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The 2014 10th International Conference on Natural Computation (ICNC 2014) and the 2014 11th International Conference on Fuzzy Systems and knowledge Discovery (FSKD 2014)(第十届自然计算和第十一届模糊系统与知识发现国际会议)

作者： He Hu Xiaoyong Du School of Information Renmin University of China Key Laboratory of Data Engineering and Knowledge EngineeringMOE BeijingChina

ISBN: (纸本)9781479951499

The information on the Web is usually fabricated to be understandable by human users rather than ***'s not easy to automatically catalogue and extract the Web information solely with a software *** on these observations,we present an approach that uses human guided operations to automatically generate a PQL query,a SQL like query language focusing on Web pages,to extract the interested information fragments on Web *** PQL query uses XPath expressions to locating the target HTML *** develop a K-Medoid clustering algorithm to process PQL queries to generate the structural *** extracted information is structured as a relational table(in CSV format)which can be manipulated smoothly with spreadsheet software or a relational DBMS system.

关键词： PQL Structural Extraction Browser Extension

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Mining Approximate Closed Frequent Itemsets over Stream

Mining Approximate Closed Frequent Itemsets over Stream

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ACIS International Conference on Software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Haifeng Li Zongjian Lu Hong Chen School of Information Key Laboratory of Data Engineering and Knowledge EngineeringMOEg Renmin University of China Beijing China

ISBN: (纸本)9780769532636

Frequent itemset mining is a very important problem in data mining. Closed frequent itemsets is the condensed representation of frequent itemsets thus spend less memory, so it is much suitable for stream mining. But on the other hand, when the minimum support is much lower, the size of closed frequent itemsets turns larger, which makes the performance reduced a lot. In this paper, we introduce a threshold to approximately mine closed frequent itemsets with a limited error tolerance. A new algorithm named ACFIM is proposed based on the introduction of the distance conception to mine the sliding window of stream, in which more data are pruned and more computation time are saved, so it much raise the performance in running time and memory comparing to the state-of-art closed frequent itemsets mining methods. Our experimental results over real-life datasets show that ACFIM is effective and efficient.

关键词： Itemsets data mining Distance measurement Pediatrics Approximation algorithms Approximation methods databases

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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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Geometric Routing Precluding Loops and Dead Ends in 3-D Wireless Sensor Networks

Geometric Routing Precluding Loops and Dead Ends in 3-D Wire...

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2010 IEEE Global Telecommunications Conference GLOBECOM 2010

作者： Jun Duan Deying Li Wenping Chen Key Laboratory of Data Engineering and Knowledge Engineering MOE School of Information Renmin University of China Beijing China

Numerous algorithms on geometric networks has been studied, and most of them were based on 2-dimensional networks. But 2-dimensional geometric routing algorithms cannot be directly adapted to the 3-dimensional networks. In this paper, we propose routing algorithms based on the iteration of specific angles on the networks of Delaunay Triangulation in 3D space, and prove the certainty of data transmission of our routing algorithms. In the algorithms, the messages only need to carry information of O(1) nodes and each node just keeps 1-hop neighbors' information.

关键词： Routing Peer to peer computing Three dimensional displays Algorithm design and analysis Compass Wireless sensor networks Relays

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Research on Web Services Selection Model Based on AHP

Research on Web Services Selection Model Based on AHP

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2008 IEEE International Conference on Service Operations and Logistics, and Informatics(IEEE/SOLI’2008)(IEEE服务运作、物流与信息年会)

作者： Bei Wu Meiyun Zuo Shijuan Wang Information School and Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China BeijingChina

This paper focuses on the problem that how to select the optimal service among many Web services which all meet the functional needs,establishes an index system for Web services products selection from four aspects,namely the supply side,the user,product and *** on this,we collect the views of 30 experts by Analytic Hierarchy Process (AHP) method and calculate the weight of each index at all levels based on the data collected from questionnaire *** the overall sample data analysis,we put two types of sample data namely business operation experts and academics for comparative *** Web services selection model proposed in this article can provide the reference to Web services managers when they selecting Web services,and also contributes to in-depth research on the adoption of Web services based information system.

关键词： web services selection model analytic hierarchy process information system

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Cutting Learned Index into Pieces: An In-depth Inquiry into Updatable Learned Indexes

Cutting Learned Index into Pieces: An In-depth Inquiry into ...

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International Conference on data engineering

作者： Jiake Ge Boyu Shi Yanfeng Chai Yuanhui Luo Yunda Guo Yinxuan He Yunpeng Chai Key Laboratory of Data Engineering and Knowledge Engineering MOE China School of Information Renmin University of China China

Numerous high-performance updatable learned indexes have recently been designed to support the writing requirements in practical systems. Researchers have proposed various strategies to improve the availability of updatable learned indexes. However, it is unclear which strategy is more profitable. Therefore, we deconstruct the design of learned indexes into multiple dimensions and in-depth evaluate their impacts on the overall performance, respectively. Through the in-depth exploration of learned indexes, we reckon that the approximation algorithm is the most crucial design dimension for improving the performance of the learned indexes rather than the popular works that focus on the learned index structure. Moreover, this paper makes a comprehensive end-to-end evaluation based on a high-performance key-value store to answer people’s concerns about which learned index is better and whether learned indexes can outperform traditional ones. Finally, according to end-to-end and in-depth evaluation results, we give some constructive suggestions on designing a better learned index in these dimensions, especially how to design an excellent approximate algorithm to improve the lookup and insertion performance of learned indexes.

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Optimizing Random Access to Hierarchically-Compressed data on GPU

Optimizing Random Access to Hierarchically-Compressed Data o...

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Supercomputing Conference

作者： Feng Zhang Yihua Hu Haipeng Ding Zhiming Yao Zhewei Wei Xiao Zhang Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering (MOE) School of Information Renmin University of China Beijing China

ISBN: (纸本)9781665454452

GPU's powerful computational capacity holds great potentials for processing hierarchically-compressed data without decompression in data science domain. Unfortunately, existing GPU approaches offer only traversal-based data analytics; random access is extremely inefficient, substantially limiting their utility. To solve this problem, we develop a novel and broadly applicable optimization that enables efficient random access to hierarchically-compressed data without decompression in GPU memory. We address three major challenges for enabling efficient random access to compressed data on GPUs. The first challenge is designing GPU data structures that support random access. The second challenge is efficiently generating data structures on GPU. Generating data structures for random access is costly on the CPU, and the inefficiency increases dramatically when PCIe data transmission is incorporated. The third challenge is query processing on compressed data in GPU memory. Random accesses, including data updates, result in significant conflicts between massive threads. To solve the first challenge, we propose and modify a number of compressed data structures, including indexing within the complicated GPU memory hierarchy. To address the second challenge, we develop a two-phase process for generating these data structures on the GPU. To handle the third challenge, we propose a double-parsing design to avoid data conflicts. We evaluate our solution on two GPU platforms using five real-world datasets. Experiments show that the random access operations on GPU can achieve 65.04x average speedup compared to the state-of-the-art method.

关键词： Limiting Query processing Instruction sets High performance computing Memory management Graphics processing units data science

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Improve Frequent Closed Itemsets Mining over data Stream with Bitmap

Improve Frequent Closed Itemsets Mining over Data Stream wit...

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ACIS International Conference on Software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

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

ISBN: (纸本)9780769532639

Frequent itemsets mining is an important problem in data mining. Frequent closed itemsets mining provides complete and condensed information for frequent pattern analysis thus reduces the memory cost without accuracy loss. More research focus on stream mining with the more application of stream. Stream is fast and unlimited thus data had to be stored in limited memory, how to save running time and memory usage is the most important target. In this paper, we propose an improved frequent closed itemsets mining method based on traditional stream mining algorithm CFI-stream with bitmap coding named CLIMB (closed itemset mining with bitmap) over stream's sliding window. The distinct items are maintained in memory in lexicographic order and each itemset is coded to bit-sequence with the order of items, moreover, the bit-sequence is split into sections to be recoded to reduce the memory cost. The experimental results on real-life show that CLIMB algorithm is effective and efficient.

关键词： Itemsets data mining Encoding Decoding data structures Runtime Algorithm design and analysis

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Advances in Web and Network Technologies and Information Management 2009

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丛书名： Lecture Notes in Computer Science

2009年

作者： Lei Chen Chengfei Liu Xiao Zhang Shan Wang Darijus Strasunskas Stein L. Tomassen Jinghai Rao Wen-Syan Li K. Selçuk Candan Dickson K. W. Chiu Yi Zhuang

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AN EXTENDED MODEL FOR PROJECT PORTFOLIO SELECTION WITH PROJECT DIVISIBILITY AND INTERDEPENDENCY

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Journal of Systems Science and Systems engineering 2016年第1期25卷 119-138页

作者： Xingmei Li Shu-Cherng Fang Xiaoling Guo Zhibin Deng Jianxun Qi School of Economics and Management North China Electric Power University Beijing 102206 China Industrial and Systems Engineering Department North Carolina State University Raleigh NC 27695 USA College of Engineering andlnformation Technology University of Chinese Academy of Sciences Beijing100049 China School of Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

In this paper, we develop an extended model for the project portfolio selection problem over a planning horizon with multiple time periods. The model incorporates the factors of project divisibility and interdependency at the same time for real-life applications. The project divisibility is considered as a strategy, not an unfortunate event as in the literature, in choosing the best execution schedule for the projects, and the classical concept of＂project interdependencies＂ among fully executed projects is then extended to the portions of executed projects. Additional constraints of reinvestment consideration, setup cost, cardinality restriction, precedence relationship and scheduling are also included in the model. For efficient computations, an equivalent mixed integer linear programming representation of the proposed model is derived. Numerical examples under four scenarios are presented to highlight the characteristics of the proposed model. In particular, the positive effects of project divisibility are shown for the first time.

关键词： Project management project portfolio selection problem project divisibility project interdependency

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