Transform Based And Search Aware Text Compression Schemes And Compressed Domain Text Retrieval

In recent times, we have witnessed an unprecedented growth of textual information via the Internet, digital libraries and archival text in many applications. While a good fraction of this information is of transient interest, useful information of archival value will continue to accumulate. We need ways to manage, organize and transport this data from one point to the other on data communications links with limited bandwidth. We must also have means to speedily find the information we need from this huge mass of data. Sometimes, a single site may also contain large collections of data such as a library database, thereby requiring an efficient search mechanism even to search within the local data. To facilitate the information retrieval, an emerging ad hoc standard for uncompressed text is XML which preprocesses the text by putting additional user defined metadata such as DTD or hyperlinks to enable searching with better efficiency and effectiveness. This increases the file size considerably, underscoring the importance of applying text compression. On account of efficiency (in terms of both space and time), there is a need to keep the data in compressed form for as much as possible. Text compression is concerned with techniques for representing the digital text data in alternate representations that takes less space. Not only does it help conserve the storage space for archival and online data, it also helps system performance by requiring less number of secondary storage (disk or CD Rom) accesses and improves the network transmission bandwidth utilization by reducing the transmission time. Unlike static images or video, there is no international standard for text compression, although compressed formats like .zip, .gz, .Z files are increasingly being used. In general, data compression methods are classified as lossless or lossy. Lossless compression allows the original data to be recovered exactly. Although used primarily for text data, lossless compression algorithms are useful in special classes of images such as medical imaging, finger print data, astronomical images and data bases containing mostly vital numerical data, tables and text information. Many lossy algorithms use lossless methods at the final stage of the encoding stage underscoring the importance of lossless methods for both lossy and lossless compression applications. In order to be able to effectively utilize the full potential of compression techniques for the future retrieval systems, we need efficient information retrieval in the compressed domain. This means that techniques must be developed to search the compressed text without decompression or only with partial decompression independent of whether the search is done on the text or on some inversion table corresponding to a set of key words for the text. In this dissertation, we make the following contributions: (1) Star family compression algorithms: We have proposed an approach to develop a reversible transformation that can be applied to a source text that improves existing algorithm\u27s ability to compress. We use a static dictionary to convert the English words into predefined symbol sequences. These transformed sequences create additional context information that is superior to the original text. Thus we achieve some compression at the preprocessing stage. We have a series of transforms which improve the performance. Star transform requires a static dictionary for a certain size. To avoid the considerable complexity of conversion, we employ the ternary tree data structure that efficiently converts the words in the text to the words in the star dictionary in linear time. (2) Exact and approximate pattern matching in Burrows-Wheeler transformed (BWT) files: We proposed a method to extract the useful context information in linear time from the BWT transformed text. The auxiliary arrays obtained from BWT inverse transform brings logarithm search time. Meanwhile, approximate pattern matching can be performed based on the results of exact pattern matching to extract the possible candidate for the approximate pattern matching. Then fast verifying algorithm can be applied to those candidates which could be just small parts of the original text. We present algorithms for both k-mismatch and k-approximate pattern matching in BWT compressed text. A typical compression system based on BWT has Move-to-Front and Huffman coding stages after the transformation. We propose a novel approach to replace the Move-to-Front stage in order to extend compressed domain search capability all the way to the entropy coding stage. A modification to the Move-to-Front makes it possible to randomly access any part of the compressed text without referring to the part before the access point. (3) Modified LZW algorithm that allows random access and partial decoding for the compressed text retrieval: Although many compression algorithms provide good compression ratio and/or time complexity, LZW is the first one studied for the compressed pattern matching because of its simplicity and efficiency. Modifications on LZW algorithm provide the extra advantage for fast random access and partial decoding ability that is especially useful for text retrieval systems. Based on this algorithm, we can provide a dynamic hierarchical semantic structure for the text, so that the text search can be performed on the expected level of granularity. For example, user can choose to retrieve a single line, a paragraph, or a file, etc. that contains the keywords. More importantly, we will show that parallel encoding and decoding algorithm is trivial with the modified LZW. Both encoding and decoding can be performed with multiple processors easily and encoding and decoding process are independent with respect to the number of processors

Indices and Applications in High-Throughput Sequencing

Recent advances in sequencing technology allow to produce billions of base pairs per day in the form of reads of length 100 bp an longer and current developments promise the personal $1,000 genome in a couple of years. The analysis of these unprecedented amounts of data demands for efficient data structures and algorithms. One such data structures is the substring index, that represents all substrings or substrings up to a certain length contained in a given text. In this thesis we propose 3 substring indices, which we extend to be applicable to millions of sequences. We devise internal and external memory construction algorithms and a uniform framework for accessing the generalized suffix tree. Additionally we propose different index-based applications, e.g. exact and approximate pattern matching and different repeat search algorithms. Second, we present the read mapping tool RazerS, which aligns millions of single or paired-end reads of arbitrary lengths to their potential genomic origin using either Hamming or edit distance. Our tool can work either lossless or with a user-defined loss rate at higher speeds. Given the loss rate, we present a novel approach that guarantees not to lose more reads than specified. This enables the user to adapt to the problem at hand and provides a seamless tradeoff between sensitivity and running time. We compare RazerS with other state-of-the-art read mappers and show that it has the highest sensitivity and a comparable performance on various real-world datasets. At last, we propose a general approach for frequency based string mining, which has many applications, e.g. in contrast data mining. Our contribution is a novel and lightweight algorithm that is faster and uses less memory than the best available algorithms. We show its applicability for mining multiple databases with a variety of frequency constraints. As such, we use the notion of entropy from information theory to generalize the emerging substring mining problem to multiple databases. To demonstrate the improvement of our algorithm we compared to recent approaches on real-world experiments of various string domains, e.g. natural language, DNA, or protein sequences

Improved Periodicity Mining in Time Series Databases

Time series data represents information about real world phenomena and periodicity mining explores the interesting periodic behavior that is inherent in the data. Periodicity mining has numerous applications such as in weather forecasting, stock market prediction and analysis, pattern recognition, etc. Recently, the suffix tree, a powerful data structure that efficiently solves many strings related problems has been used to gather information about repeated substrings in the text and then perform periodicity mining. However, periodicity mining deals with large amounts of data which makes it difficult to perform mining in main memory due to the space constraints of the suffix tree. Thus, we first propose the use of the Compressed Suffix Tree (CST) for space efficient periodicity mining in very large datasets. Given the time-space trade-off that comes with any practical usage of the CST, we provide a comprehensive empirical analysis on the practical usage of CSTs and traditional suffix trees for periodicity mining.;Noise is an inherent part of practical time series data, and it is important to mine periods in spite of the noise. This leads to the problem of approximate periodicity mining. Existing algorithms have dealt with the noise introduced between the occurrences of the periodic pattern, but not the noise introduced in the structure of the pattern itself. We present a taxonomy for approximate periodicity and then propose an algorithm that performs periodicity mining in the presence of noise introduced simultaneously in both the structure of the pattern and between the periodic occurrences of the pattern

Indexing multilingual information on the web

The web connects people speaking more than twenty languages in more than one hundred countries. Search engines, which provide users starting points to navigate and retrieve resources on the web, should thus be able to handle documents in many languages. Moreover, with information being added and changed every minute on the web, search engines should discover new index terms time-efficiently. This paper introduces an abstraction of viewing multilingual documents and a statistical analysis method so that search engines can index multilingual documents in a generic, efficient, and effective manner with minimal requirements of language-specific information.published_or_final_versio

Stream Processing using Grammars and Regular Expressions

In this dissertation we study regular expression based parsing and the use of grammatical specifications for the synthesis of fast, streaming string-processing programs. In the first part we develop two linear-time algorithms for regular expression based parsing with Perl-style greedy disambiguation. The first algorithm operates in two passes in a semi-streaming fashion, using a constant amount of working memory and an auxiliary tape storage which is written in the first pass and consumed by the second. The second algorithm is a single-pass and optimally streaming algorithm which outputs as much of the parse tree as is semantically possible based on the input prefix read so far, and resorts to buffering as many symbols as is required to resolve the next choice. Optimality is obtained by performing a PSPACE-complete pre-analysis on the regular expression. In the second part we present Kleenex, a language for expressing high-performance streaming string processing programs as regular grammars with embedded semantic actions, and its compilation to streaming string transducers with worst-case linear-time performance. Its underlying theory is based on transducer decomposition into oracle and action machines, and a finite-state specialization of the streaming parsing algorithm presented in the first part. In the second part we also develop a new linear-time streaming parsing algorithm for parsing expression grammars (PEG) which generalizes the regular grammars of Kleenex. The algorithm is based on a bottom-up tabulation algorithm reformulated using least fixed points and evaluated using an instance of the chaotic iteration scheme by Cousot and Cousot

Management of biological sequences using suffix trees

The amount of available biological sequences, represented as strings over the DNA and protein alphabets, grows at phenomenal rate. Supporting various search tasks over such data efficiently requires development of sophisticated indexing techniques. Recently, suffix tree (ST) and suffix array (SA) received considerable attention as suitable data structures in this context. However, existing solutions often focus on either efficiency or scalability, but not both. Further, some of the solutions require advanced computational resources or are tailored towards a specific application. We investigate, both theoretically and experimentally, ways to improve efficiency and scalability in management of biological sequence data. Our goal is to develop an indexing technique that is reasonable in construction time and space utilization, and supports efficiently versatile search applications in biological sequences of various sizes, running on a typical desktop computer. The contributions of this research include development of a ST based indexing technique, called HST, together with exact and approximate search algorithms that use the index. The results of our experiments indicate that the index construction cost is comparable to other ST based techniques, such as TDD and Trellis, in terms of construction time and main memory requirement. While HST exhibits slower construction time than Vmatch, the best known SA based solution, with the same amount of main memory HST can handle sequences that are an order of magnitude longer. In terms of the index size, HST is comparable to TDD and Vmatch, which is half of the Trellis index size. We also develop efficient and scalable search applications using HST, including exact match, k-mismatch, and structured motif search. Our experiments using real-life sequences indicated that for short sequences (e.g., human chromosomes), our exact match search is comparable to Vmatch, about 3 times faster than TDD, and more than 10 times faster than Trellis. Further, HST can be used to search directly in longer DNA sequences, as opposed to partitioning such a sequence and search in the parts - the only option to follow with Vmatch. We found that a direct exact match search using HST is twice faster when searching in the entire human genome, compared to using Vmatch on parts. Compared to Trellis, which can handle direct search in human genome, HST was more than 20 times faster. To further compare performance of HST and Vmatch, we considered k-mismatch search. Our results indicated significant improvement of the HST based solution over Vmatch, ranging from 2 to 9 times faster k-mismatch search on average, for short and long sequences, respectively. For structured motif search, HST was about 6 times faster than SMOTIF1, the best known structured motif search tool