XML is a widely used data exchange format. The verbose nature of XML leads to the requirement to efficiently store and process this type of data using compression. Various general-purpose transforms and compression techniques exist that can be used to transform and compress XML data. More compact alternatives to XML data have been developed, namely JSON due to the verbosity of XML data. Similarly, there is a requirement to efficiently store and process SMILES data used in Chemoinformatics. General-purpose transforms and compressors can be used to compress this type of data to a certain extent, however, these techniques are not specific to SMILES data. The primary contribution of this research is to provide developers that use XML, JSON or SMILES data, with key knowledge of the best transformation techniques to use with certain types of data, and which compression techniques would provide the best compressed output size and processing times, depending on their requirements. The main study in this thesis, investigates the extent of which using data transforms prior to data compression can further improve the compression of XML and JSON data. It provides a comparative analysis of applying a variety of data transform and data transform variations, to a number of different types of XML and JSON equivalent datasets of various sizes, and applying different general-purpose compression techniques over the transformed data. A case study is also conducted, to investigate data transforms prior to compression to improve the compression of data within a data-specific domain.

XML is a widely used data exchange format. The verbose nature of XML leads to the requirement to efficiently store and process this type of data using compression. Various general-purpose transforms and compression techniques exist that can be used to transform and compress XML data. More compact alternatives to XML data have been developed, namely JSON due to the verbosity of XML data. Similarly, there is a requirement to efficiently store and process SMILES data used in Chemoinformatics. General-purpose transforms and compressors can be used to compress this type of data to a certain extent, however, these techniques are not specific to SMILES data. The primary contribution of this research is to provide developers that use XML, JSON or SMILES data, with key knowledge of the best transformation techniques to use with certain types of data, and which compression techniques would provide the best compressed output size and processing times, depending on their requirements. The main study in this thesis, investigates the extent of which using data transforms prior to data compression can further improve the compression of XML and JSON data. It provides a comparative analysis of applying a variety of data transform and data transform variations, to a number of different types of XML and JSON equivalent datasets of various sizes, and applying different general-purpose compression techniques over the transformed data. A case study is also conducted, to investigate data transforms prior to compression to improve the compression of data within a data-specific domain. / The files of software accompanying this thesis are unable to be presented online with the thesis.

<p>Modern semiconductor design methods makes it possible to design increasingly complex system-on-a-chips (SOCs). Testing such SOCs becomes highly expensive due to the rapidly increasing test data volumes with longer test times as a result. Several approaches exist to compress the test stimuli and where hardware is added for decompression. This master’s thesis presents a test data compression method based on a modified facsimile code. An embedded processor on the SOC is used to decompress and apply the data to the cores of the SOC. The use of already existing hardware reduces the need of additional hardware. </p><p>Test data may be rearranged in some manners which will affect the compression ratio. Several modifications are discussed and tested. To be realistic a decompressing algorithm has to be able to run on a system with limited resources. With an assembler implementation it is shown that the proposed method can be effectively realized in such environments. Experimental results where the proposed method is applied to benchmark circuits show that the method compares well with similar methods. </p><p>A method of including the response vector is also presented. This approach makes it possible to abort a test as soon as an error is discovered, still compressing the data used. To correctly compare the test response with the expected one the data needs to include don’t care bits. The technique uses a mask vector to mark the don’t care bits. The test vector, response vector and mask vector is merged in four different ways to find the most optimal way.</p>

Modern semiconductor design methods makes it possible to design increasingly complex system-on-a-chips (SOCs). Testing such SOCs becomes highly expensive due to the rapidly increasing test data volumes with longer test times as a result. Several approaches exist to compress the test stimuli and where hardware is added for decompression. This master’s thesis presents a test data compression method based on a modified facsimile code. An embedded processor on the SOC is used to decompress and apply the data to the cores of the SOC. The use of already existing hardware reduces the need of additional hardware. Test data may be rearranged in some manners which will affect the compression ratio. Several modifications are discussed and tested. To be realistic a decompressing algorithm has to be able to run on a system with limited resources. With an assembler implementation it is shown that the proposed method can be effectively realized in such environments. Experimental results where the proposed method is applied to benchmark circuits show that the method compares well with similar methods. A method of including the response vector is also presented. This approach makes it possible to abort a test as soon as an error is discovered, still compressing the data used. To correctly compare the test response with the expected one the data needs to include don’t care bits. The technique uses a mask vector to mark the don’t care bits. The test vector, response vector and mask vector is merged in four different ways to find the most optimal way.

We report a new lossless data compression algorithm (LDC) for implementing predictably-fixed compression values. The fuzzy binary and-or algorithm (FBAR), primarily aims to introduce a new model for regular and superdense coding in classical and quantum information theory. Classical coding on x86 machines would not suffice techniques for maximum LDCs generating fixed values of Cr &gt;= 2:1. However, the current model is evaluated to serve multidimensional LDCs with fixed value generations, contrasting the popular methods used in probabilistic LDCs, such as Shannon entropy. The currently introduced entropy is of ‘fuzzy binary’ in a 4D hypercube bit flag model, with a product value of at least 50% compression. We have implemented the compression and simulated the decompression phase for lossless versions of FBAR logic. We further compared our algorithm with the results obtained by other compressors. Our statistical test shows that, the presented algorithm mutably and significantly competes with other LDC algorithms on both, temporal and spatial factors of compression. The current algorithm is a steppingstone to quantum information models solving complex negative entropies, giving double-efficient LDCs &gt; 87.5% space savings. / Vi rapporterar en ny förlustfri komprimering algoritm (MUL) för att genomföra förutsägbart-fast komprimering värden. Den luddiga binär och-eller algoritm (FBAR), syftar bland annat att införa en ny modell för regelbunden och superdense kodning i klassiska och kvantmekaniska information teori. Klassiska kodning på x86-maskiner inte skulle räcka teknik för maximal LDC att skapa fasta värden av Cr &gt;= 2:1. Men den nuvarande modellen utvärderas för att tjäna flerdimensionella LDC med fast värde generationer, där de populära metoder som används i probabilistiska LDC, såsom Shannon entropi. De närvarande in entropi är av &quot;fuzzy binära&quot; i en 4D blixtkub lite flagga modell, med en produkt värde av minst 50% komprimering. Vi har genomfört komprimering och simulerade den tryckfall fasen för förlustfri versioner av FBAR logik. Jämförde vi ytterligare vår algoritm med de resultat som andra kompressorer. Vår statistiska testet visar att den presenterade algoritmen mutably och betydligt konkurrerar med andra LDC algoritmer på båda, tidsmässiga och geografiska faktorer av kompression. Den nuvarande algoritmen är en steppingstone att kvantinformationsteknik modeller lösa komplexa negativa entropies, vilket ger dubbel-effektiva LDC&gt; 87,5 besparingar utrymme. / +46 455 38 50 00