检索结果-内蒙古大学图书馆

Structural Join and Staircase Join Algorithms of Sibling Relationship

Journal of Computer Science & technology 2007年第2期22卷 171-181页

作者：万常选刘喜平 School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Jiangxi Key Laboratory of Data and Knowledge Engineering Nanchang 330013 China

The processing of XML queries can result in evaluation of various structural relationships. Efficient algorithms for evaluating ancestor-descendant and parent-child relationships have been proposed. Whereas the problems of evaluating preceding-sibling-following-sibling and preceding-following relationships are still open. In this paper, we studied the structural join and staircase join for sibling relationship. First, the idea of how to filter out and minimize unnecessary reads of elements using parent＇s structural information is introduced, which can be used to accelerate structural joins of parent-child and preceding-sibling-following-sibling relationships. Second, two efficient structural join algorithms of sibling relationship are proposed. These algorithms lead to optimal join performance： nodes that do not participate in the join can be judged beforehand and then skipped using B^＋-tree index. Besides, each element list joined is scanned sequentially once at most. Furthermore, output of join results is sorted in document order. We also discussed the staircase join algorithm for sibling axes. Studies show that, staircase join for sibling axes is close to the structural join for sibling axes and shares the same characteristic of high efficiency. Our experimental results not only demonstrate the effectiveness of our optimizing techniques for sibling axes, but also validate the efficiency of our algorithms. As far as we know, this is the first work addressing this problem specially.

关键词： XML query processing sibling relationship structural join staircase join

来源：评论

学校读者我要写书评

暂无评论

Combining Immune with Ant Colony Algorithm for Geometric Constraint Solving

Combining Immune with Ant Colony Algorithm for Geometric Con...

引用

International Workshop on knowledge Discovery and data Mining (WKDD)

作者： Hua Yuan Yi Li Wenhui Li Kong Zhao Duo Wang Rongqin Yi Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China School of Computer Science & Engineering Changchun University of Technology China Suzhou Top Institute of Information Technology Suzhou China

Geometric constraint problem can be transformed to an optimization problem which the objective function and constraints are non-convex functions. In this paper an evolutionary algorithm based on ant colony optimization algorithm and the immune system model is proposed to provide solution to the geometric constraints problem. In the new algorithm, affinity calculation process and pheromone trail lying is embedded to maintain diversity and carry out the global search and the local search in many directions rather than one direction around the same individual simultaneously. This new algorithm different with current optimization methods in that it gets the good solution by excluding bad solutions. The experimental results reported here will shed more light into how affects the hybrid algorithm's search power in solving geometric constraint problem.

关键词： Immune system Ant colony optimization Nonlinear equations knowledge engineering Computer science Educational technology Evolutionary computation data mining Laboratories Computer science education

来源：评论

学校读者我要写书评

暂无评论

MRST-- An Efficient Monitoring technology of Summarization on Stream data

引用

Journal of Computer Science & technology 2007年第2期22卷 190-196页

作者：樊小泊解婷婷李翠平陈红 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing 100872 China

Monitoring on data streams is an efficient method of acquiring the characters of data stream. However the available resources for each data stream are limited, so the problem of how to use the limited resources to process infinite data stream is an open challenging problem. In this paper, we adopt the wavelet and sliding window methods to design a multi-resolution summarization data structure, the Multi-Resolution Summarization Tree （MRST） which can be updated incrementally with the incoming data and can support point queries, range queries, multi-point queries and keep the precision of queries. We use both synthetic data and real-world data to evaluate our algorithm. The results of experiment indicate that the efficiency of query and the adaptability of MRST have exceeded the current algorithm, at the same time the realization of it is simpler than others.

关键词： Haar wavelet sliding window stream data

来源：评论

学校读者我要写书评

暂无评论

Indexing Future Trajectories of Moving Objects in a Constrained Network

引用

Journal of Computer Science & technology 2007年第2期22卷 245-251页

作者：陈继东孟小峰 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 China

Advances in wireless sensor networks and positioning technologies enable new applications monitoring moving objects. Some of these applications, such as traffic management, require the possibility to query the future trajectories of the objects. In this paper, we propose an original data access method, the ANR-tree, which supports predictive queries. We focus on real life environments, where the objects move within constrained networks, such as vehicles on roads. We introduce a simulation-based prediction model based on graphs of cellular automata, which makes full use of the network constraints and the stochastic traffic behavior. Our technique differs strongly from the linear prediction model, which has low prediction accuracy and requires frequent updates when applied to real traffic with velocity changing frequently. The data structure extends the R-tree with adaptive units which group neighbor objects moving in the similar moving patterns. The predicted movement of the adaptive unit is not given by a single trajectory, but instead by two trajectory bounds based on different assumptions on the traffic conditions and obtained from the simulation. Our experiments, carried on two different datasets, show that the ANR-tree is essentially one order of magnitude more efficient than the TPR-tree, and is much more scalable.

关键词： database spatial database access methods moving objects

来源：评论

学校读者我要写书评

暂无评论

Research on Adaptive Calibration of Tri-axial Magnetometer Based on ANN

Research on Adaptive Calibration of Tri-axial Magnetometer B...

引用

7th World Congress on Intelligent Control and Automation (WCICA 2008), vol.19

作者： Xiaohong Wang Key Laboratory of Numerical Control of Jiangxi Province Jiujiang University Jiujiang China School of Electronic and Information Engineering Dalian University of Technology Dalian China

When tri-axial magnetometer is used to estimate a magnetic field's module, different results will be obtained in different attitudes. Focusing on the diversionary errors of tri-axial magnetometer, A new adaptive calibration method based on artificial neural networks (ANN) was proposed. The relationships between the diversionary errors and the tri-axial magnetometer's system parameters, such as nonorthogonality between axes, different sensitivity and zero-shift of each axes, were analyzed. Through the corresponding ANN structure, a mathematical adaptive calibration model was established, which could be used to estimate the magnetic field's module. The simulation and practical experiment's results show that the diversionary errors of tri-axial magnetometer can be rectified under 10nT from 500nT. Without expensive, complex and special equipments, the presented calibration method is more suitable for tri-axial magnetometer.

关键词： Tri-axial magnetometer Adaptive calibration Artificial neural networks (ANN) Diversionary error

来源：评论

学校读者我要写书评

暂无评论

An approximate sequential pattern mining approach over data stream based on Levenshtein-Automata

引用

Journal of Computational information Systems 2007年第5期3卷 2025-2030页

作者： Li, Haifeng Chen, Hong School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering China

Sequential pattern mining is an important method in data mining. Traditional mining algorithms are not adapted to the fast, unlimited, continuous and dynamic data stream because they are multiple pass in scanning database. Some approximate sequential pattern mining algorithms are proposed recently which cost too many system resources in sequence compare process. A sequential compare method based on Levenshtein-Automata is proposed in this paper. This method build state conversion model with pretreatment which can finish computing the sequences' similarity in linear time. A combination of Levenshtein-Automata computation and common computation of edit distance is presented in allusion to the Levenshtein-Automata's problem of using too much memory, so a tradeoff between time cost and space cost is implemented. The experiment result shows this method is effective and efficient.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

Energy-efficient approximate skyline computation in sensor networks

引用

Journal of Computational information Systems 2007年第5期3卷 1821-1826页

作者： Xie, Tingting Lai, Yongxuan Chen, Hong School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering China

The skyline query is frequently used to find a set of dominating data points (called skyline points) in a multidimensional dataset It is one of the most important query methods for database, datastream, P2P networks. However, it has not been implemented in sensor networks due to limited energy of the sensor nodes. This paper presents an energy-efficient approximate skyline query scheme for sensor networks. According to the experiments, this scheme can greatly improve the lifetime of sensor networks compared to the naive skyline query.

关键词： Wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

CLASCN： Candidate Network Selection for Efficient Top-κ keyword Queries over databases

引用

Journal of Computer Science & technology 2007年第2期22卷 197-207页

作者：张俊彭朝晖王珊聂惠静 School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 China Computer Science and Technology College Dalian Maritime University Dalian 116026 China

keyword Search Over Relational databases （KSORD） enables casual or Web users easily access databases through free-form keyword queries. Improving the performance of KSORD systems is a critical issue in this area. In this paper, a new approach CLASCN （Classification, Learning And Selection of Candidate Network） is developed to efficiently perform top-κ keyword queries in schema-graph-based online KSORD systems. In this approach, the Candidate Networks （CNs） from trained keyword queries or executed user queries are classified and stored in the databases, and top-κ results from the CNs are learned for constructing CN Language Models （CNLMs）. The CNLMs are used to compute the similarity scores between a new user query and the CNs from the query. The CNs with relatively large similarity score, which are the most promising ones to produce top-κ results, will be selected and performed. Currently, CLASCN is only applicable for past queries and New All-keyword-Used （NAU） queries which are frequently submitted queries. Extensive experiments also show the efficiency and effectiveness of our CLASCN approach.

关键词： relational database keyword search top-κ query candidate network

来源：评论

学校读者我要写书评

暂无评论

Adaptive load shedding for data stream using PI feedback controller

引用

Journal of Computational information Systems 2007年第5期3卷 1827-1832页

作者： Huang, Chongzheng Cai, Shaojiang Xie, Zhijun Zhang, Yuanyu Chen, Hong School of Information Renmin University of China Beijing 100872 China Key Laboratory of Data Engineering and Knowledge Engineering China School of Electronics and Information Engineering Xi'an Jiaotong University Xi'an 710000 China

Load shedding has been widely used in data stream management systems (DSMSs) to keep DSMSs running steadily. One key problem in load shedding is determining how much system load to shed. Existing works tend to adapt coarse algorithm (CA) to solve this problem. In this paper, we present an adaptive PI controller-based load shedding framework for data stream. The main contribution of this paper is our use of feedback control theory to design the load shedding scheme. In contrast to the existing approaches, we firstly apply system identification to establish a dynamic model to describe DSMS, which enables us analyze DSMS quantitatively. Then, based on the model, we use the Root Locus method to design the PI controller with proven performance guarantees. The adaptive framework has been implemented by modifying Borealis system. Theoretic analysis and experimental results demonstrate that our approach is robust even when system load changes frequently. Comparing to existing strategies, our approach also achieves significantly better performance.

关键词： Feedback control

来源：评论

学校读者我要写书评

暂无评论

Optimization framework for distributed clustering scheme in wireless sensor networks

Optimization framework for distributed clustering scheme in ...

引用

SNPD 2007: 8th ACIS International Conference on Software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing

作者： Yongxuan, Lai Xiaobo, Fan Hong, Chen Tingting, Xie School of Information Renmin University of China Beijing Key Laboratory of Data Engineering and Knowledge Engineering MOE

ISBN: (纸本)0769529097

Recently there have been growing interests in the applications of wireless sensor networks. Innovative techniques that improve energy efficiency to prolong the network lifetime are highly required. Clustering is an effective topology control approach and data gathering protocol in wireless sensor networks. In this paper, we propose a novel optimization framework called MICA (MICa Adjustments) for clustering scheme in wireless sensor networks. MICA takes the cluster as a whole, and dynamically adjusts the clusters according to the real-lime workload of the local clusters to balance energy consumption among clusters. The adjustments can also deal with link failures. Simulation results show that MICA can be easily embedded into existing clustering schemes to balance the energy consumptions among the clusters, and to prolong the lifetime of wireless sensor networks. © 2007 IEEE.

关键词： Wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：