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2009 Spring Simulation Multiconference, SpringSim 2009

作者： Qin, Xiongpai Cao, Wei Wang, Shan Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China MOE Beijing100872 China

The paper describes the details of using J-SIM in main memory database parallel recovery simulation. In update intensive main memory database systems, I/O is still the dominant performance bottleneck. A proposal of parallel recovery scheme for large-scale update intensive main memory database systems is presented. Simulation provides a faster way of evaluating the new idea compared to actual system implementation. J-SIM is an open source discrete time simulation software package. The simulation implementation using J-SIM is elaborated in terms of resource modeling, transaction processing system modeling and workload modeling. Finally, with simulation results analyzed, the effectiveness of the parallel recovery scheme is verified and the feasibility of J-SIM's application in main memory database system simulation is demonstrated.

关键词： Recovery

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Lightweight data mining based database system self-optimization: A case study

Lightweight data mining based database system self-optimizat...

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Joint International Conference on Advances in data and Web Management, APWeb/WAIM 2009

作者： Qin, Xiongpai Cao, Wei Wang, Shan Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China MOE Beijing 100872 China

ISBN: (纸本)9783642006715

With the system becoming more complex and workloads becoming more fluctuating, it is very hard for DBA to quickly analyze performance data and optimize the system, self optimization is a promising technique. A data mining based optimization scheme for the lock table in database systems is presented. After trained with performance data, a neural network become intelligent enough to predict system performance with newly provided configuration parameters and performance data. During system running, performance data is collected continuously for a rule engine, which chooses the proper parameter of the lock table for adjusting, the rule engine relies on the trained neural network to precisely provide the amount of adjustment. The selected parameter is adjusted accordingly. The scheme is implemented and tested with TPC-C workload, system throughput increases by about 16 percent. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Locks (fasteners)

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Approximation algorithm for constructing data aggregation trees for wireless sensor networks

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中国高等学校学术文摘·计算机科学 2009年第4期3卷 524-534页

作者： Deying LI Jiannong CAO Qinghua ZHU Key Laboratory of Data Engineering and Knowledge Engineering School of InformationRenmin University of ChinaBeijing 100872China Internet and Mobile Computing Lab Department of ComputingHong Kong Polytechnic UniversityHong KongChina

This paper considers the problem of constructing data aggregation trees in wireless sensor networks (WSNs)for a group of sensor nodes to send collected information to a single sink *** data aggregation tree contains the sink node,all the source nodes,and some other non-source *** goal of constructing such a data aggregation tree is to minimize the number of non-source nodes to be included in the tree so as to save *** prove that the data aggregation tree problem is NP-hard and then propose an approximation algorithm with a performance ratio of four and a greedy *** also give a distributed version of the approximation *** simulations are performed to study the performance of the proposed *** results show that the proposed algorithms can find a tree of a good approximation to the optimal tree and has a high degree of scalability.

关键词： WSNs data aggregation tree approximation algorithm distributed algorithm

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COP: Privacy-preserving multidimensional partition in DAS paradigm 09

COP: Privacy-preserving multidimensional partition in DAS pa...

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2009 International Conference on Extending database Technology/International Conference on database Theory Workshops, EDBT/ICDT '09

作者： Wang, Jieping Du, Xiaoyong Wang, Haocong Yang, Pingping Key Laboratory of Data Engineering and Knowledge Engineering MOE China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9781605586502

database-as-a-Service (DAS) is an emerging database management paradigm wherein partition based index is an effective way to querying encrypted data. However, previous research either focuses on one-dimensional partition or ignores multidimensional data distribution characteristic, especially sparsity and locality. In this paper, we propose Cluster based Onion Partition (COP), which is designed to decrease both false positive and dead space at the same time. Basically, COP is composed of two steps. First, it partition covered space level by level, which is like peeling of onion;second, at each level, a clustering algorithm based on local density is proposed to achieve local optimal secure partition. Extensive experiments on real dataset and synthetic dataset show that COP is a secure multidimensional partition with much less efficiency loss than previous top down or bottom up counterparts. Copyright 2009 ACM.

关键词： database systems

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An Adaptive Method for the Efficient Similarity Calculation

An Adaptive Method for the Efficient Similarity Calculation

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14th International Conference on database Systems for Advanced Applications

作者： Cai, Yuanzhe Liu, Hongyan He, Jun Du, Xiaoyong Jia, Xu Key Labs of Data Engineering and Knowledge Engineering MOE China School of Information Renmin University of China China Department of Management Science and Engineering Tsinghua University China

ISBN: (纸本)9783642008863

SimRank is a well-known algorithm for similarity calculation based on object-to-object relationship. However, it suffers from high computation cost. Inthis paper, we find that the convergence behavior of different object pairs is different when we use SimRank to compute the similarity of objects. Many similarity scores converge fast, while others need more time before convergence. Based on this observation, we propose an adaptive method called Adaptive-SimRank to speed up similarity calculation. Using this method, we don't need to recalculate those converged pairs' similarity. The experiments conducted on web datasets and synthetic dataset show that our new method can reduce the running time by nearly 35%.

关键词： Similarity Calculation Linkage Mining

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Region extreme value query in sensor networks

Region extreme value query in sensor networks

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6th International Conference on Fuzzy Systems and knowledge Discovery, FSKD 2009

作者： Wu, Zhongbo Chen, Hong School of Information Renmin University of China Beijing 100872 China MOE Key Lab. of Data Engineering and Knowledge Engineering Beijing 100872 China

ISBN: (纸本)9780769537351

Existing research on extreme value query in wireless sensor networks is mainly focus on finding out sensors with highest metric. Yet in most actually scenarios, people cares more about special network regions than detailed metric of some single nodes. We put forward region extreme query in sensor networks which don't consider a sensor node but a region as query unit. We formally define region extreme value query and discuss how they can be expressed using the SQL. Then we devise a distributed algorithm named DCA for in-network evaluation of region extreme value query in dynamic region, aiming at the minimization of power consumption. Experiments show that DCA has good performance. © 2009 IEEE.

关键词： Sensor nodes

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Assessing the Influence Probability between Objects: A Random Walker Approach

Assessing the Influence Probability between Objects: A Rando...

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IEEE Symposium on Computational Intelligence and data Mining

作者： Li, Pei Li, Zhixu He, Jun Du, Xiaoyong Liu, Hongyan Key Labs of Data Engineering and Knowledge Engineering MOE China and School of Information Renmin University of China Beijing China Department of Management Science and Engineering Tsinghua University Beijing China

ISBN: (纸本)9781424427659

Influence between objects needs to be assessed in many applications. Lots of measures have been proposed, but a domain-independent method is still expected. In this paper, we give a probabilistic definition of influence based on the random walker model on graphs. Two approaches, linear systems method and Basic InfRank algorithm, are shown and return equal results, but Basic InfRank is more efficient by iterative computation. Two variants on bipartite graphs and star graphs are discussed. Experiments show InfRank algorithms have good accuracy, fast convergent rate and high performance.

关键词： Linear systems

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MrCAR: A Multi-relational Classification Algorithm based on Association Rules

MrCAR: A Multi-relational Classification Algorithm based on ...

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International Conference on Web Information Systems and Mining

作者： Gu, Yingqin Liu, Hongyan He, Jun Hu, Bo Du, Xiaoyong Key Labs. of Data Engineering and Knowledge Engineering MOE China Information School Renmin University of China Beijing 100872 China School of Economics and Management Tsinghua University Beijing 100084 China

ISBN: (纸本)9780769538174

Classification is an important subject in data mining and machine learning, which has been studied extensively and has a wide range of applications. Classification based on association rules is one of the most effective classification method, whose accuracy is higher and discovered rules are easier to understand comparing with classical classification methods. However, current algorithms for classification based on association rules is single table oriented, which means they can only apply to the data stored in a single relational table. Directly applying these algorithms in multi-relational data environment will result in many problems. This paper proposes a novel algorithm MrCAR for classification based on association rules in multi-relational data environment. MrCAR mines relevant features in each table to predict the class label. Close itemsets technique and Tuple ID Propagation method are used to improve the performance of the algorithm. Experimental results show that MrCAR has higher accuracy and better understandability comparing with a typical existing multirelational classification algorithm.

关键词： Association rules

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Efficient Computation of Reverse Skyline on data Stream

Efficient Computation of Reverse Skyline on Data Stream

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The Second International Joint Conference on Computational Science and Optimization(CSO 2009)(2009 国际计算科学与优化会议)

作者： Ling Zhu Cuiping Li Hong Chen School of Information Renmin University of China Key Lab of Data Engineering and Knowledge Engineering of MOE

Reverse Skyline Queries have been proved very useful in business location, environmental monitoring and some other applications. In this paper, we consider reverse skyline queries processing on data stream, which provides continuous, high-speed data elements. Specifically, we consider the latest objects in the sliding window. The challenge is that it is difficult to maintain a multidimensional index (for example, R-tree) in a dynamic dataset. Focusing on this challenge, we propose an algorithm with a DC-Tree as index and effective pruning methods to reduce the search space of query processing and the cost of index maintaining. Extensive experiments show that our algorithms are efficient and effective for on-line reverse skyline query.

关键词： Monitoring Query processing Costs data engineering knowledge engineering Multidimensional systems

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Incremental computation for MEDIAN cubes in what-if analysis

Incremental computation for MEDIAN cubes in what-if analysis

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Joint International Conference on Advances in data and Web Management, APWeb/WAIM 2009

作者： Xiao, Yanqin Zhang, Yansong Wang, Shan Chen, Hong Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China Computer Center Hebei University Baoding Hebei 071002 China Department of Computer Science Harbin Finance College Harbin 150030 China

ISBN: (纸本)9783642006715

What-if analysis is an important type of DSS analysis processing procedure. It analyzes hypothetical scenarios based on historical data. The data cube view must be updated when the what-if condition is changed. Since source data must be kept in order to compute the new aggregate value when new tuples are inserted or deleted, in what-if analysis, incrementally computing a data cube for holistic aggregation functions is a difficult problem. In this paper, we adopt delta cube strategy and work area technique to incrementally compute data cube for MEDIAN function. The size of work area has important influence on the efficiency of the incremental computing. This paper optimizes the size of work area based on the number and the cardinality of dimension attributes of the cuboid. Performance study shows that our algorithms are effective over large databases. © Springer-Verlag Berlin Heidelberg 2009.

关键词： Geometry

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