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20th ACM Conference on Information and knowledge Management, CIKM'11

作者： Cui, Jianwei Liu, Hongyan Yan, Jun Ji, Lei Jin, Ruoming He, Jun Gu, Yingqin Chen, Zheng Du, Xiaoyong Key Labs. of Data Engineering and Knowledge Engineering School of Information Renmin University of China China Department of Management Science and Engineering Tsinghua University China Microsoft Research Asia Beijing China Computer Science Department Kent State University United States

ISBN: (纸本)9781450307178

Search engine users often have clear search tasks hidden behind their queries. Inspired by this, the modern search engines are providing an increasing number of services to help users simplify their key tasks. However, the problem of what are the major user search tasks with high traffic for which search engines should design special services is still underexplored. In this paper, we propose a novel Multi-view Random Walk (MRW) algorithm to measure the search task oriented similarity between queries, and then group search queries with similar tasks so that the major search tasks of users can be identified from search engine click-through log. The proposed MRW, which is a general framework to combine knowledge from different views in a random walk process, allows the random surfer to walk across different views to integrate information for search task discovery. Experimental results on click-through log of a commonly used commercial search engine show that our proposed MRW algorithm can effectively discover user search tasks. © 2011 ACM.

关键词： Search engines

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Discovering Chinese compound term using termhood and unithood measures

Proceedings - 2011 6th Annual ChinaGrid Conference, ChinaGri...

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Proceedings - 2011 6th Annual ChinaGrid Conference, ChinaGrid 2011 2011年 60-67页

作者： Kang, Jingjing Liu, Tao Hu, He Du, Xiaoyong Key Labs. of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China

ISBN: (纸本)9780769544724

Domain terms play a crucial role in many research areas, which has led to a rise in demand for automatic domain terms extraction. In this paper, we present a two-level evaluation approach based on term hood and unit hood to extract Chinese domain compound terms automatically, which takes the character-level and word-level information into account. To achieve this, we incorporate semantic features by using the word segmentation to recognize single word terms, then leverage the improved C-value and heuristic methods such as word formation pattern and word formation power to evaluate candidates at both levels. By validating our approach with several existing dictionaries, a significant improvement of compound terms detection is achieved. Experiments in legal corpus show our method is superior over other compared methods. © 2011 IEEE.

关键词： Heuristic methods

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SeCCX: Semantics-based fine granular concurrency control for XML data

SeCCX: Semantics-based fine granular concurrency control for...

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1st International Workshop on Graph database, IWGD 2010, 1st International Workshop on Advanced Techniques on XML data Management, XMLDM 2010, 2nd International Workshop on WCMT 2010, Held in Conj. with WAIM 2010

作者： Rong, Chuitian Lu, Wei Zhang, Xiao Liu, Zhen Du, Xiaoyong Key Labs. of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China China

ISBN: (纸本)3642167195

This paper proposes a new locking protocol, SeCCX, for isolation of concurrent transactions on XML data. This protocol adopts the semantics of operations issued by users. Comparing with previous XML locking protocols, SeCCX has some new features, like richer lock modes, finer granular, lower lock confliction and dynamic lock escalation. The importance is that it locks the objects according to the semantics of user's operation and sets different restrictions on different objects. It provides finer granular concurrency control such that the overall system performance can be improved. We prove that the SeCCX protocol is conflict serializability. We also compare it with other protocols to demonstrate the advantages of SeCCX. © 2010 Springer-Verlag.

关键词： Locks (fasteners)

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FlexTable: Using a Dynamic Relation Model to Store RDF data

FlexTable: Using a Dynamic Relation Model to Store RDF Data

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15th International Conference on database Systems for Advanced Applications

作者： Wang, Yan Du, Xiaoyong Lu, Jiaheng Wang, Xiaofang Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China Information School Renmin University of China China

ISBN: (纸本)9783642120251

Efficient management of RDF data is an important factor in realizing the Semantic Web vision. The existing approaches store RDF data based on triples instead of a relation model. In this paper, we propose a system called FlexTable, where all triples of an instance are coalesced into one tuple and all tuples are stored in relation schemas. The main technical challenge is how to partition all the triples into several tables, i.e. it is needed to design an effective and dynamic schema structure to store RDF triples. To deal with this challenge, we firstly propose a schema evolution method called LBA, which is based on a lattice structure to automatically evolve schemas while new triples are inserted. Secondly, we propose a novel page layout with an interpreted storage format to reduce the physical adjustment cost during schema evolution. Finally we perform comprehensive experiments on two practical RDF data sets to demonstrate that FlexTable is superior to the state-of-the-art approaches.

关键词： FlexTable RDF data dynamic relational model Lattice

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Detecting Hot Events from Web Search Logs

Detecting Hot Events from Web Search Logs

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11th International Conference on Web-Age Information Management (WAIM 2010)

作者： Gu, Yingqin Cui, Jianwei Liu, Hongyan Jiang, Xuan He, Jun Du, Xiaoyong Li, Zhixu Key Labs. of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing 100872 China Department of Management Science and Engineering Tsinghua University Beijing 100084 China

ISBN: (纸本)9783642142451

Detecting events from web resources is a challenging task, attracting many attentions in recent years. Web search log is an important data source for event detection because the information it contains reflects users' activities and interestingness to various real world events. There are three major issues for event detection from web search logs: effectiveness, efficiency and the organization of detected events. In this paper, we develop a novel Topic and Event Detection method, TED, to address these issues. We first divide the whole data into topics for efficiency consideration, and then incorporate link information, temporal information and query content to ensure the quality of detected events. Finally, events detected are organized through the proposed interestingness measure as well as topics they belong to. Experiments are conducted on a commercial search engine log. The results demonstrate that our method can effectively and efficiently detect hot events and give a meaningful organization of them.

关键词： Event detection click-through data efficient algorithm

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A fast two-stage algorithm for computing SimRank and its extensions

A fast two-stage algorithm for computing SimRank and its ext...

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作者： Jia, Xu Liu, Hongyan Zou, Li He, Jun Du, Xiaoyong Key Labs. of Data Engineering and Knowledge Engineering Ministry of Education China Department of Computer Science Renmin University of China 100872 China Department of Management Science and Engineering Tsinghua University 100084 China

ISBN: (纸本)3642167195

Similarity estimation can be used in many applications such as recommender system, cluster analysis, information retrieval and link prediction. SimRank is a famous algorithm to measure objects' similarities based on link structure. We observe that if one node has no in-link, similarity score between this node and any of the others is always zero. Based on this observation, we propose a new algorithm, fast two-stage SimRank (F2S-SimRank), which can avoid storing unnecessary zeros and can accelerate the computation without accuracy loss. Under the circumstance of no accuracy loss, this algorithm uses less computation time and occupies less main memory. Experiments conducted on real and synthetic datasets demonstrate the effectiveness and efficiency of our F2S-SimRank. © 2010 Springer-Verlag.

关键词： Cluster analysis

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Law text clustering based on referential relations

Law text clustering based on referential relations

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5th Annual ChinaGrid Conference, ChinaGrid 2010

作者： Fan, Biao Liu, Tao Hu, He Du, Xiaoyong Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China Beijing China

ISBN: (纸本)9780769541068

This paper proposes a new method to cluster law texts based on referential relation of laws. We extract law entities (an entity represents a law) and their referential relation from law texts. Then SimRank algorithm is applied to calculate law entity's similarity through referential relation and law clustering is carried out based on the SimRank similarity. This is the first time to apply SimRank algorithm in the domain of Law and use it to carry out text clustering. Prototype and experiments show that our solution is feasible. We also publish the extracted data as Linked Law data with RDF data model, which forms the first open semantic web database in Law domain. Linked Law data enables user to access law data with rich data links and query web data by application interface of Semantic Web. © 2010 IEEE.

关键词： Cluster analysis

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Efficient common items extraction from multiple sorted lists

Efficient common items extraction from multiple sorted lists

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12th International Asia Pacific Web Conference, APWeb 2010

作者： Lu, Wei Rong, Chuitian Chen, Jinchuan Du, Xiaoyong Cheong Fung, Gabriel Pui Zhou, Xiaofang School of Information Renmin University of China China Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China School of Computing Informatics Arizona State University United States School of ITEE University of Queensland Australia

ISBN: (纸本)9780769540122

Given a set of lists, where items of each list are sorted by the ascending order of their values, the objective of this paper is to figure out the common items that appear in all of the lists efficiently. This problem is sometimes known as common items extraction from sorted lists. To solve this problem, one common approach is to scan all items of all lists sequentially in parallel until one of the lists is exhausted. However, we observe that if the overlap of items across all lists is not high, such sequential access approach can be significantly improved. In this paper, we propose two algorithms, MergeSkip and MergeESkip, to solve this problem by taking the idea of skipping as many items of lists as possible. As a result, a large number of comparisons among items can be saved, and hence the efficiency can be improved. We conduct extensive analysis of our proposed algorithms on one real dataset and two synthetic datasets with different data distributions. We report all our findings in this paper. © 2010 IEEE.

关键词： Problem solving

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Local methods for estimating SimRank score

Local methods for estimating SimRank score

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12th International Asia Pacific Web Conference, APWeb 2010

作者： Jia, Xu Liu, Hongyan Zou, Li He, Jun Du, Xiaoyong Cai, Yuanzhe Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China Department of Computer Science Renmin University of China 100872 China Department of Management Science and Engineering Tsinghua University 100084 China Department of Computer Science University of Texas Arlington TX 76019 United States

ISBN: (纸本)9780769540122

SimRank is a well known algorithm which conducts link analysis to measure similarity between each pair of nodes (nodepair). But it suffers from high computational cost, limiting its usage in large-scale datasets. Moreover, Links between nodes are changing over time. It may be desirable to quickly approximate the similarity score between certain nodepair without performing a large-scale computation on the entire graph. In our approach we propose a method to efficiently estimate the similarity score using only a small subgraph of the entire graph. We call this novel algorithm "Local-SimRank". The experimental results conducted on real datasets and synthetic dataset show that our algorithm efficiently produces good approximations to the global SimRank scores. Meanwhile, we prove that the Local-SimRank score LS(a, b) is always less than original SimRank score S(a, b) mathematically. © 2010 IEEE.

关键词： Approximation algorithms

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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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