Relevant bibliographies by topics / Data compression

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data compression'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 July 2025

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Journal articles on the topic "Data compression"

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Shevchuk, Yury Vladimirovich. "Memory-efficient sensor data compression." Program Systems: Theory and Applications 13, no. 2 (April 4, 2022): 35–63. http://dx.doi.org/10.25209/2079-3316-2022-13-2-35-63.

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We treat scalar data compression in sensor network nodes in streaming mode (compressing data points as they arrive, no pre-compression buffering). Several experimental algorithms based on linear predictive coding (LPC) combined with run length encoding (RLE) are considered. In entropy coding stage we evaluated (a) variable-length coding with dynamic prefixes generated with MTF-transform, (b) adaptive width binary coding, and (c) adaptive Golomb-Rice coding. We provide a comparison of known and experimental compression algorithms on 75 sensor data sources. Compression ratios achieved in the tests are about 1.5/4/1000000 (min/med/max), with compression context size about 10 bytes.
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Sharath, R., Sinha Shivani, B.I Supreeth, Hebbar B.C. Varun, and B. Santhosh. "Distributed Data Compression using Cloud Computing Approach." Journal of Optical Communication Electronics 5, no. 2 (May 1, 2019): 5–10. https://doi.org/10.5281/zenodo.2656008.

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<em>Storage and data trafficking have grown a great deal over the past decade. Therefore, there is a need to reduce the size of the data for better speed and proper utilization of the bandwidth. There are compressions that happen online as well as some happen statically. The online based compression is in greater demand, since an online platform is much easier and faster. Static methods have a requisition of the entire file to be present at the time of transmission, while online compressions don&rsquo;t. Compressions make use of an algorithm to effectively shrink the data. Depending upon the speed of compression and the data loss, we can determine whether the compression algorithm is good or not. Data compression can be used on any form of data. Be it images, or videos to simple text files, we can compress the data. Compression mainly involves removing the redundancy in the data. Depending on the redundancy, a text file can be reduced up to 50% or more than its original size. There are many forms of compression. Few examples are: ZIP, RAR, GZIP, PNG, JPEG, WAV, etc. There are two methods of compression. A loss less compression and a lossy compression. Few algorithms that are most widely used in lossless compression is Huffman encoding and LZ77 encoding. In this study, we hope to come up with a better compression algorithm using modern approaches and tools using Singular Value Decomposition technique.</em>
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Saidhbi, Sheik. "An Intelligent Multimedia Data Encryption and Compression and Secure Data Transmission of Public Cloud." Asian Journal of Engineering and Applied Technology 8, no. 2 (May 5, 2019): 37–40. http://dx.doi.org/10.51983/ajeat-2019.8.2.1141.

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Data compression is a method of reducing the size of the data file so that the file should take less disk space for storage. Compression of a file depends upon encoding of file. In lossless data compression algorithm there is no data loss while compressing a file, therefore confidential data can be reproduce if it is compressed using lossless data compression. Compression reduces the redundancy and if a compressed file is encrypted it is having a better security and faster transfer rate across the network than encrypting and transferring uncompressed file. Most of the computer applications related to health are not secure and these applications exchange lot of confidential health data having different file formats like HL7, DICOM images and other audio, image, textual and video data formats etc. These types of confidential data need to be transmitted securely and stored efficiently. Therefore this paper proposes a learning compression- encryption model for identifying the files that should be compressed before encrypting and the files that should be encrypted without compressing them.
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Nithya, P., T. Vengattaraman, and M. Sathya. "Survey On Parameters of Data Compression." REST Journal on Data Analytics and Artificial Intelligence 2, no. 1 (March 1, 2023): 1–7. http://dx.doi.org/10.46632/jdaai/2/1/1.

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The rapid development in the hardware and the software gives rise to data growth. This data growth has numerous impacts, including the need for a larger storage capacity for storing and transmitting. Data compression is needed in today’s world because it helps to minimize the amount of storage space required to store and transmit data. Performance measures in data compression are used to evaluate the efficiency and effectiveness of data compression algorithms. In recent times, numerous data compression algorithms are developed to reduce data storage and increase transmission speed in this internet era. In order to analyses how data compression performance is measured in terms of text, image, audio, and video compressions. This survey presents discussion made for important data compression parameters according to their data types.
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Chen, Xinyu, Jiannan Tian, Ian Beaver, Cynthia Freeman, Yan Yan, Jianguo Wang, and Dingwen Tao. "FCBench: Cross-Domain Benchmarking of Lossless Compression for Floating-Point Data." Proceedings of the VLDB Endowment 17, no. 6 (February 2024): 1418–31. http://dx.doi.org/10.14778/3648160.3648180.

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While both the database and high-performance computing (HPC) communities utilize lossless compression methods to minimize floating-point data size, a disconnect persists between them. Each community designs and assesses methods in a domain-specific manner, making it unclear if HPC compression techniques can benefit database applications or vice versa. With the HPC community increasingly leaning towards in-situ analysis and visualization, more floating-point data from scientific simulations are being stored in databases like Key-Value Stores and queried using in-memory retrieval paradigms. This trend underscores the urgent need for a collective study of these compression methods' strengths and limitations, not only based on their performance in compressing data from various domains but also on their runtime characteristics. Our study extensively evaluates the performance of eight CPU-based and five GPU-based compression methods developed by both communities, using 33 real-world datasets assembled in the Floating-point Compressor Benchmark (FCBench). Additionally, we utilize the roofline model to profile their runtime bottlenecks. Our goal is to offer insights into these compression methods that could assist researchers in selecting existing methods or developing new ones for integrated database and HPC applications.
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Bernstein, Herbert J., Alexei Soares, Kimberly Horvat, and Jean Jakoncic. "Massive Compression for High Data Rate Macromolecular Crystallography (HDRMX): Impact on Diffraction Data and Subsequent Structural Analysis." Structural Dynamics 12, no. 2_Supplement (March 1, 2025): A147. https://doi.org/10.1063/4.0000456.

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New higher-count-rate, integrating, large area X-ray detectors with framing rates as high as 17,400 images per second are beginning to be available. Data from these detectors are always compressed losslessly, but systems may not keep up with these data rates, and the files may still be larger than seems necessary. We propose that such MX experiments will require lossy compression algorithms to keep up with data throughput and capacity for long-term storage, but note that some information may be lost. Indeed, one might employ dramatic lossy compression only for archiving of data after structures are solved and published. Can we minimize this loss with acceptable impact on structural information? To explore this question, we have considered several approaches: summing short sequences of images to reverse fine phi-slicing, binning to create the effect of larger pixels, use of JPEG-2000 lossy wavelet-based compression from the movie industry, and the use of Hcompress which is a Haar-wavelet-based lossy compression from astronomy. In each of these last two methods one can specify approximately how much one wants the result to be compressed from the starting-file size. We have also explored the effect of combinations of summing and binning with Hcompress or JPEG-2000. These provide particularly effective lossy compressions that retain essential information for structure solution from coherent Bragg reflections. There are many lossy compressions to consider. For this work we have experimented with lossy compression of less than 10 to over 50,000. Caution is needed to avoid over-compression that loses significant structural data and damages the quality of peak integrations. The combined use of modest degrees of binning and summing actually strengthens the weak reflections, which helps to protect them from the impact of the massive compressions provided by Hcompress or JPEG-2000. See the figures below for the impact of JPEG-2000 and Hcompress by themselves and in combination with binning and summing by two. The bottom half of the first figure shows the impact of JPEG-2000 compressions on weak reflections. J2k200 keeps the weak peaks findable but distorts the shoulders. The higher JPEG-2000 compressions do serious damage, losing the peak entirely for j2k1000. The top half of the first figure shows that Hcompress does better but also loses the peak entirely for high compressions. In the second figure, the weak peak has been protected by modest binning and summing allowing useful overall compression ratios well over 500 to 1 to be achieved in many cases and over 1000 to 1 in some cases. The lesson we have learned is that with care to protect weak peaks with modest binning and summing, and with care to avoid over-compression, in many cases, massive lossy compression can be applied successfully while retaining the information necessary to find and integrate Bragg reflections and to solve structures with good statistics.
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Ryabko, Boris. "Time-Universal Data Compression." Algorithms 12, no. 6 (May 29, 2019): 116. http://dx.doi.org/10.3390/a12060116.

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Nowadays, a variety of data-compressors (or archivers) is available, each of which has its merits, and it is impossible to single out the best ones. Thus, one faces the problem of choosing the best method to compress a given file, and this problem is more important the larger is the file. It seems natural to try all the compressors and then choose the one that gives the shortest compressed file, then transfer (or store) the index number of the best compressor (it requires log m bits, if m is the number of compressors available) and the compressed file. The only problem is the time, which essentially increases due to the need to compress the file m times (in order to find the best compressor). We suggest a method of data compression whose performance is close to optimal, but for which the extra time needed is relatively small: the ratio of this extra time and the total time of calculation can be limited, in an asymptotic manner, by an arbitrary positive constant. In short, the main idea of the suggested approach is as follows: in order to find the best, try all the data compressors, but, when doing so, use for compression only a small part of the file. Then apply the best data compressors to the whole file. Note that there are many situations where it may be necessary to find the best data compressor out of a given set. In such a case, it is often done by comparing compressors empirically. One of the goals of this work is to turn such a selection process into a part of the data compression method, automating and optimizing it.
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McGeoch, Catherine C. "Data Compression." American Mathematical Monthly 100, no. 5 (May 1993): 493. http://dx.doi.org/10.2307/2324310.

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Helman, D. R., and G. G. Langdon. "Data compression." IEEE Potentials 7, no. 1 (February 1988): 25–28. http://dx.doi.org/10.1109/45.1889.

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Lelewer, Debra A., and Daniel S. Hirschberg. "Data compression." ACM Computing Surveys 19, no. 3 (September 1987): 261–96. http://dx.doi.org/10.1145/45072.45074.

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Dissertations / Theses on the topic "Data compression"

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Williams, Ross Neil. "Adaptive data compression." Adelaide, 1989. http://web4.library.adelaide.edu.au/theses/09PH/09phw7262.pdf.

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Steinruecken, Christian. "Lossless data compression." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709134.

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Lindsay, Robert A., and B. V. Cox. "UNIVERSAL DATA COMPRESSION." International Foundation for Telemetering, 1985. http://hdl.handle.net/10150/615552.

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International Telemetering Conference Proceedings / October 28-31, 1985 / Riviera Hotel, Las Vegas, Nevada<br>Universal and adaptive data compression techniques have the capability to globally compress all types of data without loss of information but have the disadvantage of complexity and computation speed. Advances in hardware speed and the reduction of computational costs have made universal data compression feasible. Implementations of the Adaptive Huffman and Lempel-Ziv compression algorithms are evaluated for performance. Compression ratios versus run times for different size data files are graphically presented and discussed in the paper. Required adjustments needed for optimum performance of the algorithms relative to theoretical achievable limits will be outlined.
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Radhakrishnan, Radhika. "Genome data modeling and data compression." abstract and full text PDF (free order & download UNR users only), 2007. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1447611.

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García, Sobrino Francisco Joaquín. "Sounder spectral data compression." Doctoral thesis, Universitat Autònoma de Barcelona, 2018. http://hdl.handle.net/10803/663984.

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IASI (Infrared Atmospheric Sounding Interferometer) es un espectrómetro basado en la transformada de Fourier diseñado para medir radiación infrarroja emitida por La Tierra. A partir de estas mediciones se generan datos con una precisión y resolución espectral sin precedentes. Esta información es útil para obtener perfiles de temperatura y humedad, así como concentraciones de gases traza, que son esenciales para la comprensión y monitorización del clima y para realizar previsiones atmosféricas. La alta resolución espectral, espacial y temporal de los datos producidos por el instrumento implica generar productos con un tamaño considerablemente grande, lo que demanda el uso de técnicas de compresión eficientes para mejorar tanto las capacidades de transmisión como las de almacenamiento. En esta tesis se realiza un exhaustivo análisis de la compresión de datos IASI y se proporcionan recomendaciones para generar datos reconstruidos útiles para el usuario final. En este análisis se utilizan datos IASI transmitidos a las estaciones de recepción (productos IASI L0) y datos destinados a usuarios finales que son distribuidos a centros de predicción numérica y a la comunidad científica en general (productos IASI L1C). Para comprender mejor la naturaleza de los datos capturados por el instrumento se analizan las estadísiticas de la información y el rendimiento de varias técnicas de compresión en datos IASI L0. Se estudia la entropía de orden-0 y las entropías contextuales de orden-1, orden-2 y orden-3. Este estudio revela que el tamaño de los datos se podría reducir considerablemente explotando la entropía de orden-0. Ganancias más significativas se podrían conseguir si se utilizaran modelos contextuales. También se investiga el rendimiento de varias técnicas de compresión sin pérdida. Los resultados experimentales sugieren que se puede alcanzar un ratio de compresión de 2,6:1, lo que implica que sería posible transmitir más datos manteniendo la tasa de transmisión original o, como alternativa, la tasa de transmisión del instrumento se podría reducir. También se realiza un exhaustivo análisis de la compresión de datos IASI L1C donde se evalúa el rendimiento de varias transformadas espectrales y técnicas de compresión. La experimentación abarca compresión sin pérdida, compresión casi sin pérdida y compresión con pérdida sobre una amplia gama de productos IASI-A e IASI-B. Para compresión sin pérdida es posible conseguir ratios de compresión superiores a 2,5:1. Para compresión casi sin pérdida y compresión con pérdida se pueden alcanzar ratios de compresión mayores a la vez que se producen espectros reconstruidos de calidad. Aunque la compresión casi sin pérdida y la compresión con pérdida producen ratios de compresión altos, la utilidad de los espectros reconstruidos se puede ver comprometida ya que cierta información es eliminada durante la etapa de compresión. En consecuencia, se estudia el impacto de la compresión casi sin pérdida y la compresión con pérdida en aplicaciones de usuario final. Concretamente, se evalúa el impacto de la compresión en datos IASI L1C cuando algoritmos estadísticos se utilizan posteriormente para predecir información física a partir de los espectros reconstruidos. Los resultados experimentales muestran que el uso de espectros reconstruidos puede conseguir resultados de predicción competitivos, mejorando incluso los resultados que se obtienen cuando se utilizan datos sin comprimir. A partir del análisis previo se estudia el origen de los beneficios que produce la compresión obteniendo dos observaciones principales. Por un lado, la compresión produce eliminación de ruido y filtrado de la señal lo que beneficia a los métodos de predicción. Por otro lado, la compresión es una forma indirecta de producir regularización espectral y espacial entre píxeles vecinos lo que beneficia a algoritmos que trabajan a nivel de píxel.<br>The Infrared Atmospheric Sounding Interferometer (IASI) is a Fourier Transform Spectrometer implemented on the MetOp satellite series. The instrument is intended to measure infrared radiation emitted from the Earth. IASI produces data with unprecedented accuracy and spectral resolution. Notably, the sounder harvests spectral information to derive temperature and moisture profiles, as well as concentrations of trace gases, essential for the understanding of weather, for climate monitoring, and for atmospheric forecasts. The large spectral, spatial, and temporal resolution of the data collected by the instrument involves generating products with a considerably large size, about 16 Gigabytes per day by each of the IASI-A and IASI-B instruments currently operated. The amount of data produced by IASI demands efficient compression techniques to improve both the transmission and the storage capabilities. This thesis supplies a comprehensive analysis of IASI data compression and provides effective recommendations to produce useful reconstructed spectra. The study analyzes data at different processing stages. Specifically, we use data transmitted by the instrument to the reception stations (IASI L0 products) and end-user data disseminated to the Numerical Weather Prediction (NWP) centres and the scientific community (IASI L1C products). In order to better understand the nature of the data collected by the instrument, we analyze the information statistics and the compression performance of several coding strategies and techniques on IASI L0 data. The order-0 entropy and the order-1, order-2, and order-3 context-based entropies are analyzed in several IASI L0 products. This study reveals that the size of the data could be considerably reduced by exploiting the order-0 entropy. More significant gains could be achieved if contextual models were used. We also investigate the performance of several state-of-the-art lossless compression techniques. Experimental results suggest that a compression ratio of 2.6:1 can be achieved, which involves that more data could be transmitted at the original transmission rate or, alternatively, the transmission rate of the instrument could be further decreased. A comprehensive study of IASI L1C data compression is performed as well. Several state-of-the-art spectral transforms and compression techniques are evaluated on IASI L1C spectra. Extensive experiments, which embrace lossless, near-lossless, and lossy compression, are carried out over a wide range of IASI-A and IASI-B orbits. For lossless compression, compression ratios over 2.5:1 can be achieved. For near-lossless and lossy compression, higher compression ratios can be achieved, while producing useful reconstructed spectra. Even though near-lossless and lossy compression produce higher compression ratios compared to lossless compression, the usefulness of the reconstructed spectra may be compromised because some information is removed during the compression stage. Therefore, we investigate the impact of near-lossless and lossy compression on end-user applications. Specifically, the impact of compression on IASI L1C data is evaluated when statistical retrieval algorithms are later used to retrieve physical information. Experimental results reveal that the reconstructed spectra can enable competitive retrieval performance, improving the results achieved for the uncompressed data, even at high compression ratios. We extend the previous study to a real scenario, where spectra from different disjoint orbits are used in the retrieval stage. Experimental results suggest that the benefits produced by compression are still significant. We also investigate the origin of these benefits. On the one hand, results illustrate that compression performs signal filtering and denoising, which benefits the retrieval methods. On the other hand, compression is an indirect way to produce spectral and spatial regularization, which helps pixel-wise statistical algorithms.
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Du, Toit Benjamin David. "Data Compression and Quantization." Diss., University of Pretoria, 2014. http://hdl.handle.net/2263/79233.

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Data Compression Due to limitations in data storage and bandwidth, data of all types has often required compression. This need has spawned many different methods of compressing data. In certain situations the fidelity of the data can be compromised and unnecessary information can be discarded, while in other situations, the fidelity of the data is necessary for the data to be useful thereby requiring methods of reducing the data storage requirements without discarding any information. The theory of data compression has received much attention over the past half century, with some of the most important work done by Claude E. Shannon in the 1940’s and 1950’s and at present topics such as Information and Coding Theory, which encompass a wide variety of sciences, continue to make headway into the interesting and highly applicable topic of data compression. Quantization Quantization is a broad notion used in several fields especially in the sciences, including signal processing, quantum physics, computer science, geometry, music and others. The concept of quantization is related to the idea of grouping, dividing or approximating some physical quantity by a set of small discrete measurements. Data Quantization involves the discretization of data, or the approximation of large data sets by smaller data sets. This mini dissertation is a research dissertation that considers how data, which is of a statistical nature, can be quantized and compressed.<br>Dissertation (MSc)--University of Pretoria, 2014.<br>Statistics<br>MSc<br>Unrestricted
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Roguski, Łukasz 1987. "High-throughput sequencing data compression." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/565775.

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Thanks to advances in sequencing technologies, biomedical research has experienced a revolution over recent years, resulting in an explosion in the amount of genomic data being generated worldwide. The typical space requirement for storing sequencing data produced by a medium-scale experiment lies in the range of tens to hundreds of gigabytes, with multiple files in different formats being produced by each experiment. The current de facto standard file formats used to represent genomic data are text-based. For practical reasons, these are stored in compressed form. In most cases, such storage methods rely on general-purpose text compressors, such as gzip. Unfortunately, however, these methods are unable to exploit the information models specific to sequencing data, and as a result they usually provide limited functionality and insufficient savings in storage space. This explains why relatively basic operations such as processing, storage, and transfer of genomic data have become a typical bottleneck of current analysis setups. Therefore, this thesis focuses on methods to efficiently store and compress the data generated from sequencing experiments. First, we propose a novel general purpose FASTQ files compressor. Compared to gzip, it achieves a significant reduction in the size of the resulting archive, while also offering high data processing speed. Next, we present compression methods that exploit the high sequence redundancy present in sequencing data. These methods achieve the best compression ratio among current state-of-the-art FASTQ compressors, without using any external reference sequence. We also demonstrate different lossy compression approaches to store auxiliary sequencing data, which allow for further reductions in size. Finally, we propose a flexible framework and data format, which allows one to semi-automatically generate compression solutions which are not tied to any specific genomic file format. To facilitate data management needed by complex pipelines, multiple genomic datasets having heterogeneous formats can be stored together in configurable containers, with an option to perform custom queries over the stored data. Moreover, we show that simple solutions based on our framework can achieve results comparable to those of state-of-the-art format-specific compressors. Overall, the solutions developed and described in this thesis can easily be incorporated into current pipelines for the analysis of genomic data. Taken together, they provide grounds for the development of integrated approaches towards efficient storage and management of such data.<br>Gràcies als avenços en el camp de les tecnologies de seqüenciació, en els darrers anys la recerca biomèdica ha viscut una revolució, que ha tingut com un dels resultats l'explosió del volum de dades genòmiques generades arreu del món. La mida típica de les dades de seqüenciació generades en experiments d'escala mitjana acostuma a situar-se en un rang entre deu i cent gigabytes, que s'emmagatzemen en diversos arxius en diferents formats produïts en cada experiment. Els formats estàndards actuals de facto de representació de dades genòmiques són en format textual. Per raons pràctiques, les dades necessiten ser emmagatzemades en format comprimit. En la majoria dels casos, aquests mètodes de compressió es basen en compressors de text de caràcter general, com ara gzip. Amb tot, no permeten explotar els models d'informació especifícs de dades de seqüenciació. És per això que proporcionen funcionalitats limitades i estalvi insuficient d'espai d'emmagatzematge. Això explica per què operacions relativament bàsiques, com ara el processament, l'emmagatzematge i la transferència de dades genòmiques, s'han convertit en un dels principals obstacles de processos actuals d'anàlisi. Per tot això, aquesta tesi se centra en mètodes d'emmagatzematge i compressió eficients de dades generades en experiments de sequenciació. En primer lloc, proposem un compressor innovador d'arxius FASTQ de propòsit general. A diferència de gzip, aquest compressor permet reduir de manera significativa la mida de l'arxiu resultant del procés de compressió. A més a més, aquesta eina permet processar les dades a una velocitat alta. A continuació, presentem mètodes de compressió que fan ús de l'alta redundància de seqüències present en les dades de seqüenciació. Aquests mètodes obtenen la millor ratio de compressió d'entre els compressors FASTQ del marc teòric actual, sense fer ús de cap referència externa. També mostrem aproximacions de compressió amb pèrdua per emmagatzemar dades de seqüenciació auxiliars, que permeten reduir encara més la mida de les dades. En últim lloc, aportem un sistema flexible de compressió i un format de dades. Aquest sistema fa possible generar de manera semi-automàtica solucions de compressió que no estan lligades a cap mena de format específic d'arxius de dades genòmiques. Per tal de facilitar la gestió complexa de dades, diversos conjunts de dades amb formats heterogenis poden ser emmagatzemats en contenidors configurables amb l'opció de dur a terme consultes personalitzades sobre les dades emmagatzemades. A més a més, exposem que les solucions simples basades en el nostre sistema poden obtenir resultats comparables als compressors de format específic de l'estat de l'art. En resum, les solucions desenvolupades i descrites en aquesta tesi poden ser incorporades amb facilitat en processos d'anàlisi de dades genòmiques. Si prenem aquestes solucions conjuntament, aporten una base sòlida per al desenvolupament d'aproximacions completes encaminades a l'emmagatzematge i gestió eficient de dades genòmiques.
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Frimpong-Ansah, K. "Adaptive data compression with memory." Thesis, Imperial College London, 1986. http://hdl.handle.net/10044/1/38008.

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Kretzmann, Jane Lee. "Compression of bitmapped graphic data." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/25761.

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This paper explores the general topic of data compression, with emphasis on application of the techniques to graphic bitmapped data. Run-length encoding, statistical encoding (including Huffman codes), and relative encoding are examined and evaluated. A compression application of the Huffman coding of a run-length encoded file is designed and partially implemented in Chapter VII. A listing of the computer program which performs the compression is included as an appendix. Possibilities for further study are suggested. Data compression, Compression, Graphics, Bitmapped, Computer network, Run-length encoding, Huffman codes, Lossless, Lossy, Relative encoding, Statistical encoding, Computer program. (jes)
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Barr, Kenneth C. (Kenneth Charles) 1978. "Energy aware lossless data compression." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87316.

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Books on the topic "Data compression"

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Salomon, David. Data Compression. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9.

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Salomon, David. Data Compression. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-86092-8.

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Huang, Bormin, ed. Satellite Data Compression. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1183-3.

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Williams, Ross N. Adaptive Data Compression. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-4046-5.

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Motta, Giovanni, Francesco Rizzo, and James A. Storer, eds. Hyperspectral Data Compression. Boston: Kluwer Academic Publishers, 2006. http://dx.doi.org/10.1007/0-387-28600-4.

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Huang, Bormin. Satellite data compression. New York, NY: Springer Science+Business Media, LLC, 2011.

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Giovanni, Motta, Rizzo Francesco, and Storer James A. 1953-, eds. Hyperspectral data compression. New York: Springer, 2005.

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Williams, Ross N. Adaptive Data Compression. Boston: Kluwer Academic Publishers, 1991.

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Mark, Nelson. The data compression book: Featuring fast, efficient data compresssion techniques in C. Redwood City, CA: M & T, 1991.

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Salomon, David, and Giovanni Motta. Handbook of Data Compression. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84882-903-9.

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Book chapters on the topic "Data compression"

1

Salomon, David. "Image Compression." In Data Compression, 163–249. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_4.

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Salomon, David. "Image Compression." In Data Compression, 221–456. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-86092-8_5.

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Salomon, David. "Video Compression." In Data Compression, 581–630. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-86092-8_7.

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Salomon, David. "Audio Compression." In Data Compression, 631–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-86092-8_8.

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Salomon, David. "Basic Techniques." In Data Compression, 1–19. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_1.

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Salomon, David. "Error Correcting Codes." In Data Compression, 337–48. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_10.

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Salomon, David. "Fourier Transform." In Data Compression, 349–53. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_11.

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Salomon, David. "Group 4 Codes Summary." In Data Compression, 355–56. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_12.

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Salomon, David. "Hashing." In Data Compression, 357–60. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_13.

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Salomon, David. "Interpolating Polynomials." In Data Compression, 361–66. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4757-2939-9_14.

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Conference papers on the topic "Data compression"

1

Qin, Liang, and Jie Sun. "Model Compression for Data Compression: Neural Network Based Lossless Compressor Made Practical." In 2023 Data Compression Conference (DCC). IEEE, 2023. http://dx.doi.org/10.1109/dcc55655.2023.00013.

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Marin, Jeison, Leonardo Betancur, and Henry Arguello. "Compression Ratio Design in Compressive Spectral Imaging." In 2016 Data Compression Conference (DCC). IEEE, 2016. http://dx.doi.org/10.1109/dcc.2016.81.

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"Author Index." In Data Compression Conference. IEEE, 2005. http://dx.doi.org/10.1109/dcc.2005.19.

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Sangho Yoon, Chee Sun Won, Kyungsuk Pyun, and R. M. Gray. "Image classification using GMM with context information and with a solution of singular covariance problem." In Data Compression Conference. IEEE, 2003. http://dx.doi.org/10.1109/dcc.2003.1194076.

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"Author index." In Data Compression Conference. IEEE, 2003. http://dx.doi.org/10.1109/dcc.2003.1194078.

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"Proceedings. DCC 2005. Data Compression Conference." In Data Compression Conference. IEEE, 2005. http://dx.doi.org/10.1109/dcc.2005.28.

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"Table of Contents." In Data Compression Conference. IEEE, 2005. http://dx.doi.org/10.1109/dcc.2005.84.

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"Title Page." In Data Compression Conference. IEEE, 2005. http://dx.doi.org/10.1109/dcc.2005.89.

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"Proceedings DCC 2003. Data Compression Conference." In Data Compression Conference. IEEE, 2003. http://dx.doi.org/10.1109/dcc.2003.1193990.

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Mishali, Moshe, and Yonina C. Eldar. "Xampling: Analog Data Compression." In 2010 Data Compression Conference. IEEE, 2010. http://dx.doi.org/10.1109/dcc.2010.39.

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Reports on the topic "Data compression"

1

Creusere, Charles D., and Jim Witham. Data Compression Project. Fort Belvoir, VA: Defense Technical Information Center, October 1999. http://dx.doi.org/10.21236/ada370497.

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Pan, David. Efficient Data Compression Techniques for Weather Data. Fort Belvoir, VA: Defense Technical Information Center, January 2011. http://dx.doi.org/10.21236/ada540395.

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Choi, Junho, and Mitchell R. Grunes. Lossless Data Compression of Packet Data Streams,. Fort Belvoir, VA: Defense Technical Information Center, February 1996. http://dx.doi.org/10.21236/ada304792.

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Gryder, R., and K. Hake. Survey of data compression techniques. Office of Scientific and Technical Information (OSTI), September 1991. http://dx.doi.org/10.2172/10107839.

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Gryder, R., and K. Hake. Survey of data compression techniques. Office of Scientific and Technical Information (OSTI), September 1991. http://dx.doi.org/10.2172/5926128.

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Duff, C. R. W. Data compression and computation speed. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1986. http://dx.doi.org/10.4095/315270.

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Clark, D. Argon Excluder Foam Compression Data. Office of Scientific and Technical Information (OSTI), July 1991. http://dx.doi.org/10.2172/1031764.

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Horan, Shield. Data Compression Techniques to Reduce Bandwidth. Fort Belvoir, VA: Defense Technical Information Center, August 2000. http://dx.doi.org/10.21236/ada399290.

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Senecal, Joshua G. Length-Limited Data Transformation and Compression. Office of Scientific and Technical Information (OSTI), September 2005. http://dx.doi.org/10.2172/877882.

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Perkins, William W. Data Compression With Application to Geo-Location. Fort Belvoir, VA: Defense Technical Information Center, August 2010. http://dx.doi.org/10.21236/ada532376.

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