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DMSN '07: 4th Workshop on data Management for Sensor Networks

作者： Lai, Yongxuan Chen, Hong Wang, Yufeng School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering MOE No.59 Zhongguancun Street Haidian District Beijing China

ISBN: (纸本)9781595939111

data-centric storage is an effective and important technique in the wireless sensor networks. It stores the sensing data according to their values by mapping them to some point in the network in order to avoid routing all the values outside the network and flooding the queries. However, in most data-centric storage schemes, there is a "hotspot" problem due to the skewness of data and randomness of the mapping functions. Large number of sensor readings (events) may be routed to the same point by the predefined hashed function. In this paper, we propose a new Dynamic BAlanced data-centric Storage (DBAS) scheme, a cooperative strategy between the base station and the in-network processing in wireless sensor network. Our scheme, which utilizes the rich resources in the base station and is aware of the data distributions of the network, dynamically adjusts the mappings from readings to the storage points to balance the storage and workload in the network, as well as to reduce the cost of storing these readings. Moreover, it takes advantage of perimeter routing algorithm of the GPSR routing protocol to store multiple copies of readings to improve the robustness of the network with little overhead. Simulation results show that DBAS is more balanced and energy efficient than the traditional data-centric storage mechanism in wireless sensor network.

关键词： Wireless sensor networks

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J+-tree: A new index structure in main memory

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12th International Conference on database Systems for Advanced Applications, DASFAA 2007

作者： Luan, Hua Du, Xiaoyong Wang, Shan Ni, Yongzhi Chen, Qiming School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing China HP Labs. China Beijing China

ISBN: (纸本)9783540717027

As the memory capacity increases and the hardware becomes cheaper, main memory databases (MMDB) have come true and been used in more and more applications, because they can provide better response time and throughputs. The advent of MMDB requires a reconsideration of data structures and algorithms of traditional DBMS. The index structure is one of the most important aspects that need be redesigned since it can affect the overall system performance heavily. Even though the T-tree index, which was proposed for main memory databases, has been widely accepted as a promising index structure. B+-tree and its variants still have their advantages in memory and are also regarded as the potential main memory database index structures. In this paper, we propose a new indexing technique called J+-tree for MMDB, inspired by the Judy structure which is an associative array data structure. Our J+-tree index not only holds the advantages of Judy (such as good single value search characteristic) but also outperforms it in many ways. For example, J +-tree can obtain better performance for range queries that are very slow in Judy structure. We compare the J+-tree index with Judy, T-tree and B+-tree on time and space aspects, and the experimental results show that J+-tree can provide better overall performance in main memory. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Storage allocation (computer)

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

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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

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Energy-efficient approximate skyline computation in sensor networks

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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

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Computing Degree of Association Based on Different Semantic Relationships

Computing Degree of Association Based on Different Semantic ...

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International Workshop on database and Expert Systems Applications

作者： Xuan Tian Xiaoyong Du Haihua Li School of Information Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China

In domain ontologies, there is usually no weight assigned to the link between two concepts. This has been considered as one of main obstacles in using ontologies. Semantic Association (SA) is to depict the correlation of two concepts, and can be measured as the weight of the link. In this paper, we defined Degree of Association (DOA) to measure SA from a concept to its direct-related concept in domain ontology, and proposed a Language-Model-Based Method (LMBM) to compute DOA. Our idea comes from the intuition that the semantic relationship between two concepts implies certain semantic association of them. We took probabilistic model for computing DOA, and used Maximum Likelihood Estimation to estimate parameters. We tested the proposed method on two different domain ontologies, and applied it in experiments of semantic query expansion. Experimental results show the benefit of our approach and demonstrate the promising effectiveness over semantic query expansion.

关键词： Ontologies information retrieval Direction of arrival estimation Parameter estimation data mining databases Expert systems Computer applications Laboratories data engineering

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Modeling User_s Cognitive Structure in Contextual information retrieval

Modeling User_s Cognitive Structure in Contextual Informatio...

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International Conference on Fuzzy Systems and knowledge Discovery (FSKD)

作者： Xuan Tian Xiaoyong Du He Hu Haihua Li School of Information Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China

In contextual information retrieval, the retrieval of information depends on the time and place of submitting query, history of interaction, task in hand, and many other factors that are not given explicitly but implicitly lie in the interaction and surroundings of searching, namely the context. User's cognition is one of important contextual factors for understanding his or her personal needs. We propose a model called DOSAM to get user's individual cognitive structure on domain knowledge. DOSAM is developed from the spreading-activation model of psychology and is established on the domain ontology. The cost analysis of algorithm shows that it is feasible to get cognitive structure by DOSAM. Personalized search experimental results on digital library indicate that DOSAM can help improve the search effectiveness and user's satisfaction.

关键词： Context modeling information retrieval Ontologies Cognition History Psychology Helium Laboratories data engineering knowledge engineering

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Adaptive load shedding for data stream using PI feedback controller

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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

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Energy-efficient Robust data-centric Storage in Wireless Sensor Networks

Energy-efficient Robust Data-centric Storage in Wireless Sen...

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第三届IEEE无线通讯、网络技术暨移动计算国际会议

作者： Yongxuan Lai Yufeng Wang Hong Chen School of Information Renmin University of China Beijing P.R.China Key Laboratory of Data Engineering and Knowledge Engineering MOE

In this paper, we propose a new Dynamic datacentric Storage (DDS) mechanism in wireless sensor network. DDS, which is aware of the data distributions of the network, dynamically adjusts the mappings from sensor readings to the storage points to reduce the cost of storing these readings, as well as to balance the storage and workload in the network. Moreover, it takes advantage of the GPSR routing protocol to store multiple copies of readings to improve the robustness of the network with little overhead. Simulation results show that the approach is more energy-efficient and robust than other data-centric schemes.

关键词： energy-efficient robust storage data-centric storage wireless sensor networks

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Minimizing XML Queries Using a Family of Constraints

Minimizing XML Queries Using a Family of Constraints

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International Conference on Fuzzy Systems and knowledge Discovery (FSKD)

作者： Xiping Liu Changxuan Wan School of Information Technology Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

An effective way to optimize XML queries is to minimize XML queries. In this paper, we improve redundance elimination in XPath queries greatly by incorporating two novel kinds of constraints: parent constraint and sibling constraint, and by extending the tractable fragment to include descendant-or-self axis. The two novel kinds of constraints, together with child constraint and descendant constraint, form a family of constraints, which complicate the problem but offer possibilities for further minimization. Two techniques, tree augmentation and simulation augmentation, are employed to cope with constraints. We elaborate on the minimizing algorithms and running efficiencies both in the absence and in the presence of various kinds of constraints.

关键词： XML Polynomials Minimization methods information technology Finance Laboratories knowledge engineering Constraint optimization Fuzzy systems System testing

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Measuring Semantic Associaiton in Domain Ontology

Measuring Semantic Associaiton in Domain Ontology

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International Conference on Semantics, knowledge and Grid (SKG)

作者： Xuan Tian Haihua Li Xiaoyong Du School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOESchool of Information Renmin University of China Beijing China

In domain ontology, semantic association (SA) is used to depict the correlation between two concepts. In this paper, we define semantic association degree (SAD) for measuring SA in the domain ontology. We first present a method to measure SAD of two direct related concepts by evaluating the semantic relationship between them, and then give another method to measure SAD of two indirect related concepts though SAD of two directed neighboring concepts. A set of comparison experiments show the benefit of our approaches.

关键词： Ontologies Psychology data mining Diseases information retrieval Laboratories data engineering knowledge engineering Text categorization History

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