Lempel-Ziv Data Compression on Parallel and Distributed Systems

We present a survey of results concerning Lempel-Ziv data compression on parallel and distributed systems, starting from the theoretical approach to parallel time complexity to conclude with the practical goal of designing distributed algorithms with low communication cost. An extension by Storer to image compression is also discussed

Practical Aspects of Implementing a Suffix Array-based Lempel-Ziv Data Compressor

Lempel-Ziv factorization of a string is a fundamental tool that is used by myriad data compressors. Despite its optimality regarding the number of produced factors, it is rarely used without modification, for reasons of its computational cost. In recent years, Lempel-Ziv factorization has been a busy research subject, and we have witnessed the state-of-the-art being completely changed. In this thesis, I explore the properties of the latest suffix array-based Lempel-Ziv factorization algorithms, while I experiment with turning them into an efficient general-purpose data compressor. The setting of this thesis is purely exploratory, guided by reliable and repeatable benchmarking. I explore all aspects of the suffix array-based Lempel-Ziv data compressor. I describe how the chosen factorization method affects the development of encoding and other components of a functional data compressor. I show how the chosen factorization technique, together with capabilities of modern hardware, allows determining the length of the longest common prefix of two strings over 80% faster compared to the baseline approach. I also present a novel approach to optimizing the encoding cost of the Lempel-Ziv factorization of a string, i.e., bit-optimality, using a dynamic programming approach to the Single-Source Shortest Path problem. I observed that, in its current state, the process of suffix array construction is a major computational bottleneck in suffix array-based Lempel-Ziv factorization. Additionally, using a suffix array to produce a Lempel-Ziv factorization leads to optimality regarding the number of factors, which does not necessarily correspond to bit-optimality. Finally, a comparison with common third-party data compressors revealed that relying exclusively on Lempel-Ziv factorization prevents reaching the highest compression efficiency. For these reasons, I conclude that current suffix array-based Lempel-Ziv factorization is unsuitable for general-purpose data compression

Study of Efficient Parsing in Lz Adaptive Dictionary Compression

The purpose of this research was to study three commonly known efficient parsing problems of LZ adaptive dictionary compression schemes. They are the efficiency of finding the longest match between the look-ahead buffer and the text window, coding redundancy and parsing strategies. We introduced an AVL tree data structure to the original LZSS variant and the Knuth-Moore-Pratt string matching algorithm to the LZ77 variant, and compared their performances. We also tried to modify the one-bit flag fixed length coding method of LZSS to a two-bit flag variable length coding method and investigated the effort. Finally, we discussed a newly presented Non-Greedy parsing strategy. Acknowledgments and thanks go to my thesis advisor Professor John Chandler for his great help, guidance, and patience during the entire work. My thanks also go to Dr. Huizhu Lu and Dr. K.M. George for their helpful suggestions to my research. I also wish to give thanks to Dr. Kathleen Kaplan who spent a lot of time to answer my questions on string matching and generously lent me all the related papers and books. I am very thankful for the love and encouragement from my families, my parents, my mother-in-law, my younger brother, especially my wife Kaiping Deng. I cannot imagine, without all the help from these kind people, I could have finished my research on time

Improving the tokenisation of identifier names

Identifier names are the main vehicle for semantic information during program comprehension. For tool-supported program comprehension tasks, including concept location and requirements traceability, identifier names need to be tokenised into their semantic constituents. In this paper we present an approach to the automated tokenisation of identifier names that improves on existing techniques in two ways. First, it improves the tokenisation accuracy for single-case identifier names and for identifier names containing digits, which existing techniques largely ignore. Second, performance gains over existing techniques are achieved using smaller oracles, making the approach easier to deploy. Accuracy was evaluated by comparing our algorithm to manual tokenizations of 28,000 identifier names drawn from 60 well-known open source Java projects totalling 16.5 MSLOC. Moreover, the projects were used to perform a study of identifier tokenisation features (single case, camel case, use of digits, etc.) per object-oriented construct (class names, method names, local variable names, etc.), thus providing an insight into naming conventions in industrial-scale object-oriented code. Our tokenisation tool and datasets are publicly available

Shortest common superstring approximaation nopea toteutus sekä soveltaminen relative lempel-ziv pakkaukseen

The objective of the shortest common superstring problem is to find a string of minimum length that contains all keywords in the given input as substrings. Shortest common superstrings have many applications in the fields of data compression and bioinformatics. For example, a common superstring can be seen as a compressed form of the keywords it is generated from. Since the shortest common superstring problem is NP-hard, we focus on the approximation algorithms that implement a so-called greed heuristic. It turns out that the actual shortest common superstring is not always needed. Instead, it is often enough to find an approximate solution of sufficient quality. We provide an implementation of the Ukkonen's linear time algorithm for the greedy heuristic. The practical performance of this implementation is measured by comparing it to another implementation of the same heuristic. We also hypothesize that shortest common superstrings can be potentially used to improve the compression ratio of the Relative Lempel-Ziv data compression algorithm. This hypothesis is examined and shown to be valid

Lossless Differential Compression for Synchronizing Arbitrary Single-Dimensional Strings

Differential compression allows expressing a modified document as differences relative to another version of the document. A compressed string requires space relative to amount of changes, irrespective of original document sizes. The purpose of this study was to answer what algorithms are suitable for universal lossless differential compression for synchronizing two arbitrary documents either locally or remotely. Two main problems in differential compression are finding the differences (differencing), and compactly communicating the differences (encoding). We discussed local differencing algorithms based on subsequence searching, hashtable lookups, suffix searching, and projection. We also discussed probabilistic remote algorithms based on both recursive comparison and characteristic polynomial interpolation of hashes computed from variable-length content-defined substrings. We described various heuristics for approximating optimal algorithms as arbitrary long strings and memory limitations force discarding information. Discussion also included compact delta encoding and in-place reconstruction. We presented results from empirical testing using discussed algorithms. The conclusions were that multiple algorithms need to be integrated into a hybrid implementation, which heuristically chooses algorithms based on evaluation of the input data. Algorithms based on hashtable lookups are faster on average and require less memory, but algorithms based on suffix searching find least differences. Interpolating characteristic polynomials was found to be too slow for general use. With remote hash comparison, content-defined chunks and recursive comparison can reduce protocol overhead. A differential compressor should be merged with a state-of-art non-differential compressor to enable more compact delta encoding. Input should be processed multiple times to allow constant a space bound without significant reduction in compression efficiency. Compression efficiently of current popular synchronizers could be improved, as our empiral testing showed that a non-differential compressor produced smaller files without having access to one of the two strings

Deep Active Learning for Named Entity Recognition

Deep learning has yielded state-of-the-art performance on many natural language processing tasks including named entity recognition (NER). However, this typically requires large amounts of labeled data. In this work, we demonstrate that the amount of labeled training data can be drastically reduced when deep learning is combined with active learning. While active learning is sample-efficient, it can be computationally expensive since it requires iterative retraining. To speed this up, we introduce a lightweight architecture for NER, viz., the CNN-CNN-LSTM model consisting of convolutional character and word encoders and a long short term memory (LSTM) tag decoder. The model achieves nearly state-of-the-art performance on standard datasets for the task while being computationally much more efficient than best performing models. We carry out incremental active learning, during the training process, and are able to nearly match state-of-the-art performance with just 25\% of the original training data