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On the performance of data compression algorithms based upon string matching

IEEE TRANSACTIONS ON INFORMATION THEORY 1998年第1期44卷 47-65页

作者： Yang, EH Kieffer, JC Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Univ Minnesota Dept Elect Engn Minneapolis MN 55455 USA

Lossless and lossy data compression algorithms based on string matching are considered. In the lossless case, a result of Wyner and Ziv is extended, In the lossy case, a data compression algorithm based on approximate string matching is analyzed in the following two frameworks: 1) the database and the source together form a Markov chain of finite order;2) the database and the source are independent with the database coming from a Markov model and the source from a general stationary, ergodic model. In either framework, it is shown that the resulting compression rate converges with probability one to a quantity computable as the infimum of an information-theoretic functional over a set of auxiliary random variables;the quantity is strictly greater than the rate distortion function of the source except in some symmetric cases. In particular, this result implies that the lossy algorithm proposed by Steinberg and Gutman is not optimal, even for memoryless or Markov sources.

关键词： data compression algorithms phi-mixing sources rate distortion function string matching

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Resource efficient data compression algorithms for demanding, WSN based biomedical applications

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JOURNAL OF BIOMEDICAL INFORMATICS 2016年 59卷 1-14页

作者： Antonopoulos, Christos P. Voros, Nikolaos S. Technol Educ Inst Western Greece Comp & Informat Engn Dept Natl Rd Antiriou Ioanninon Antirio 30020 Greece

During the last few years, medical research areas of critical importance such as Epilepsy monitoring and study, increasingly utilize wireless sensor network technologies in order to achieve better understanding and significant breakthroughs. However, the limited memory and communication bandwidth offered by WSN platforms comprise a significant shortcoming to such demanding application scenarios. Although, data compression can mitigate such deficiencies there is a lack of objective and comprehensive evaluation of relative approaches and even more on specialized approaches targeting specific demanding applications. The research work presented in this paper focuses on implementing and offering an in-depth experimental study regarding prominent, already existing as well as novel proposed compression algorithms. All algorithms have been implemented in a common Matlab framework. A major contribution of this paper, that differentiates it from similar research efforts, is the employment of real world Electroencephalography (EEG) and Electrocardiography (ECG) datasets comprising the two most demanding Epilepsy modalities. Emphasis is put on WSN applications, thus the respective metrics focus on compression rate and execution latency for the selected datasets. The evaluation results reveal significant performance and behavioral characteristics of the algorithms related to their complexity and the relative negative effect on compression latency as opposed to the increased compression rate. It is noted that the proposed schemes managed to offer considerable advantage especially aiming to achieve the optimum tradeoff between compression rate -latency. Specifically, proposed algorithm managed to combine highly completive level of compression while ensuring minimum latency thus exhibiting real-time capabilities. Additionally, one of the proposed schemes is compared against state-of-the-art general-purpose compression algorithms also exhibiting considerable advantages as far as the

关键词： Biomedical signal processing data compression algorithms Wireless sensor networks Performance evaluation ECG/EEG signal compression Epilepsy monitoring

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A Study of Vessel Trajectory compression Based on Vector data compression algorithms 22nd

A Study of Vessel Trajectory Compression Based on Vector Dat...

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22nd International Conference on Business Information Systems (BIS)

作者： Ji, Yuanyuan Xu, Wenhai Deng, Ansheng Dalian Maritime Univ Informat Sci Technol Coll Dalian 116026 Peoples R China

ISBN: (纸本)9783030366919;9783030366902

With the development of information technology and its vast applications in vessel traffic, such as the popular Automatic Identification System (AIS), a large quantity of vessel trajectory data has been recorded and stored. Vessel traffic has also entered the age of big data. However, the redundancy of data considerably reduces the availability of research and applications, and how to compress these data becomes a problem that needs to be solved. In this paper, several classical vector data compression algorithms are summarized, and the ideas of each algorithm and the steps to compress vessel trajectories are introduced. The vessel trajectory compression experiments based on the algorithms are performed. The results are analyzed, and the characteristics of each algorithm are summarized. The results and conclusions lay the foundation for the selection and improvement of the algorithms in vessel trajectory compression. Through the study of this paper, a systematic theoretical support for the compression of vessel trajectories is provided, which could guide practical applications.

关键词： Vessel trajectory Big data data compression algorithms AIS

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On the aerodynamic shape optimization of cars using steady & compression-assisted unsteady adjoint

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ENGINEERING OPTIMIZATION 2025年

作者： Margetis, Andreas-Stefanos Papoutsis-Kiachagias, Evangelos Giannakoglou, Kyriakos Natl Tech Univ Athens NTUA Sch Mech Engn Parallel CFD & Optimizat Unit Athens Greece

This article investigates the application of the continuous adjoint method to optimize passenger car shapes. Three optimization strategies, of industrial interest, are assessed in terms of computational cost and effectiveness of the optimization: (a) solving the Reynolds-Averaged Navier-Stokes (RANS) primal and adjoint equations;(b) solving the unsteady RANS primal and adjoint equations;and (c) solving the RANS adjoint equations based on a time-averaged Delayed-Detached Eddy Simulation solution, yielding an unsteady primal/steady adjoint approach. All of them include the adjoint to the Spalart-Allmaras turbulence model equation. All optimized shapes are re-evaluated using all of the aforementioned flow models, discussing differences in performance and flow features around the car. To reduce the turn-around time of unsteady adjoint, lossy data compression techniques, recently developed by the authors, are used for the first time in such a large-scale application. All methods are implemented as an extension of the publicly available adjointOptimisation library of OpenFOAM $ <^>{\circledR } $ (R).

关键词： Automotive aerodynamic shape optimization continuous adjoint computational fluid dynamics data compression algorithms OpenFOAM

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Latency-aware adaptive micro-batching techniques for streamed data compression on graphics processing units

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第11期33卷 e5786-e5786页

作者： Stein, Charles M. Rockenbach, Dinei A. Griebler, Dalvan Torquati, Massimo Mencagli, Gabriele Danelutto, Marco Fernandes, Luiz G. Tres de Maio Fac SETREM Lab Adv Res Cloud Comp Tres De Maio Brazil Pontifical Catholic Univ Rio Grande Sch Technol Ipiranga Ave Porto Alegre 6681 RS Brazil Univ Pisa Comp Sci Dept Pisa Italy

Stream processing is a parallel paradigm used in many application domains. With the advance of graphics processing units (GPUs), their usage in stream processing applications has increased as well. The efficient utilization of GPU accelerators in streaming scenarios requires to batch input elements in microbatches, whose computation is offloaded on the GPU leveraging data parallelism within the same batch of data. Since data elements are continuously received based on the input speed, the bigger the microbatch size the higher the latency to completely buffer it and to start the processing on the device. Unfortunately, stream processing applications often have strict latency requirements that need to find the best size of the microbatches and to adapt it dynamically based on the workload conditions as well as according to the characteristics of the underlying device and network. In this work, we aim at implementing latency-aware adaptive microbatching techniques and algorithms for streaming compression applications targeting GPUs. The evaluation is conducted using the Lempel-Ziv-Storer-Szymanski compression application considering different input workloads. As a general result of our work, we noticed that algorithms with elastic adaptation factors respond better for stable workloads, while algorithms with narrower targets respond better for highly unbalanced workloads.

关键词： data compression algorithms dynamic reconfiguration parallel programming service level objective stream parallelism stream processing

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A new algorithm for energy-driven data compression in VLIW embedded processors

A new algorithm for energy-driven data compression in VLIW e...

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Design, Automation and Test in Europe Conference and Exhibition (DATE 03)

作者： Macii, A Macii, E Crudo, F Zafalon, R Politecn Torino Turin Italy

ISBN: (纸本)0769518702

This paper presents a new algorithm for on-the-fly data compression in high performance VLIW processors. The algorithm aggressively targets energy minimization of some of the dominant factors in the SoC energy budget (i.e., main memory access and high throughput global bus). Based on a differential technique, both the new algorithm and the HW compression unit have been developed to efficiently manage data compression and decompression into a high performance industrial processor architecture, under strict real time constraints (Lx-ST200: A 4-issue, 6-stages pipelined VLIW processor with on-chip D and I-Cache). The original data-Cache line is compressed before write-back to main memory and, then, decompressed whenever Cache refill takes place. An extensive experimental strategy has been developed for the specific validation of the target Lx processor. In order to allow public comparison, we also report the results obtained on a MIPS pipelined RISC processor simulated with SimpleScalar. The two platforms have been benchmarked over Ptolemy and MediaBench programs. Energy savings provided by the application of the proposed technique range from 10% to 22% on the Lx-ST200 platform and from 11% to 14% on the M1PS platform.

关键词： data compression algorithms system-level energy optimization VLIW embedded processors

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Pipelined decoder for the limited context order Burrows-Wheeler transformation

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IET CIRCUITS DEVICES & SYSTEMS 2019年第1期13卷 31-38页

作者： Safieh, Malek Freudenberger, Juergen Univ Appl Sci HTWG Konstanz ISD Constance Germany

The Burrows-Wheeler transformation (BWT) is a reversible block sorting transform that is an integral part of many data compression algorithms. This work proposes a memory-efficient pipelined decoder for the BWT. In particular, the authors consider the limited context order BWT that has low memory requirements and enable fast encoding. However, the decoding of the limited context order BWT is typically much slower than the encoding. The proposed decoder pipeline provides a fast inverse BWT by splitting the decoding into several processing stages which are executed in parallel.

关键词： pipelined decoder data compression sorting limited context order BWT decoding data compression algorithms reversible block sorting transform memory-efficient pipelined decoder fast inverse BWT decoding limited context order Burrows–Wheeler transformation fast encoding inverse transforms

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A New Algorithm for Energy-Driven data compression in VLIW Embedded Processors 03

A New Algorithm for Energy-Driven Data Compression in VLIW E...

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Proceedings of the conference on Design, Automation and Test in Europe - Volume 1

作者： Alberto Macii Enrico Macii Fabrizio Crudo Roberto Zafalon Politecnico di Torino STMicroelectronics

ISBN: (纸本)9780769518701

This paper presents a new algorithm for on-the-fly data compression in high performance VLIW processors. The algorithm aggressively targets energy minimization of some of the dominant factors in the SoC energy budget (i.e., main memory access and high throughput global bus). Based on a differential technique, both the new algorithm and the HW compression unit have been developed to efficiently manage data compression and decompression into a high performance industrial processor architecture, under strict real time constraints (Lx-ST200:A 4-issue, 6-stages pipelined VLIW processor with on-chip D and I-Cache). The original data-Cache line is compressed before write-back to main memory and, then, decompressed whenever Cache refill takes place. An extensive experimental strategy has been developed for the specific validation of the target Lx processor. In order to allow public comparison, we also report the results obtained on a MIPS pipelined RISC processor simulated with SimpleScalar. The two platforms have been benchmarked over Ptolemy and MediaBench programs. Energy savings provided by the application of the proposed technique range from 10% to 22% on the Lx-ST200 platform and from 11% to 14% on the MIPS platform.

关键词： data compression algorithms VLIW embedded processors system-level energy optimization

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Method of compact archiving of signals in time delay spectrometry

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MEASUREMENT TECHNIQUES 2004年第10期47卷 980-984页

作者： Martynuk, MV

Using a time delay spectrometry system as an example, we consider ways of compactly archiving discrete signals with distinct spectral peaks in the low-frequency range.

关键词： data compression algorithms time delay spectrometry differential pulse-code modulation Fourier transform discrete quantized signal

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Expert, Journal, and Automatic Classification of Full Texts and Annotations of Scientific Articles

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AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS 2021年第4期55卷 178-189页

作者： Selivanova, I. V. Kosyakov, D. V. Dubovitskii, D. A. Guskov, A. E. Russian Acad Sci Siberian Branch State Publ Sci Technol Lib Novosibirsk Russia Novosibirsk State Univ Novosibirsk Russia

In this article we consider a fundamentally new information-theoretic approach to the classification of scientific texts based on compression algorithms. An analysis using the example of the comparative classification of full-text documents from *** and short annotations from Scopus showed that the accuracy of the proposed method is 87-92% and, in general, is not inferior to the existing ones. These conclusions were confirmed by an expert assessment.

关键词： text classification methods data compression algorithms scientific texts *** Scopus k-nearest neighbors logistic regression random forests naive Bayesian classification support vector machines

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