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On linear potential functions for approximating Bayesian computations

JOURNAL OF THE ACM 1996年第3期43卷 399-430页

作者： Santos, E Department of Electrical and Computer Engineering Air Force Institute of Technology Wright-Patterson AFB Ohio

Probabilistic reasoning suffers from NP-hard implementations. In particular, the amount of probabilistic information necessary to the computations is often overwhelming. For example, the size of conditional probability tables in Bayesian networks has long been a limiting factor in the general use of these networks. We present a new approach for manipulating the probabilistic information given. This approach avoids being overwhelmed by essentially compressing the information using approximation functions called linear potential functions. We can potentially reduce the information from a combinatorial amount to roughly linear in the number of random variable assignments. Furthermore, we can compute these functions through closed form equations. As it turns out, our approximation method is quite general and may be applied to other data compression problems.

关键词： artificial intelligence data compaction and compression integer programming least squares approximation pattern recognition probabilistic reasoning uncertainty

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Clustering-Based compression for Population DNA Sequences

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019年第1期16卷 208-221页

作者： Cheng, Kin-On Law, Ngai-Fong Siu, Wan-Chi Hong Kong Polytech Univ Dept Elect & Informat Engn Ctr Signal Proc Hung Hom Hong Kong Peoples R China

Due to the advancement of DNA sequencing techniques, the number of sequenced individual genomes has experienced an exponential growth. Thus, effective compression of this kind of sequences is highly desired. In this work, we present a novel compression algorithm called Reference-based compression algorithm using the concept of Clustering (RCC). The rationale behind RCC is based on the observation about the existence of substructures within the population sequences. To utilize these substructures, k-means clustering is employed to partition sequences into clusters for better compression. A reference sequence is then constructed for each cluster so that sequences in that cluster can be compressed by referring to this reference sequence. The reference sequence of each cluster is also compressed with reference to a sequence which is derived from all the reference sequences. Experiments show that RCC can further reduce the compressed size by up to 91.0 percent when compared with state-of-the-art compression approaches. There is a compromise between compressed size and processing time. The current implementation in Matlab has time complexity in a factor of thousands higher than the existing algorithms implemented in C/C++. Further investigation is required to improve processing time in future.

关键词： Biology and genetics clustering compression technologies data compaction and compression

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Nonthreshold-Based Event Detection for 3D Environment Monitoring in Sensor Networks

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2008年第12期20卷 1699-1711页

作者： Li, Mo Liu, Yunhao Chen, Lei Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Kowloon Hong Kong Peoples R China

Event detection is a crucial task for wireless sensor network applications, especially environment monitoring. Existing approaches for event detection are mainly based on some predefined threshold values and, thus, are often inaccurate and incapable of capturing complex events. For example, in coal mine monitoring scenarios, gas leakage or water osmosis can hardly be described by the overrun of specified attribute thresholds but some complex pattern in the full-scale view of the environmental data. To address this issue, we propose a nonthreshold-based approach for the real 3D sensor monitoring environment. We employ energy-efficient methods to collect a time series of data maps from the sensor network and detect complex events through matching the gathered data to spatiotemporal data patterns. Finally, we conduct trace-driven simulations to prove the efficacy and efficiency of this approach on detecting events of complex phenomena from real-life records.

关键词： Distributed applications data compaction and compression query processing wireless sensor networks

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data-Aware Adaptive compression for Stream Processing

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2024年第9期36卷 4531-4549页

作者： Zhang, Yu Zhang, Feng Li, Hourun Zhang, Shuhao Guo, Xiaoguang Chen, Yuxing Pan, Anqun Du, Xiaoyong Renmin Univ China Sch Informat Key Lab Data Engn & Knowledge Engn MOE Beijing 100872 Peoples R China Nanyang Technol Univ NTU Sch Comp Sci & Engn SCSE Singapore 639798 Singapore Tencent Inc Database R&D Dept Shenzhen Peoples R China

Stream processing has been in widespread use, and one of the most common application scenarios is SQL query on streams. By 2021, the global deployment of IoT endpoints reached 12.3 billion, indicating a surge in data generation. However, the escalating demands for high throughput and low latency in stream processing systems have posed significant challenges due to the increasing data volume and evolving user requirements. We present a compression-based stream processing engine, called CompressStreamDB, which enables adaptive fine-grained stream processing directly on compressed streams, to significantly enhance the performance of existing stream processing solutions. CompressStreamDB utilizes nine diverse compression methods tailored for different stream data types and integrates a cost model to automatically select the most efficient compression schemes. CompressStreamDB provides high throughput with low latency in stream SQL processing by identifying and eliminating redundant data among streams. Our evaluation demonstrates that CompressStreamDB improves average performance by 3.84x and reduces average delay by 68.0% compared to the state-of-the-art stream processing solution for uncompressed streams, along with 68.7% space savings. Besides, our edge trials show an average throughput/price ratio of 9.95x and a throughput/power ratio of 7.32x compared to the cloud design.

关键词： Encoding compression algorithms Real-time systems Low latency communication data processing Costs Delays data compaction and compression stream processing edge computing

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Interactive level-of-detail selection using image-based quality metric for large volume visualization

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2007年第1期13卷 122-134页

作者： Wang, Chaoli Garcia, Antonio Shen, Han-Wei Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA Intel Corp Bellevue WA 98007 USA

For large volume visualization, an image-based quality metric is difficult to incorporate for level-of-detail selection and rendering without sacrificing the interactivity. This is because it is usually time-consuming to update view-dependent information as well as to adjust to transfer function changes. In this paper, we introduce an image-based level-of-detail selection algorithm for interactive visualization of large volumetric data. The design of our quality metric is based on an efficient way to evaluate the contribution of multiresolution data blocks to the final image. To ensure real-time update of the quality metric and interactive level-of-detail decisions, we propose a summary table scheme in response to runtime transfer function changes and a GPU-based solution for visibility estimation. Experimental results on large scientific and medical data sets demonstrate the effectiveness and efficiency of our algorithm.

关键词： data compaction and compression perceptual reasoning viewing algorithms interaction techniques hierarchical image representation volume visualization

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Improved word-aligned binary compression for text indexing

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2006年第6期18卷 857-861页

作者： Anh, VN Moffat, A Univ Melbourne Dept Comp Sci & Software Engn Melbourne Vic 3010 Australia

We present an improved compression mechanism for handling the compressed inverted indexes used in text retrieval systems, extending the word-aligned binary coding carry method. Experiments using two typical document collections show that the new method obtains superior compression to previous static codes, without penalty in terms of execution speed.

关键词： data compaction and compression textual databases indexing methods file organization compression inverted index binary code text retrieval system text searching Web searching

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Applications of forman's discrete Morse theory to topology visualization and mesh compression

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2004年第5期10卷 499-508页

作者： Lewiner, T Lopes, H Tavares, G Pontificia Univ Catolica Rio de Janeiro Dept Matemat Lab MatMidia BR-22453900 Rio De Janeiro Brazil

Morse theory is a powerful tool for investigating the topology of smooth manifolds. It has been widely used by the computational topology, computer graphics, and geometric modeling communities to devise topology-based algorithms and data structures. Forman introduced a discrete version of this theory which is purely combinatorial. This work aims to build, visualize, and apply the basic elements of Forman's discrete Morse theory. It intends to use some of those concepts to visually study the topology of an object. As a basis, an algorithmic construction of optimal Forman's discrete gradient vector fields is provided. This construction is then used to topologically analyze mesh compression schemes, such as Edgebreaker and Grow&Fold. In particular, this paper proves that the complexity class of the strategy optimization of Grow&Fold is MAX-SNP hard.

关键词： discrete mathematics hypergraphs data compaction and compression computer fraphics computer-aided design

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compression of Multiple DNA Sequences Using Intra-Sequence and Inter-Sequence Similarities

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015年第6期12卷 1322-1332页

作者： Cheng, Kin-On Wu, Paula Law, Ngai-Fong Siu, Wan-Chi Hong Kong Polytech Univ Dept Elect & Informat Engn Ctr Signal Proc Hong Kong Hong Kong Peoples R China

Traditionally, intra-sequence similarity is exploited for compressing a single DNA sequence. Recently, remarkable compression performance of individual DNA sequence from the same population is achieved by encoding its difference with a nearly identical reference sequence. Nevertheless, there is lack of general algorithms that also allow less similar reference sequences. In this work, we extend the intra-sequence to the inter-sequence similarity in that approximate matches of subsequences are found between the DNA sequence and a set of reference sequences. Hence, a set of nearly identical DNA sequences from the same population or a set of partially similar DNA sequences like chromosome sequences and DNA sequences of related species can be compressed together. For practical compressors, the compressed size is usually influenced by the compression order of sequences. Fast search algorithms for the optimal compression order are thus developed for multiple sequences compression. Experimental results on artificial and real datasets demonstrate that our proposed multiple sequences compression methods with fast compression order search are able to achieve good compression performance under different levels of similarity in the multiple DNA sequences.

关键词： Biology and genetics data compaction and compression data dependencies information theory

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A Selective Encryption Scheme with Multiple security Levels for the H. 264/AVC Video Coding 16

A Selective Encryption Scheme with Multiple security Levels ...

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IEEE International Conference on Computer and Information Technology (CIT)

作者： Sbiaa, F. Kotel, S. Zeghid, M. Tourki, R. Machhout, M. Baganne, A. Univ South Brittany Lab Informat Sci & Technol Commun & Knowledge Lab STICC Lorient France Univ Monastir Fac Sci Elect & Microelect Lab Monastir 5019 Tunisia

ISBN: (纸本)9781509043149

The H. 264 Advanced Video Coding (H. 264/AVC) standard is considered to be the most commonly used format for video compression. Although it supports a very broad applications range covering all forms of digital compressed video, the confidentiality of the encoded content needs enhancements. The present work proposes a new selective encryption scheme with multiple security levels. In fact, depending on the encrypted coefficients nature, five different cryptographic scenarios are proposed. They provide an effective confidentiality and deal with the real-time processing requirements. The Hardware/Software co-simulation and the experimental results proved the efficiency of the proposed crypto-system that will be suitable for real-time video applications and resource-limited systems.

关键词： Video coding Coding and Information Theory data encryption data compaction and compression

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compression of Solar Spectroscopic Observations: a Case Study of MgII k Spectral Line Profiles Observed by NASA's IRIS Satellite 18

Compression of Solar Spectroscopic Observations: a Case Stud...

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18th International Conference on Content-Based Multimedia Indexing (IEEE CBMI)

作者： Sadykov, Viacheslav M. Kitiashvili, Irina N. Dalda, Alberto Sainz Oria, Vincent Kosovichev, Alexander G. Illarionov, Egor Georgia State Univ Dept Phys & Astron Atlanta GA 30303 USA NASA Supercomp Div Ames Res Ctr Moffett Field CA USA Lockheed Martin Solar & Astrophys Lab Palo Alto CA USA New Jersey Inst Technol Dept Comp Sci Newark NJ USA New Jersey Inst Technol Dept Phys Newark NJ 07102 USA Moscow MV Lomonosov State Univ Dept Mech & Math Moscow Russia

ISBN: (纸本)9781665442206

In this study we extract the deep features and investigate the compression of the MgII k spectral line profiles observed in quiet Sun regions by NASA's IRIS satellite. The data set of line profiles used for the analysis was obtained on April 20th, 2020, at the center of the solar disc, and contains almost 300,000 individual MgII k line profiles after data cleaning. The data are separated into train and test subsets. The train subset was used to train the autoencoder of the varying embedding layer size. The early stopping criterion was implemented on the test subset to prevent the model from overfitting. Our results indicate that it is possible to compress the spectral line profiles more than 27 times (which corresponds to the reduction of the data dimensionality from 110 to 4) while having a 4DN (data Number) average reconstruction error, which is comparable to the variations in the line continuum. The mean squared error and the reconstruction error of even statistical moments sharply decrease when the dimensionality of the embedding layer increases from 1 to 4 and almost stop decreasing for higher numbers. The observed occasional improvements in training for values higher than 4 indicate that a better compact embedding may potentially be obtained if other training strategies and longer training times are used. The features learned for the critical four-dimensional case can be interpreted. In particular, three of these four features mainly control the line width, line asymmetry, and line dip formation respectively. The presented results are the first attempt to obtain a compact embedding for spectroscopic line profiles and confirm the value of this approach, in particular for feature extraction, data compression, and denoising.

关键词： Machine learning Neural nets data compaction and compression Feature extraction or construction

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