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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

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Efficient Duplicate Detection on Cloud Using a New Signature Scheme

Efficient Duplicate Detection on Cloud Using a New Signature...

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12th International Conference on Web-Age Information Management

作者： Rong, Chuitian Lu, Wei Du, Xiaoyong Zhang, Xiao Key Labs. of Data Engineering and Knowledge Engineering MOE China School of Information Renmin University of China China Shanghai Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)9783642235344;9783642235351

Duplicate detection has been well recognized as a crucial task to improve the quality of data. Related work on this problem mainly aims to propose efficient approaches over a single machine. However, with increasing volume of the data, the performance to identify duplicates is still far from satisfactory. Hence, we try to handle the problem of duplicate detection over MapReduce, a share-nothing paradigm. We argue the performance of utilizing MapReduce to detect duplicates mainly depends on the number of candidate record pairs. In this paper, we proposed a new signature scheme with new pruning strategy over MapReduce to minimize the number of candidate record pairs. Our experimental results over both real and synthetic datasets demonstrate that our proposed signature based method is efficient and scalable.

关键词： duplicate detection MapReduce Cloud

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Hadamard Encoding Based Frequent Itemset Mining under Local Differential Privacy

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Journal of Computer Science & Technology 2023年第6期38卷 1403-1422页

作者：赵丹赵素云陈红刘睿瑄李翠平张晓莹 Institute of Scientific and Technical Information of China Beijing 100038China Key Laboratory of Data Engineering and Knowledge Engineering(Ministry of Education) School of InformationRenmin University of ChinaBeijing 100872China

Local differential privacy(LDP)approaches to collecting sensitive information for frequent itemset mining(FIM)can reliably guarantee *** current approaches to FIM under LDP add"padding and sampling"steps to obtain frequent itemsets and their frequencies because each user transaction represents a set of *** current state-of-the-art approach,namely set-value itemset mining(SVSM),must balance variance and bias to achieve accurate ***,an unbiased FIM approach with lower variance is highly *** narrow this gap,we propose an Item-Level LDP frequency oracle approach,named the Integrated-with-Hadamard-Transform-Based Frequency Oracle(IHFO).For the first time,Hadamard encoding is introduced to a set of values to encode all items into a fixed vector,and perturbation can be subsequently applied to the *** FIM approach,called optimized united itemset mining(O-UISM),is pro-posed to combine the padding-and-sampling-based frequency oracle(PSFO)and the IHFO into a framework for acquiring accurate frequent itemsets with their ***,we theoretically and experimentally demonstrate that O-UISM significantly outperforms the extant approaches in finding frequent itemsets and estimating their frequencies under the same privacy guarantee.

关键词： local differential privacy frequent itemset mining frequency oracle

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Improved head-driven statistical models for natural language parsing

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Journal of Central South University 2013年第10期20卷 2747-2752页

作者：袁里驰 School of Information Technology Jiangxi University of Finance and Economics Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics

Head-driven statistical models for natural language parsing are the most representative lexicalized syntactic parsing models, but they only utilize semantic dependency between words, and do not incorporate other semantic information such as semantic collocation and semantic category. Some improvements on this distinctive parser are presented. Firstly, "valency" is an essential semantic feature of words. Once the valency of word is determined, the collocation of the word is clear, and the sentence structure can be directly derived. Thus, a syntactic parsing model combining valence structure with semantic dependency is purposed on the base of head-driven statistical syntactic parsing models. Secondly, semantic role labeling(SRL) is very necessary for deep natural language processing. An integrated parsing approach is proposed to integrate semantic parsing into the syntactic parsing process. Experiments are conducted for the refined statistical parser. The results show that 87.12% precision and 85.04% recall are obtained, and F measure is improved by 5.68% compared with the head-driven parsing model introduced by Collins.

关键词： valence structure semantic dependency head-driven statistical syntactic parsing semantic role labeling

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Efficient SPARQL query evaluation via automatic data partitioning

Efficient SPARQL query evaluation via automatic data partiti...

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18th International Conference on database Systems for Advanced Applications, DASFAA 2013

作者： Yang, Tao Chen, Jinchuan Wang, Xiaoyan Chen, Yueguo Du, Xiaoyong School of Information Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE China

ISBN: (纸本)9783642374494

The volume of RDF data increases very fast within the last five years, e.g. the Linked Open data cloud grows from 2 billions to 50 billions of RDF triples. With its wonderful scalability, cloud computing platform like Hadoop is a good choice for processing queries over large data sets. Previous works on evaluating SPARQL queries with Hadoop mainly focus on reducing the number of joins through careful split of HDFS files and algorithms for generating Map/Reduce jobs. However, the way of partitioning RDF data could also affect the performance. Specifically, a good partitioning will greatly reduce or even totally avoid cross-node joins and significantly reduce the cost of query evaluation. Based on HadoopDB, this work processes SPARQL queries in a hybrid architecture where Map/Reduce takes charge of the computing tasks and an RDF query engine, RDF-3X, stores the data and evaluates join operations over local data. Based on analysis of query work-loads, we propose a novel algorithm for automatically partitioning RDF data. We also present an approximate solution to physically place the partitions in order to reduce data redundancy. All the proposed approaches are evaluated by extensive experiments over large RDF data sets. © Springer-Verlag 2013.

关键词： Resource Description Framework (RDF)

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Enhancing Storage Efficiency and Performance: A Survey of data Partitioning Techniques

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Journal of Computer Science & Technology 2024年第2期39卷 346-368页

作者：刘鹏举李翠平陈红 Distinguished Member CCF 1.School of InformationRenmin University of ChinaBeijing 100872China Key Laboratory of Data Engineering and Knowledge Engineering of the Ministry of Education Beijing 100872China

data partitioning techniques are pivotal for optimal data placement across storage devices,thereby enhancing resource utilization and overall system ***,the design of effective partition schemes faces multiple challenges,including considerations of the cluster environment,storage device characteristics,optimization objectives,and the balance between partition quality and computational ***,dynamic environments necessitate robust partition detection *** paper presents a comprehensive survey structured around partition deployment environments,outlining the distinguishing features and applicability of various partitioning strategies while delving into how these challenges are *** discuss partitioning features pertaining to database schema,table data,workload,and runtime *** then delve into the partition generation process,segmenting it into initialization and optimization stages.A comparative analysis of partition generation and update algorithms is provided,emphasizing their suitability for different scenarios and optimization ***,we illustrate the applications of partitioning in prevalent database products and suggest potential future research directions and *** survey aims to foster the implementation,deployment,and updating of high-quality partitions for specific system scenarios.

关键词： data partitioning survey partitioning feature partition generation partition update

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Causality-driven attribute editing with bidirectional generative network

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Expert Systems with Applications 2025年 284卷

作者： Dianlong You Zhao Yu Jiawei Shen Zexuan Li Zhen Chen Xindong Wu School of Information Science and Engineering Yanshan University Qinhuangdao 066004 Hebei China The Key Laboratory for Software Engineering of Hebei Province Yanshan University Qinhuangdao 066004 Hebei China The Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei 230009 Anhui China

Existing attribute editing methods typically assume that the attributes in an image are independent, which suffers from some dilemmas in real situations: causing irrelevant changes, neglecting causal transmissions, and lower reconstruction performance, resulting in a lack of rationality in the generated images. To address the above issues, we propose the CAE-BGN model with causal representation and attribute editing modules. CAE-BGN has a three-fold main idea: 1) utilizing prior knowledge to model non-linear causal relationships between attributes rather than treating them as mutually independent; 2) exploiting a bidirectional generative network to optimize causal relationships and get causal representation, and 3) using an encoder-decoder architecture integrated coordinate attention and skip connection to locate edited regions and enhance the quality of generated images. The results demonstrate that CAE-BGN achieves significantly better performance than its rivals. To elaborate on our CAE-BGN model’s effectiveness, a real-world case study on season translation is conducted. Code is released at https://***/youdianlong/CAE-BGN .

关键词： Attribute editing Bidirectional generative network Causal representation Coordinate attention

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A Part-of-speech Tagging Model Employing Word Clustering and Syntactic Parsing

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Chinese Journal of Electronics 2014年第1期23卷 109-114页

作者： YUAN Lichi School of Information Technology Jiangxi University of Finance and Economics Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics

Part-Of-Speech tagging is a basic task in the field of natural language processing. This paper builds a POS tagger based on improved Hidden Markov model,by employing word clustering and syntactic parsing ***, In order to overcome the defects of the classical HMM, Markov family model(MFM), a new statistical model was introduced. Secondly, to solve the problem of data sparseness, we propose a bottom-to-up hierarchical word clustering algorithm. Then we combine syntactic parsing with part-of-speech tagging. The Part-ofSpeech tagging experiments show that the improved PartOf-Speech tagging model has higher performance than Hidden Markov models(HMMs) under the same testing conditions, the precision is enhanced from 94.642% to97.235%.

关键词： Part-of-speech tagging Hidden Markov model Markov family model Word clustering

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Multi-Cluster Feature Selection Based on Isometric Mapping

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 570-572页

作者： Yadi Wang Zefeng Zhang Yinghao Lin Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng 475004 Institute of Data and Knowledge Engineering School of Computer and Information EngineeringHenan UniversityKaifeng 475004China

Dear editor,This letter presents an unsupervised feature selection method based on machine *** selection is an important component of artificial intelligence,machine learning,which can effectively solve the curse of d... 详细信息

关键词： problem letter dimensionality

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Operating system mechanisms for TPM-based lifetime measurement of process integrity

Operating system mechanisms for TPM-based lifetime measureme...

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2009 IEEE 6th International Conference on Mobile Adhoc and Sensor Systems, MASS '09

作者： Xiao, Li Wenchang, Shi Zhaohui, Liang Bin, Liang Zhiyong, Shan Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University) Ministry of Education

ISBN: (纸本)9781424451135

Implementing runtime integrity measurement in an acceptable way is a big challenge. We tackle this challenge by developing a framework called Patos. This paper discusses the design and implementation concepts of our operating system mechanisms for runtime process integrity measurement, which is an important part of the Patos framework and is named Patos-RIP. Patos-RIP is developed into the mainstream Linux operating system and utilizes TPM as hardware support for tamper-resistance. From the beginning a process is created to the moment the process dies, Patos-RIP conducts integrity measurement at appropriate points of time when the process runs, so as to ensure that the integrity of a process is not compromised during its whole lifetime. This way, Patos-RIP can improve trustworthiness of processes by effectively detecting runtime tampering attacks on processes' integrity. ©2009 IEEE.

关键词： Computer operating systems

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