Data Compression in the Petascale Astronomy Era: a GERLUMPH case study

As the volume of data grows, astronomers are increasingly faced with choices on what data to keep -- and what to throw away. Recent work evaluating the JPEG2000 (ISO/IEC 15444) standards as a future data format standard in astronomy has shown promising results on observational data. However, there is still a need to evaluate its potential on other type of astronomical data, such as from numerical simulations. GERLUMPH (the GPU-Enabled High Resolution cosmological MicroLensing parameter survey) represents an example of a data intensive project in theoretical astrophysics. In the next phase of processing, the ~27 terabyte GERLUMPH dataset is set to grow by a factor of 100 -- well beyond the current storage capabilities of the supercomputing facility on which it resides. In order to minimise bandwidth usage, file transfer time, and storage space, this work evaluates several data compression techniques. Specifically, we investigate off-the-shelf and custom lossless compression algorithms as well as the lossy JPEG2000 compression format. Results of lossless compression algorithms on GERLUMPH data products show small compression ratios (1.35:1 to 4.69:1 of input file size) varying with the nature of the input data. Our results suggest that JPEG2000 could be suitable for other numerical datasets stored as gridded data or volumetric data. When approaching lossy data compression, one should keep in mind the intended purposes of the data to be compressed, and evaluate the effect of the loss on future analysis. In our case study, lossy compression and a high compression ratio do not significantly compromise the intended use of the data for constraining quasar source profiles from cosmological microlensing.Comment: 15 pages, 9 figures, 5 tables. Published in the Special Issue of Astronomy & Computing on The future of astronomical data format

RDF-TR: Exploiting structural redundancies to boost RDF compression

The number and volume of semantic data have grown impressively over the last decade, promoting compression as an essential tool for RDF preservation, sharing and management. In contrast to universal compressors, RDF compression techniques are able to detect and exploit specific forms of redundancy in RDF data. Thus, state-of-the-art RDF compressors excel at exploiting syntactic and semantic redundancies, i.e., repetitions in the serialization format and information that can be inferred implicitly. However, little attention has been paid to the existence of structural patterns within the RDF dataset; i.e. structural redundancy. In this paper, we analyze structural regularities in real-world datasets, and show three schema-based sources of redundancies that underpin the schema-relaxed nature of RDF. Then, we propose RDF-Tr (RDF Triples Reorganizer), a preprocessing technique that discovers and removes this kind of redundancy before the RDF dataset is effectively compressed. In particular, RDF-Tr groups subjects that are described by the same predicates, and locally re-codes the objects related to these predicates. Finally, we integrate RDF-Tr with two RDF compressors, HDT and k2-triples. Our experiments show that using RDF-Tr with these compressors improves by up to 2.3 times their original effectiveness, outperforming the most prominent state-of-the-art techniques

Selection of compressible signals from telemetry data

Sensors are deployed in all aspects of modern city infrastructure and generate vast amounts of data. Only subsets of this data, however, are relevant to individual organisations. For example, a local council may collect suspension movement from vehicles to detect pot-holes, but this data is not relevant when assessing traffic flow. Supervised feature selection aims to find the set of signals that best predict a target variable. Typical approaches use either measures of correlation or similarity, as in filter methods, or predictive power in a learned model, as in wrapper methods. In both approaches selected features often have high entropies and are not suitable for compression. This is of particular issue in the automotive domain where fast communication and archival of vehicle telemetry data is likely to be prevalent in the near future, especially with technologies such as V2V and V2X. In this paper, we adapt a popular feature selection filter method to consider the compressibility of signals being selected for use in a predictive model. In particular, we add a compression term to the Minimal Redundancy Maximal Relevance (MRMR) filter and introduce Minimal Redundancy Maximal Relevance And Compression (MRMRAC). Using MRMRAC, we then select features from the Controller Area Network (CAN) and predict each of current instantaneous fuel consumption, engine torque, vehicle speed, and gear position, using a Support Vector Machine (SVM). We show that while performance is slightly lower when compression is considered, the compressibility of the selected features is significantly improved

Secure and efficient storage of multimedia: content in public cloud environments using joint compression and encryption

The Cloud Computing is a paradigm still with many unexplored areas ranging from the technological component to the de nition of new business models, but that is revolutionizing the way we design, implement and manage the entire infrastructure of information technology. The Infrastructure as a Service is the delivery of computing infrastructure, typically a virtual data center, along with a set of APIs that allow applications, in an automatic way, can control the resources they wish to use. The choice of the service provider and how it applies to their business model may lead to higher or lower cost in the operation and maintenance of applications near the suppliers. In this sense, this work proposed to carry out a literature review on the topic of Cloud Computing, secure storage and transmission of multimedia content, using lossless compression, in public cloud environments, and implement this system by building an application that manages data in public cloud environments (dropbox and meocloud). An application was built during this dissertation that meets the objectives set. This system provides the user a wide range of functions of data management in public cloud environments, for that the user only have to login to the system with his/her credentials, after performing the login, through the Oauth 1.0 protocol (authorization protocol) is generated an access token, this token is generated only with the consent of the user and allows the application to get access to data/user les without having to use credentials. With this token the framework can now operate and unlock the full potential of its functions. With this application is also available to the user functions of compression and encryption so that user can make the most of his/her cloud storage system securely. The compression function works using the compression algorithm LZMA being only necessary for the user to choose the les to be compressed. Relatively to encryption it will be used the encryption algorithm AES (Advanced Encryption Standard) that works with a 128 bit symmetric key de ned by user. We build the research into two distinct and complementary parts: The rst part consists of the theoretical foundation and the second part is the development of computer application where the data is managed, compressed, stored, transmitted in various environments of cloud computing. The theoretical framework is organized into two chapters, chapter 2 - Background on Cloud Storage and chapter 3 - Data compression. Sought through theoretical foundation demonstrate the relevance of the research, convey some of the pertinent theories and input whenever possible, research in the area. The second part of the work was devoted to the development of the application in cloud environment. We showed how we generated the application, presented the features, advantages, and safety standards for the data. Finally, we re ect on the results, according to the theoretical framework made in the rst part and platform development. We think that the work obtained is positive and that ts the goals we set ourselves to achieve. This research has some limitations, we believe that the time for completion was scarce and the implementation of the platform could bene t from the implementation of other features.In future research it would be appropriate to continue the project expanding the capabilities of the application, test the operation with other users and make comparative tests.A Computação em nuvem é um paradigma ainda com muitas áreas por explorar que vão desde a componente tecnológica à definição de novos modelos de negócio, mas que está a revolucionar a forma como projetamos, implementamos e gerimos toda a infraestrutura da tecnologia da informação. A Infraestrutura como Serviço representa a disponibilização da infraestrutura computacional, tipicamente um datacenter virtual, juntamente com um conjunto de APls que permitirá que aplicações, de forma automática, possam controlar os recursos que pretendem utilizar_ A escolha do fornecedor de serviços e a forma como este aplica o seu modelo de negócio poderão determinar um maior ou menor custo na operacionalização e manutenção das aplicações junto dos fornecedores. Neste sentido, esta dissertação propôs· se efetuar uma revisão bibliográfica sobre a temática da Computação em nuvem, a transmissão e o armazenamento seguro de conteúdos multimédia, utilizando a compressão sem perdas, em ambientes em nuvem públicos, e implementar um sistema deste tipo através da construção de uma aplicação que faz a gestão dos dados em ambientes de nuvem pública (dropbox e meocloud). Foi construída uma aplicação no decorrer desta dissertação que vai de encontro aos objectivos definidos. Este sistema fornece ao utilizador uma variada gama de funções de gestão de dados em ambientes de nuvem pública, para isso o utilizador tem apenas que realizar o login no sistema com as suas credenciais, após a realização de login, através do protocolo Oauth 1.0 (protocolo de autorização) é gerado um token de acesso, este token só é gerado com o consentimento do utilizador e permite que a aplicação tenha acesso aos dados / ficheiros do utilizador ~em que seja necessário utilizar as credenciais. Com este token a aplicação pode agora operar e disponibilizar todo o potencial das suas funções. Com esta aplicação é também disponibilizado ao utilizador funções de compressão e encriptação de modo a que possa usufruir ao máximo do seu sistema de armazenamento cloud com segurança. A função de compressão funciona utilizando o algoritmo de compressão LZMA sendo apenas necessário que o utilizador escolha os ficheiros a comprimir. Relativamente à cifragem utilizamos o algoritmo AES (Advanced Encryption Standard) que funciona com uma chave simétrica de 128bits definida pelo utilizador. Alicerçámos a investigação em duas partes distintas e complementares: a primeira parte é composta pela fundamentação teórica e a segunda parte consiste no desenvolvimento da aplicação informática em que os dados são geridos, comprimidos, armazenados, transmitidos em vários ambientes de computação em nuvem. A fundamentação teórica encontra-se organizada em dois capítulos, o capítulo 2 - "Background on Cloud Storage" e o capítulo 3 "Data Compression", Procurámos, através da fundamentação teórica, demonstrar a pertinência da investigação. transmitir algumas das teorias pertinentes e introduzir, sempre que possível, investigações existentes na área. A segunda parte do trabalho foi dedicada ao desenvolvimento da aplicação em ambiente "cloud". Evidenciámos o modo como gerámos a aplicação, apresentámos as funcionalidades, as vantagens. Por fim, refletimos sobre os resultados , de acordo com o enquadramento teórico efetuado na primeira parte e o desenvolvimento da plataforma. Pensamos que o trabalho obtido é positivo e que se enquadra nos objetivos que nos propusemos atingir. Este trabalho de investigação apresenta algumas limitações, consideramos que o tempo para a sua execução foi escasso e a implementação da plataforma poderia beneficiar com a implementação de outras funcionalidades. Em investigações futuras seria pertinente dar continuidade ao projeto ampliando as potencialidades da aplicação, testar o funcionamento com outros utilizadores e efetuar testes comparativos.Fundação para a Ciência e a Tecnologia (FCT

Reducing the loss of information through annealing text distortion

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Granados, A. ;Cebrian, M. ; Camacho, D. ; de Borja Rodriguez, F. "Reducing the Loss of Information through Annealing Text Distortion". IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 7 pp. 1090 - 1102, July 2011Compression distances have been widely used in knowledge discovery and data mining. They are parameter-free, widely applicable, and very effective in several domains. However, little has been done to interpret their results or to explain their behavior. In this paper, we take a step toward understanding compression distances by performing an experimental evaluation of the impact of several kinds of information distortion on compression-based text clustering. We show how progressively removing words in such a way that the complexity of a document is slowly reduced helps the compression-based text clustering and improves its accuracy. In fact, we show how the nondistorted text clustering can be improved by means of annealing text distortion. The experimental results shown in this paper are consistent using different data sets, and different compression algorithms belonging to the most important compression families: Lempel-Ziv, Statistical and Block-Sorting.This work was supported by the Spanish Ministry of Education and Science under TIN2010-19872 and TIN2010-19607 projects

Handling Massive N-Gram Datasets Efficiently

This paper deals with the two fundamental problems concerning the handling of large n-gram language models: indexing, that is compressing the n-gram strings and associated satellite data without compromising their retrieval speed; and estimation, that is computing the probability distribution of the strings from a large textual source. Regarding the problem of indexing, we describe compressed, exact and lossless data structures that achieve, at the same time, high space reductions and no time degradation with respect to state-of-the-art solutions and related software packages. In particular, we present a compressed trie data structure in which each word following a context of fixed length k, i.e., its preceding k words, is encoded as an integer whose value is proportional to the number of words that follow such context. Since the number of words following a given context is typically very small in natural languages, we lower the space of representation to compression levels that were never achieved before. Despite the significant savings in space, our technique introduces a negligible penalty at query time. Regarding the problem of estimation, we present a novel algorithm for estimating modified Kneser-Ney language models, that have emerged as the de-facto choice for language modeling in both academia and industry, thanks to their relatively low perplexity performance. Estimating such models from large textual sources poses the challenge of devising algorithms that make a parsimonious use of the disk. The state-of-the-art algorithm uses three sorting steps in external memory: we show an improved construction that requires only one sorting step thanks to exploiting the properties of the extracted n-gram strings. With an extensive experimental analysis performed on billions of n-grams, we show an average improvement of 4.5X on the total running time of the state-of-the-art approach.Comment: Published in ACM Transactions on Information Systems (TOIS), February 2019, Article No: 2

Data Compression in Multi-Hop Large-Scale Wireless Sensor Networks

Data collection from a multi-hop large-scale outdoor WSN deployment for environmental monitoring is full of challenges due to the severe resource constraints on small battery-operated motes (e.g., bandwidth, memory, power, and computing capacity) and the highly dynamic wireless link conditions in an outdoor communication environment. We present a compressed sensing approach which can recover the sensing data at the sink with good accuracy when very few packets are collected, thus leading to a significant reduction of the network traffic and an extension of the WSN lifetime. Interplaying with the dynamic WSN routing topology, the proposed approach is efficient and simple to implement on the resource-constrained motes without motes storing of a part of random measurement matrix, as opposed to other existing compressed sensing based schemes. We provide a systematic method via machine learning to find a suitable representation basis, for the given WSN deployment and data field, which is both sparse and incoherent with the measurement matrix in the compressed sensing. We validate our approach and evaluate its performance using our real-world multi-hop WSN testbed deployment in situ in collecting the humidity and soil moisture data. The results show that our approach significantly outperforms three other compressed sensing based algorithms regarding the data recovery accuracy for the entire WSN observation field under drastically reduced communication costs. For some WSN scenarios, compressed sensing may not be applicable. Therefore we also design a generalized predictive coding framework for unified lossless and lossy data compression. In addition, we devise a novel algorithm for lossless compression to significantly improve data compression performance for variouSs data collections and applications in WSNs. Rigorous simulations show our proposed framework and compression algorithm outperform several recent popular compression algorithms for wireless sensor networks such as LEC, S-LZW and LTC using various real-world sensor data sets, demonstrating the merit of the proposed framework for unified temporal lossless and lossy data compression in WSNs

Singular Value Decomposition Based Image Coding for Achieving Additional Compression to JPEG Images

Computer technology these days is most focused on storage space and speed. Considerable advancements in this direction can be achieved through the usage of digital image compression techniques. In this paper we present a well studied singular value decomposition based JPEG image compression technique. Singular Value Decomposition is a way of factorizing matrices into a series of linear approximations that expose the underlying structure of the matrix. SVD is extraordinarily useful and has many applications such as data analysis, signal processing, pattern recognition, objects detection and weather prediction. An attempt is made to implement this method of factorization to perform second round of compression on JPEG images to optimize storage space. Compression is further enhanced by the removal of singularity after the initial compression performed using SVD. MATLAB R2010a with image processing toolbox is used as the development tool for implementing the algorithm