Space and Earth Science Data Compression Workshop

The workshop explored opportunities for data compression to enhance the collection and analysis of space and Earth science data. The focus was on scientists' data requirements, as well as constraints imposed by the data collection, transmission, distribution, and archival systems. The workshop consisted of several invited papers; two described information systems for space and Earth science data, four depicted analysis scenarios for extracting information of scientific interest from data collected by Earth orbiting and deep space platforms, and a final one was a general tutorial on image data compression

Using semantic knowledge to improve compression on log files

With the move towards global and multi-national companies, information technology infrastructure requirements are increasing. As the size of these computer networks increases, it becomes more and more difficult to monitor, control, and secure them. Networks consist of a number of diverse devices, sensors, and gateways which are often spread over large geographical areas. Each of these devices produce log files which need to be analysed and monitored to provide network security and satisfy regulations. Data compression programs such as gzip and bzip2 are commonly used to reduce the quantity of data for archival purposes after the log files have been rotated. However, there are many other compression programs which exist - each with their own advantages and disadvantages. These programs each use a different amount of memory and take different compression and decompression times to achieve different compression ratios. System log files also contain redundancy which is not necessarily exploited by standard compression programs. Log messages usually use a similar format with a defined syntax. In the log files, all the ASCII characters are not used and the messages contain certain "phrases" which often repeated. This thesis investigates the use of compression as a means of data reduction and how the use of semantic knowledge can improve data compression (also applying results to different scenarios that can occur in a distributed computing environment). It presents the results of a series of tests performed on different log files. It also examines the semantic knowledge which exists in maillog files and how it can be exploited to improve the compression results. The results from a series of text preprocessors which exploit this knowledge are presented and evaluated. These preprocessors include: one which replaces the timestamps and IP addresses with their binary equivalents and one which replaces words from a dictionary with unused ASCII characters. In this thesis, data compression is shown to be an effective method of data reduction producing up to 98 percent reduction in filesize on a corpus of log files. The use of preprocessors which exploit semantic knowledge results in up to 56 percent improvement in overall compression time and up to 32 percent reduction in compressed size.TeXpdfTeX-1.40.

The 1993 Space and Earth Science Data Compression Workshop

The Earth Observing System Data and Information System (EOSDIS) is described in terms of its data volume, data rate, and data distribution requirements. Opportunities for data compression in EOSDIS are discussed

The Space and Earth Science Data Compression Workshop

This document is the proceedings from a Space and Earth Science Data Compression Workshop, which was held on March 27, 1992, at the Snowbird Conference Center in Snowbird, Utah. This workshop was held in conjunction with the 1992 Data Compression Conference (DCC '92), which was held at the same location, March 24-26, 1992. The workshop explored opportunities for data compression to enhance the collection and analysis of space and Earth science data. The workshop consisted of eleven papers presented in four sessions. These papers describe research that is integrated into, or has the potential of being integrated into, a particular space and/or Earth science data information system. Presenters were encouraged to take into account the scientists's data requirements, and the constraints imposed by the data collection, transmission, distribution, and archival system

Adaptive edge-based prediction for lossless image compression

Many lossless image compression methods have been suggested with established results hard to surpass. However there are some aspects that can be considered to improve the performance further. This research focuses on two-phase prediction-encoding method, separately studying each and suggesting new techniques.;In the prediction module, proposed Edge-Based-Predictor (EBP) and Least-Squares-Edge-Based-Predictor (LS-EBP) emphasizes on image edges and make predictions accordingly. EBP is a gradient based nonlinear adaptive predictor. EBP switches between prediction-rules based on few threshold parameters automatically determined by a pre-analysis procedure, which makes a first pass. The LS-EBP also uses these parameters, but optimizes the prediction for each pre-analysis assigned edge location, thus applying least-square approach only at the edge points.;For encoding module: a novel Burrows Wheeler Transform (BWT) inspired method is suggested, which performs better than applying the BWT directly on the images. We also present a context-based adaptive error modeling and encoding scheme. When coupled with the above-mentioned prediction schemes, the result is the best-known compression performance in the genre of compression schemes with same time and space complexity

Compressão e análise de dados genómicos

Doutoramento em InformáticaGenomic sequences are large codi ed messages describing most of the structure of all known living organisms. Since the presentation of the rst genomic sequence, a huge amount of genomics data have been generated, with diversi ed characteristics, rendering the data deluge phenomenon a serious problem in most genomics centers. As such, most of the data are discarded (when possible), while other are compressed using general purpose algorithms, often attaining modest data reduction results. Several speci c algorithms have been proposed for the compression of genomic data, but unfortunately only a few of them have been made available as usable and reliable compression tools. From those, most have been developed to some speci c purpose. In this thesis, we propose a compressor for genomic sequences of multiple natures, able to function in a reference or reference-free mode. Besides, it is very exible and can cope with diverse hardware speci cations. It uses a mixture of nite-context models (FCMs) and eXtended FCMs. The results show improvements over state-of-the-art compressors. Since the compressor can be seen as a unsupervised alignment-free method to estimate algorithmic complexity of genomic sequences, it is the ideal candidate to perform analysis of and between sequences. Accordingly, we de ne a way to approximate directly the Normalized Information Distance, aiming to identify evolutionary similarities in intra- and inter-species. Moreover, we introduce a new concept, the Normalized Relative Compression, that is able to quantify and infer new characteristics of the data, previously undetected by other methods. We also investigate local measures, being able to locate speci c events, using complexity pro les. Furthermore, we present and explore a method based on complexity pro les to detect and visualize genomic rearrangements between sequences, identifying several insights of the genomic evolution of humans. Finally, we introduce the concept of relative uniqueness and apply it to the Ebolavirus, identifying three regions that appear in all the virus sequences outbreak but nowhere in the human genome. In fact, we show that these sequences are su cient to classify di erent sub-species. Also, we identify regions in human chromosomes that are absent from close primates DNA, specifying novel traits in human uniqueness.As sequências genómicas podem ser vistas como grandes mensagens codificadas, descrevendo a maior parte da estrutura de todos os organismos vivos. Desde a apresentação da primeira sequência, um enorme número de dados genómicos tem sido gerado, com diversas características, originando um sério problema de excesso de dados nos principais centros de genómica. Por esta razão, a maioria dos dados é descartada (quando possível), enquanto outros são comprimidos usando algoritmos genéricos, quase sempre obtendo resultados de compressão modestos. Têm também sido propostos alguns algoritmos de compressão para sequências genómicas, mas infelizmente apenas alguns estão disponíveis como ferramentas eficientes e prontas para utilização. Destes, a maioria tem sido utilizada para propósitos específicos. Nesta tese, propomos um compressor para sequências genómicas de natureza múltipla, capaz de funcionar em modo referencial ou sem referência. Além disso, é bastante flexível e pode lidar com diversas especificações de hardware. O compressor usa uma mistura de modelos de contexto-finito (FCMs) e FCMs estendidos. Os resultados mostram melhorias relativamente a compressores estado-dearte. Uma vez que o compressor pode ser visto como um método não supervisionado, que não utiliza alinhamentos para estimar a complexidade algortímica das sequências genómicas, ele é o candidato ideal para realizar análise de e entre sequências. Em conformidade, definimos uma maneira de aproximar directamente a distância de informação normalizada (NID), visando a identificação evolucionária de similaridades em intra e interespécies. Além disso, introduzimos um novo conceito, a compressão relativa normalizada (NRC), que é capaz de quantificar e inferir novas características nos dados, anteriormente indetectados por outros métodos. Investigamos também medidas locais, localizando eventos específicos, usando perfis de complexidade. Propomos e exploramos um novo método baseado em perfis de complexidade para detectar e visualizar rearranjos genómicos entre sequências, identificando algumas características da evolução genómica humana. Por último, introduzimos um novo conceito de singularidade relativa e aplicamo-lo ao Ebolavirus, identificando três regiões presentes em todas as sequências do surto viral, mas ausentes do genoma humano. De facto, mostramos que as três sequências são suficientes para classificar diferentes sub-espécies. Também identificamos regiões nos cromossomas humanos que estão ausentes do ADN de primatas próximos, especificando novas características da singularidade humana

Lossless compression of images with specific characteristics

Doutoramento em Engenharia ElectrotécnicaA compressão de certos tipos de imagens é um desafio para algumas normas de compressão de imagem. Esta tese investiga a compressão sem perdas de imagens com características especiais, em particular imagens simples, imagens de cor indexada e imagens de microarrays. Estamos interessados no desenvolvimento de métodos de compressão completos e no estudo de técnicas de pré-processamento que possam ser utilizadas em conjunto com as normas de compressão de imagem. A esparsidade do histograma, uma propriedade das imagens simples, é um dos assuntos abordados nesta tese. Desenvolvemos uma técnica de pré-processamento, denominada compactação de histogramas, que explora esta propriedade e que pode ser usada em conjunto com as normas de compressão de imagem para um melhoramento significativo da eficiência de compressão. A compactação de histogramas e os algoritmos de reordenação podem ser usados como préprocessamento para melhorar a compressão sem perdas de imagens de cor indexada. Esta tese apresenta vários algoritmos e um estudo abrangente dos métodos já existentes. Métodos específicos, como é o caso da decomposição em árvores binárias, são também estudados e propostos. O uso de microarrays em biologia encontra-se em franca expansão. Devido ao elevado volume de dados gerados por experiência, são necessárias técnicas de compressão sem perdas. Nesta tese, exploramos a utilização de normas de compressão sem perdas e apresentamos novos algoritmos para codificar eficientemente este tipo de imagens, baseados em modelos de contexto finito e codificação aritmética.The compression of some types of images is a challenge for some standard compression techniques. This thesis investigates the lossless compression of images with specific characteristics, namely simple images, color-indexed images and microarray images. We are interested in the development of complete compression methods and in the study of preprocessing algorithms that could be used together with standard compression methods. The histogram sparseness, a property of simple images, is addressed in this thesis. We developed a preprocessing technique, denoted histogram packing, that explores this property and can be used with standard compression methods for improving significantly their efficiency. Histogram packing and palette reordering algorithms can be used as a preprocessing step for improving the lossless compression of color-indexed images. This thesis presents several algorithms and a comprehensive study of the already existing methods. Specific compression methods, such as binary tree decomposition, are also addressed. The use of microarray expression data in state-of-the-art biology has been well established and due to the significant volume of data generated per experiment, efficient lossless compression methods are needed. In this thesis, we explore the use of standard image coding techniques and we present new algorithms to efficiently compress this type of images, based on finite-context modeling and arithmetic coding

Joint source channel coding for progressive image transmission

Recent wavelet-based image compression algorithms achieve best ever performances with fully embedded bit streams. However, those embedded bit streams are very sensitive to channel noise and protections from channel coding are necessary. Typical error correcting capability of channel codes varies according to different channel conditions. Thus, separate design leads to performance degradation relative to what could be achieved through joint design. In joint source-channel coding schemes, the choice of source coding parameters may vary over time and channel conditions. In this research, we proposed a general approach for the evaluation of such joint source-channel coding scheme. Instead of using the average peak signal to noise ratio (PSNR) or distortion as the performance metric, we represent the system performance by its average error-free source coding rate, which is further shown to be an equivalent metric in the optimization problems. The transmissions of embedded image bit streams over memory channels and binary symmetric channels (BSCs) are investigated in this dissertation. Mathematical models were obtained in closed-form by error sequence analysis (ESA). Not surprisingly, models for BSCs are just special cases for those of memory channels. It is also discovered that existing techniques for performance evaluation on memory channels are special cases of this new approach. We further extend the idea to the unequal error protection (UEP) of embedded images sources in BSCs. The optimization problems are completely defined and solved. Compared to the equal error protection (EEP) schemes, about 0.3 dB performance gain is achieved by UEP for typical BSCs. For some memory channel conditions, the performance improvements can be up to 3 dB. Transmission of embedded image bit streams in channels with feedback are also investigated based on the model for memory channels. Compared to the best possible performance achieved on feed forward transmission, feedback leads to about 1.7 dB performance improvement

Universal Lossless Compression with Unknown Alphabets - The Average Case

Universal compression of patterns of sequences generated by independently identically distributed (i.i.d.) sources with unknown, possibly large, alphabets is investigated. A pattern is a sequence of indices that contains all consecutive indices in increasing order of first occurrence. If the alphabet of a source that generated a sequence is unknown, the inevitable cost of coding the unknown alphabet symbols can be exploited to create the pattern of the sequence. This pattern can in turn be compressed by itself. It is shown that if the alphabet size $k$ is essentially small, then the average minimax and maximin redundancies as well as the redundancy of every code for almost every source, when compressing a pattern, consist of at least 0.5 log(n/k^3) bits per each unknown probability parameter, and if all alphabet letters are likely to occur, there exist codes whose redundancy is at most 0.5 log(n/k^2) bits per each unknown probability parameter, where n is the length of the data sequences. Otherwise, if the alphabet is large, these redundancies are essentially at least O(n^{-2/3}) bits per symbol, and there exist codes that achieve redundancy of essentially O(n^{-1/2}) bits per symbol. Two sub-optimal low-complexity sequential algorithms for compression of patterns are presented and their description lengths analyzed, also pointing out that the pattern average universal description length can decrease below the underlying i.i.d.\ entropy for large enough alphabets.Comment: Revised for IEEE Transactions on Information Theor