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检索条件"任意字段=IEEE Data Compression Conference"

共 16771 条记录，以下是871-880 订阅

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Compact Representation of Interval Graphs of Bounded Degree and Chromatic Number

Compact Representation of Interval Graphs of Bounded Degree ...

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data compression conference (DCC)

作者： Chakraborty, Sankardeep Jo, Seungbum Univ Tokyo Tokyo Japan Chungnam Natl Univ Daejeon South Korea

ISBN: (纸本)9781665478939

In this paper we initiate the study of designing parameterized compact data structures for interval graph G. First, we show that when the maximum degree of G is bounded by l, there exists an (n log(2)l + O(n))-bit data structure while supporting the standard navigational queries i.e., degree, adjacency, and neighborhood optimally. Our data structure takes less space than the information-theoretic lower bound when l = O(n(epsilon)) where 0 < epsilon < 1. Furthermore, we show that the space requirement of our data structure is almost optimal by giving an explicit (1/6n log(2) l - O(n))-bit counting lower bound. Thus, we provide a compact representation of interval graphs with bounded degree for the first time in literature. Finally, we consider the interval graphs with bounded chromatic number (Delta), and design a (Delta - 1)n + o(Delta n) bit data structure with efficient query times for such graphs. This improves the worst-case information-theoretic lower bound when Delta = o(log n).

关键词： Navigation data compression data structures Standards

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An Analysis of Compressed Sensing Technology Considering Complex Electrical Quantity Signals in Agricultural Grid System 5

An Analysis of Compressed Sensing Technology Considering Com...

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5th International conference on Power and Energy Technology, ICPET 2023

作者： Li, JianQi Guo, HuiFang Huang, BiYao State Grid Smart Grid Research Institute Co. Ltd. Beijing China

ISBN: (纸本)9798350339673

In order to solve the network congestion problem caused by high-frequency information interaction in the agricultural network system and meet the needs of the current consolidation and upgrading of the agricultural network, this paper improves the traditional LZ77 compression algorithm, statistically encodes the output results of the LZ77 compression algorithm through Huffman, and compresses the data based on the hybrid algorithm of dictionary and statistics. The results show that the LZ77-Huffman compression algorithm can effectively compress the electrical quantity information in the process of information interaction in the typical power consumption scenario of the agricultural grid, and alleviate the network congestion problem of the agricultural grid system caused by massive information interaction. © 2023 ieee.

关键词： data compression huffman information interaction LZ77 rural power grid

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Preserving Topological Feature with Sign-of-Determinant Predicates in Lossy compression: A Case Study of Vector Field Critical Points 40

Preserving Topological Feature with Sign-of-Determinant Pred...

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40th ieee International conference on data Engineering, ICDE 2024

作者： Xia, Mingze Di, Sheng Cappello, Franck Jiao, Pu Zhao, Kai Liu, Jinyang Wu, Xuan Liang, Xin Guo, Hanqi University of Kentucky Lexington United States Argonne National Laboratory Lemont United States Florida State University Tallahassee United States University of California Riverside Riverside United States The Ohio State University Columbus United States University of Kentucky Department of Computer Science LexingtonKY40506 United States

ISBN: (纸本)9798350317152

Lossy compression has been employed to reduce the unprecedented amount of data produced by today's large-scale scientific simulations and high-resolution instruments. To avoid loss of critical information, state-of-the-art scientific lossy compressors provide error controls on relatively simple metrics such as absolute error bound. However, preserving these metrics does not translate to the preservation of topological features, such as critical points in vector fields. To address this problem, we investigate how to effectively preserve the sign of determinant in error-controlled lossy compression, as it is an important quantity of interest used for the robust detection of many topological features. Our contribution is three-fold. (1) We develop a generic theory to derive the allowable perturbation for one row of a matrix while preserving its sign of the determinant. As a practical use-case, we apply this theory to preserve critical points in vector fields because critical point detection can be reduced to the result of the point-in-simplex test that purely relies on the sign of determinants. (2) We optimize this algorithm with a speculative compression scheme to allow for high compression ratios and efficiently parallelize it in distributed environments. (3) We perform solid experiments with real-world datasets, demonstrating that our method achieves up to 440% improvements in compression ratios over state-of-the-art lossy compressors when all critical points need to be preserved. Using the parallelization strategies, our method delivers up to 1.25 x and 4.38 x performance speedup in data writing and reading compared with the vanilla approach without compression. © 2024 ieee.

关键词： Errors

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An Algorithmic and Software Pipeline for Very Large Scale Scientific data compression with Error Guarantees 29

An Algorithmic and Software Pipeline for Very Large Scale Sc...

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29th Annual ieee International conference on High Performance Computing, data, and Analytics (HiPC)

作者： Banerjee, Tania Choi, Jong Lee, Jaemoon Gong, Qian Wang, Ruonan Klasky, Scott Rangarajan, Anand Ranka, Sanjay Univ Florida Gainesville FL 32611 USA Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (纸本)9781665494236

Efficient data compression is becoming increasingly critical for storing scientific data because many scientific applications produce vast amounts of data. This paper presents an end-to-end algorithmic and software pipeline for data compression that guarantees both error bounds on primary data (PD) and derived data, known as Quantities of Interest (QoI). We demonstrate the effectiveness of the pipeline by compressing fusion data generated by a large-scale fusion code, XGC, which produces tens of petabytes of data in a single day. We demonstrate that the compression is conducted by setting aside computational resources known as staging nodes, and does not impact the simulation performance. For efficient parallel I/O, the pipeline uses ADIOS2, which many codes such as XGC already use for their parallel I/O. We show that our approach can compress the data by two orders of magnitude while guaranteeing high accuracy on both the PD and the QoIs. Further, the amount of resources required by compression is a few percent of the resources required by simulation while ensuring that the compression time for each stage is less than the corresponding simulation time. This pipeline consists of three main steps. The first step decomposes the data using domain decomposition into small subdomains. Each subdomain is then compressed independently to achieve a high level of parallelism. The second step uses existing techniques that guarantee error bounds on the primary data for each subdomain. The third step uses a post-processing optimization technique based on Lagrange multipliers to reduce the QoI errors for data corresponding to each subdomain. The Lagrange multipliers generated can be further quantized or truncated to increase the compression level. All of the above characteristics of our approach make it highly practical to apply on-the-fly compression while guaranteeing errors on QoIs that are critical to the scientists.

关键词： ITER XGC data compression Lagrange multipliers MGARD SZ ZFP

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Text compression Algorithms Employed Vowels and Article Pattern 7

Text Compression Algorithms Employed Vowels and Article Patt...

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7th International conference on Information Technology, InCIT 2023

作者： Le-Wan, Prayat Khancome, Chouvalit Ramkhamhaeng University Faculty of Science Department of Computer Science Bangkok Thailand

ISBN: (纸本)9798350358698

This research article introduces the TCEVP and TCEVPaA data compression algorithms. They employ a novel dictionary design that utilizes patterns of word construction based on vowel and article formats to reference words during both compression and decompression. The compressed data is stored in a bit format before being written to the compressed file. In theoretical experimental results, when considering vowel patterns of 1-12 characters, the percentage of bit compression per word is as follows: when using ASCII encoding, compression ranges from 37.5% to 90.38%;Unicode achieves compression percentages ranging from 68.80% to 95.20%;and Long Unicode can compress data by 84.40% to 97.60%. When examining text ranging from 1 kiloword to 1 teraword with mixed formats, encoded using ASCII, Unicode, big endian Unicode, and Long Unicode encoding schemes, compression rates can reach from 82.42% to 95.61%, with compression ratios ranging from 5.69 to 22.80 times. © 2023 ieee.

关键词： Signal encoding

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Binary Synchronous compression Algorithm for Big data Based on Feature-Sample Selections 35

Binary Synchronous Compression Algorithm for Big Data Based ...

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35th Chinese Control and Decision conference (CCDC)

作者： Guan, Shouping Huang, Haohong Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9798350334722

The feature selection and the sample selection are main way of data preprocessing to optimize the original dataset, which can solve the problems of long computing time and low accuracy in big data multi-classification area. However, the traditional selection strategy itself requires a lot of computing time;Meanwhile, the order of the adopted multiple selection strategies will affect the optimization results. Aiming at these problems, this paper proposed a binary synchronous compression algorithm (BSCA), which includes three key parts: firstly, a feature selection method and a sample selection method based on variance analysis and random sampling comparison, respectively, are presented;Then, the proposed two methods handle the original dataset synchronously and mark the selection results;Finally, according to the labeling results, the selected features in the selected samples are extracted to form a new sample dataset as the optimization result. The experimental results show that the proposed BSCA is effective by neural network classification problem.

关键词： dataset Optimization Binary Synchronous compression Feature Selection Sample Selection

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EC-Graph: A Distributed Graph Neural Network System with Error-Compensated compression 38

EC-Graph: A Distributed Graph Neural Network System with Err...

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38th ieee International conference on data Engineering (ICDE)

作者： Song, Zhen Gu, Yu Qi, Jianzhong Wang, Zhigang Yu, Ge Northeastern Univ Shenyang Peoples R China Univ Melbourne Melbourne Vic Australia Ocean Univ China Shenyang Peoples R China

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

The high training costs of graph neural networks (GNNs) have limited their applicability on large graphs, e.g., graphs with hundreds of millions of vertices which have become common in the era of big data. A few recent studies propose distributed GNN systems. However, these systems may generate high communication costs due to the extensive message passing among graph vertices stored on different machines. To address such limitations, in the paper, 1) we propose a distributed GNN computation system named EC-Graph for CPU clusters, which drastically reduces the communication costs among the machines by message compression;2) we design a requesting-end compensation method for the embeddings to mitigate the errors induced by compression in the forward propagation and a Bit-Tuner to adaptively balance the model accuracy and message size;and 3) we propose a responding-end compensation approach for the embedding gradients in the backward propagation. Extensive experiments over large real-world datasets show that EC-Graph outperforms state-of-the-art distributed GNN systems on two CPU clusters of different sizes.

关键词： distributed systems distributed graph neural networks compression error compensation

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PS-NERV: PATCH-WISE STYLIZED NEURAL REPRESENTATIONS FOR VIDEOS 30

PS-NERV: PATCH-WISE STYLIZED NEURAL REPRESENTATIONS FOR VIDE...

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30th ieee International conference on Image Processing (ICIP)

作者： Bai, Yunpeng Dong, Chao Wang, Cairong Yuan, Chun Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9781728198354

We study how to represent a video with implicit neural representations (INRs). Classical INRs methods generally utilize MLPs to map input coordinates to output pixels. While some recent works have tried to directly reconstruct the whole image with CNNs. However, we argue that both the above pixel-wise and image-wise strategies are not favorable to video data. Instead, we propose a patch-wise solution, PS-NeRV, which represents videos as a function of patches and the corresponding patch coordinate. It naturally inherits the advantages of image-wise methods, and achieves excellent reconstruction performance with fast decoding speed. The whole method includes conventional modules, like positional embedding, MLPs and CNNs. We also introduce AdaIN to enhance intermediate features. Extensive experiments have demonstrated its effectiveness in several video-related tasks, such as video compression and video inpainting.

关键词： Implicit neural representation video representation video compression video inpainting

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An Efficient Network Flow Rules compression Method Based On In-Network Computation On Cloud Computing 29

An Efficient Network Flow Rules Compression Method Based On ...

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29th ieee International conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhang, Penghao Zhang, Xinyi Shanxi University China Chinese Academy of Sciences Computer Network Information Center China

ISBN: (纸本)9798350330717

The development of cloud computing has led to the explosion of network traffic. The switches of cloud computing make it hard to process large-scale network traffic. Prior approaches proposed flow rules compression methods with splitting matching fields, expanding switch storage space, or reconstructing network architecture. The methods incurred little performance improvement in the network. The technology of innetwork computation(INC) has been widely used to accelerate the network of cloud computing and data-intensive distributed applications. The computing tasks performed on servers are offloaded to the network through INC. We implement a flow rules compression method(NetFR) based on INC to accelerate the network performance of cloud computing. NetFR adopts heuristic methods which include rule-deduplication and optimal coverage to compress flow rules, and offloads compressed rules into switches to accelerate the performance of the network on cloud computing. Finally, we evaluate NetFR and show that the performance of NetFR is 1.3x better than other methods of network packet matching. Furthermore, NetFR can achieve 98% compression ratio on average and 99.99% on highest. © 2023 ieee.

关键词： Cloud Computing In-Network Computing Network Flow Rules compression Programmable Switch

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Exploring Wavelet Transform Usages for Error-bounded Scientific data compression

Exploring Wavelet Transform Usages for Error-bounded Scienti...

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2023 ieee International conference on Big data, Bigdata 2023

作者： Huang, Jiajun Liu, Jinyang Di, Sheng Zhai, Yujia Jian, Zizhe Wu, Shixun Zhao, Kai Chen, Zizhong Guo, Yanfei Cappello, Franck University of California RiversideCA United States Florida State University TallahasseeFL United States Argonne National Laboratory LemontIL United States University of Illinois at Urbana-Champaign UrbanaIL United States

ISBN: (纸本)9798350324457

To address the challenges raised by the data management of exascale scientific data, error-bounded lossy compression has been proposed and well-researched as a prominent solution. Among the existing works, a recent trend leverages wavelet transforms in the error-bounded lossy compression task to effectively capture long-term data correlations within the inputs. Applying those transforms as data preprocessors and decorrelators, wavelet-based lossy compressors have achieved optimized compression rate-distortion on several datasets. However, certain significant limitations of wavelet-based compressors have also been observed: On one hand, attributed to the high computational cost of wavelet transforms, wavelet-based compressors suffer from relatively low computational efficiencies compared to other state-of-the-art compressors. On the other hand, one certain type of wavelet transform cannot perform well on all variations of scientific data. Consequently, to further fine-tune the wavelet-based scientific data lossy compression, more in-depth and systematic research and analysis needs to be conducted. In this paper, based on the FAZ auto-tuning-based modular compression framework, we have integrated a great number of wavelet transforms into the framework and evaluated them with various real-world scientific datasets and fields. From the analysis of those evaluations and the comparison to existing state-of-the-art wavelet-based and non-wavelet-based error-bounded lossy compressors, we conclude and present several essential takeaways for designing and optimizing the wavelet-based scientific error-bounded lossy compressor. © 2023 ieee.

关键词： error-bounded lossy compression scientific datasets wavelet transform

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