Database systems

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Subspace clustering based on finding effective spaces

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content technology and its Applications 2012年第22期6卷 580-589页

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

In high-dimensional data space, because the data is sparse inherently, clusters tend to exist in different subspaces, which makes the traditional methods no longer suitable for use. In this paper, we present SCFES, a subspace clustering algorithm based on finding effective spaces. First, we define the effective dimension. By calculating relative entropy we remove redundancy dimensions which affect clustering accuracy. Second, according to the data distribution in the effective dimensions, we get the effective intervals through merging adjacent intervals. The effective space is composed of effective intervals. Third, we extend the density estimator based on undirected acyclic connected graph by using weight so as to estimate the expectation of existing clusters in the space, at the same time combine it with the monotonicity of the clustering criterion mentioned in the CLIQUE algorithm to prune candidates. Consequently we get the effective spaces. Finally, we adopt the structure of sibling tree to store all the effective spaces and use DBSCAN algorithm based on density to generate maximal subspace clusters in some effective spaces. Experimental results show that SCFES effectively finds arbitrarily shaped and positioned clusters in different subspaces. Meanwhile SCFES has better clustering quality and scalability.

关键词： Clustering algorithms

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virtual grid-based clustering of uncertain data on vulnerability database

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Journal of Convergence Information technology 2012年第20期7卷 429-438页

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

Most existing vulnerability taxonomy classifies vulnerabilities by their idiosyncrasies, weaknesses, flaws and faults et al. The disadvantage of the taxonomy is that the classification standard is not unified and there is overlap classification phenomenon in vulnerability taxonomy. In order to solve the problem, we will propose an algorithm VUNClique, virtual Grid-based Clustering of Uncertain Data on vulnerability database. Firstly, this paper transforms the vulnerability database into uncertain dataset using the existing vulnerability database pretreatment model. Secondly, we define a virtual grid structure, the cells are divided into real cells and virtual cells, but only the real cells which contain data objects stored in memory. The probability attribute value similarity is defined to deal with the similarity of non-numeric attributes, which compares the number of non-numeric attributes with the same value between tuples to measure the similarity. We provide a secondary partition algorithm to improve the similarity between the tuples in the same cell, the algorithm merges a tuple into it's high-density neighbor cell which has the maximum value of probability attribute value similarity with it. Then, a novel identify cluster algorithm is provided to cluster the high-density real cells. It can identify clusters of arbitrary shapes by traversing real cells twice. Finally, performance experiments over the uncertain dataset transformed by NVD vulnerability database. The experiments results show that VUNClique can find clusters of arbitrary shapes, and greatly improve the efficiency of clustering.

关键词： Cells

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An energy-efficient routing algorithm of zigbee network

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Journal of Computational Information systems 2012年第23期8卷 9873-9879页

作者： Liu, Yongshan Chen, Wei Shang, Xuehui Lv, Yongteng Han, Yuanyuan Liu, Chang College of Information Science and Engineering Yanshan University Qinhuangdao 066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004 China

As regard to the case of extending the lifetime of zigbee network, the defination of node's boundary is proposed. First, all the information for node's boundary is stored when zigbee network is built. Then, the packet between the nodes which are in the node's boundary is transferred directly. The packet between the nodes whose boundaries are adjacent is transferred through the node with maximum energy. The exprimental results show that the new algorithm can efficiently extend the lifetime of zigbee network. ©2012 by Binary Information Press.

关键词： Zigbee

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A high dimensional data clustering algorithm based on hypergraph

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Journal of Computational Information systems 2012年第23期8卷 9839-9846页

High dimensional data clustering is an important issue for data mining. Firstly, the records in the dataset are mapped to the vertices of hypergraph, the hyperedges of hypergraph are composed of the vertices which have the same value on one certain attributes. Then a multilevel hypergraph partitioning algorithm is used to find k parts of the hypergraph. Finally, the clusters with good quality are choosed out by computing the value of clusters validity. The experimental results show that the new algorithm can efficiently construct high quality clusters. ©2012 by Binary Information Press.

关键词： Data mining

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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 scalability.

关键词： Relativity

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