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12th International Asia Pacific Web Conference, APWeb 2010

作者： An, Mingyuan Wang, Yang Wang, Weiping Key Laboratory of Computer System and Architecture Graduate University of Chinese Academy of Sciences Beijing China Key Laboratory of Computer System and Architecture Chinese Academy of Sciences Institute of Computing Technology Beijing China

ISBN: (纸本)9780769540122

MapReduce is a programming framework introduced by Google for large-scale data processing. It is usually used in a scan-centric fashion where all the data are split into blocks and Maps are generated for each block to scan and process the data in the block, then Reduces merge outputs from all the Maps. When a query intends to process only a subset of the data selected by a predicate, this brute-force method may cause extra I/O overhead spent on irrelevant data, and the overhead for initiating so many Maps may be nontrivial given that the actually interesting data for the query is comparatively small in volume. We propose an approach to integrate the index into the MapReduce execution in which only an appropriate number of Maps are generated, each of which accesses the data using an index. This approach incurs random I/O and remote access to data, so the overall performance depends on both system parameters and the query characteristics. We build a cost model for both this index access execution and the traditional full scan execution. This cost model can be used to choose between the two execution modes before executing a query. Experiments show that the index access execution can greatly outperform full scan execution when the selectivity of the predicate is low, and the cost model predicts the actual execution cost very well so can be used to determine the execution plan for a query. © 2010 IEEE.

关键词： MapReduce

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N-Docker: A NVM-HDD Hybrid Docker Storage Framework to Improve Docker Performance 16th

N-Docker: A NVM-HDD Hybrid Docker Storage Framework to Impro...

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16th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2019

作者： Gu, Lin Tang, Qizhi Wu, Song Jin, Hai Zhang, Yingxi Shi, Guoqiang Lin, Tingyu Rao, Jia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China State Key Laboratory of Intelligent Manufacturing System Technology Beijing100854 China The University of Texas at Arlington ArlingtonTX76019 United States

ISBN: (纸本)9783030307080

Docker has been widely adopted in production environment, but unfortunately deployment and cold-start of container are limited by the low speed of disk. The emerging non-volatile memory (NVM) technology, which has high speed and can store data permanently, brings a new chance to accelerate the deployment and cold-start of container. However, it is expensive to replace the whole hard disk driver (HDD) with NVM. To achieve the fastest deployment and cold-start with lowest cost, we conduct in-depth analysis on the Top-134 images in Docker Hub and obtain two main insights as: (1) the storing latency of layered image has become the bottleneck of container deployment;(2) only a few image layers are required for container cold-start. Based on these two findings, we propose a NVM-HDD hybrid docker storage framework as N-Docker. It can effectively accelerate container cold-start by detecting the bottleneck layers as well as cold-start required layers and storing them into NVM for faster container startup with limited NVM capacity. Experimental results show that N-Docker can accelerate the container deployment by 1.21X and cold-start by 2.96X. Compared to NVM-Docker, which stores all images into NVM, N-Docker achieves the same performance improvements while reducing the usage of NVM by 88.22%. © 2019, IFIP International Federation for Information Processing.

关键词： Containers

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Identity-Preserving Adversarial Training for Robust Network Embedding

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Journal of Computer Science & technology 2024年第1期39卷 177-191页

作者：岑科廷沈华伟曹婍徐冰冰程学旗 Data Intelligence System Research Center Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 101480China Beijing Academy of Artificial Intelligence Beijing 100000China Chinese Academy of Sciences Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Network embedding,as an approach to learning low-dimensional representations of nodes,has been proved extremely useful in many applications,e.g.,node classification and link ***,existing network embed-ding models are vulnerable to random or adversarial perturbations,which may degrade the performance of network em-bedding when being applied to downstream *** achieve robust network embedding,researchers introduce adversari-al training to regularize the embedding learning process by training on a mixture of adversarial examples and original ***,existing methods generate adversarial examples heuristically,failing to guarantee the imperceptibility of generated adversarial examples,and thus limit the power of adversarial *** this paper,we propose a novel method Identity-Preserving Adversarial Training(IPAT)for network embedding,which generates imperceptible adversarial exam-ples with explicit identity-preserving *** formalize such identity-preserving regularization as a multi-class classification problem where each node represents a class,and we encourage each adversarial example to be discriminated as the class of its original *** experimental results on real-world datasets demonstrate that our proposed IPAT method significantly improves the robustness of network embedding models and the generalization of the learned node representations on various downstream tasks.

关键词： network embedding identity-preserving adversarial training adversarial the example

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Efficient location-dependent skyline retrieval with peer-to-peer sharing

Efficient location-dependent skyline retrieval with peer-to-...

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15th Asia-Pacific Web Conference on Web Technologies and Applications, APWeb 2013

作者： Xiao, Yingyuan Shen, Yan Wang, Hongya Wang, Xiaoye Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Key Laboratory of Computer Vision and System Tianjin University of Technology 300384 China Donghua University 201620 Shanghai China

ISBN: (纸本)9783642374005

Motivated by the fact that a great deal of information is closely related to spatial location, a new type of skyline queries, location-dependent skyline query (LDSQ), was recently presented. Different from the conventional skyline query, which focuses on non-spatial attributes of objects and does not consider location relationship (i.e., proximity), LDSQ considers both the proximity and non-spatial attributes of objects. In this paper, we explore the problem of efficient LDSQ processing in a mobile environment. We propose a novel LDSQ processing method based on peer-to-peer (P2P) sharing. Extensive experiments are conducted, and the experimental results demonstrate the effectiveness of our methods. © 2013 Springer-Verlag.

关键词： Location based services

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Column-based RLE in row-oriented database

Column-based RLE in row-oriented database

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2009 International Conference on Cyber-Enabled Distributed computing and Knowledge Discovery, CyberC '09

作者： An, Mingyuan Key Laboratory of Computer System and Architecture Institute of Computing Technology Graduate University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781424452187

In database systems, disk I/O performance is usually the bottleneck of the whole query processing. Among many techniques, compression is one of the most important ones to reduce disk accesses so to improve system performance. RLE (Run-Length Encoding) is one light-weight compression algorithm which incurs negligible CPU cost. A lot of work show that, although RLE is one of the most effective compression techniques in column-oriented systems, it is very hard to use due to bad value locality in row-oriented systems where values from multiple attributes are stored in the same page. We propose CRLE (Column-based RLE), one compression algorithm to apply RLE to row-oriented data storage. On row-oriented storage page, CRLE can exploit value locality in individual column and encode values from the same column in run-length format. Experiments show that CRLE can lead to very good compression ratio and performance in spite of row-oriented data storage. © 2009 IEEE.

关键词： Query processing

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Rule-chain incremental mining algorithm based on directed graph

Rule-chain incremental mining algorithm based on directed gr...

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6th International Conference on Fuzzy systems and Knowledge Discovery, FSKD 2009

作者： Ning, Hongyun Zhang, Zhiying Zhang, Degan Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education Tianjin China

ISBN: (纸本)9780769537351

Aiming to discover the rule-chains directly, a novel graph-based online incremental mining algorithm (RIOMining) is proposed. Firstly, an interrelated bitmap based on a directed graph (PAGraph) is designed to compress the rule-chain information for storage. The compressed storage of the directed-graph effectively avoids repetitious database scanning and makes meaningful rules found more conveniently. Secondly, four theorems about discovery and pruning of frequent path in the graph are proved, so as to the rule-chains could be iteratively generated by searching incremental paths, while the theorems narrow the search scope and promote the performance by properly pruning and extending paths. It effectively reduces the redundancy calculation. Comparing it with other two fast mining algorithms, DLG and FP-growth, experimental result shows that it can mine rule-chains of high quality, especially, it is of real-time accuracy, and thereby it can be widely applied to dynamic network environment. © 2009 IEEE.

关键词： Directed graphs

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Security analysis and improvement for kerberos based on dynamic password and diffie-hellman algorithm

Security analysis and improvement for kerberos based on dyna...

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4th International Conference on Emerging Intelligent Data and Web Technologies, EIDWT 2013

作者： Wang, Chundong Feng, Chaoran Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin China

ISBN: (纸本)9780769550442

This paper analyses the process of Kerberos authentication protocol, and finds the existing security problems of the protocol referring to some Kerberos improvement projects proposed in some papers at home and abroad, and puts forward the thoughts and methods using dynamic password to improve the encryption security during the process of interaction between the client and Kerberos key distribution center, and makes passwords securely exchanged by using Diffile-Hellman key algorithm. © 2013 IEEE.

关键词： Cryptography

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Identification based on PRT gravity tunnel of continuous electrocardiogram 14

Identification based on PRT gravity tunnel of continuous ele...

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14th International Conference on Natural Computation, Fuzzy systems and Knowledge Discovery, ICNC-FSKD 2018

作者： Dai, Min Meng, Danyang Jia, Feng Ma, Miaoling Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

ISBN: (纸本)9781538680971

An electrocardiogram (ECG)identification method is proposed based on PRT triangle gravity tunnel of continuous electrocardiogram. The PRT center of gravity is defined by the P, R, T fiducial points in ECG signal, and the gravity tunnel of continuous PRT points is described as a matrix. The European distance is used to determine the similarity between two matrices. The performance of the proposed method is tested on MIT-BIH Database and the ECG data collected in practical environment from 25 healthy college students. Several group experiments were carried out and the average recognition accuracy is 99.87%. Comparing with the traditional correlation coefficient method, experimental results show that the average recognition accuracy is increased 1.66% when using the proposed method. © 2018 IEEE.

关键词： Electrocardiograms

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Collaborative wi-fi fingerprinting indoor positioning using near relation 24

Collaborative wi-fi fingerprinting indoor positioning using ...

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2020 24th ISPRS Congress - Technical Commission IV on Spatial Information Science

作者： Wang, Y. Wang, W. Li, X. Zhang, W. Guo, R. National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Urban Informatics Shenzhen Key Laboratory of Spatial Smart Sensing and Services Research Institute for Smart Cities School of Architecture and Urban Planning Shenzhen University Shenzhen China

Indoor positioning is of great importance to the era of mobile computing. Currently, much attention has been paid to RSS-based location for that it can provide position information without additional equipment. However, this method suffers from many challenges: (1) fingerprint ambiguity;(2) labor-intensive of fingerprint collection;(3) low efficiency of fingerprint matching. To get over these drawbacks, we provide a collaborative WiFi fingerprinting indoor positioning method using near relation. The base idea of this method is that interpolation method is used to enrich sparse Wi-Fi fingerprint. Near relation boundary is provided and Wi-Fi fingerprints is constrained to this region to reduce fingerprint ambiguity, which also can improve the efficiency of fingerprint matching. Extensive experiments show that a positioning accuracy of 3.8m can be achieved with the near relation under 1m interpolation density. © 2020 Authors.

关键词： Efficiency

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Data protection and provenance in cloud of things environment: Research challenges

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International Journal of Information and Computer Security 2020年第4期12卷 416-435页

作者： Wang, Chundong Yang, Lei Guo, Hao Wan, Fujin Key Laboratory of Computer Vision and System Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Ministry of Education Tianjin University of Technology Tianjin China Global Energy Internet Research Institute Beijing University of Technology Tianjin China Department of College of Computer and Control Engineering Nankai University Tianjin China

Internet of things are increasingly being deployed over the cloud (also referred to as cloud of things) to provide a broader range of services. However, there are serious challenges of CoT in the data protection and security provenance. This paper proposes a data privacy protection and provenance model (DPSPM) based on CoT. It can protect the privacy data of the users and trace the source of leaked data. In detail, security encryption and watermarking algorithms are proposed. Meanwhile, we use the improved k-anonymity data masking algorithm and pseudo-row watermarking algorithm in this scheme. Those algorithms can carry out security control over the whole process of data publishing, especially in data encryption, data masking and provenance verification. Finally, the experimental results show that our scheme has good efficiency. It is proved that the data masking time is proportional to the parameters k and L, the results also show good robustness to the common database watermarking attacks. © 2020 Inderscience Enterprises Ltd.

关键词： Data privacy

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