Index compression for information retrielval systems

[Abstract] Given the increasing amount of information that is available today, there is a clear need for Information Retrieval (IR) systems that can process this information in an efficient and effective way. Efficient processing means minimising the amount of time and space required to process data, whereas effective processing means identifying accurately which information is relevant to the user and which is not. Traditionally, efficiency and effectiveness are at opposite ends (what is beneficial to efficiency is usually harmful to effectiveness, and vice versa), so the challenge of IR systems is to find a compromise between efficient and effective data processing. This thesis investigates the efficiency of IR systems. It suggests several novel strategies that can render IR systems more efficient by reducing the index size of IR systems, referred to as index compression. The index is the data structure that stores the information handled in the retrieval process. Two different approaches are proposed for index compression, namely document reordering and static index pruning. Both of these approaches exploit document collection characteristics in order to reduce the size of indexes, either by reassigning the document identifiers in the collection in the index, or by selectively discarding information that is less relevant to the retrieval process by pruning the index. The index compression strategies proposed in this thesis can be grouped into two categories: (i) Strategies which extend state of the art in the field of efficiency methods in novel ways. (ii) Strategies which are derived from properties pertaining to the effectiveness of IR systems; these are novel strategies, because they are derived from effectiveness as opposed to efficiency principles, and also because they show that efficiency and effectiveness can be successfully combined for retrieval. The main contributions of this work are in indicating principled extensions of state of the art in index compression, and also in suggesting novel theoretically-driven index compression techniques which are derived from principles of IR effectiveness. All these techniques are evaluated extensively, in thorough experiments involving established datasets and baselines, which allow for a straight-forward comparison with state of the art. Moreover, the optimality of the proposed approaches is addressed from a theoretical perspective.[Resumen] Dada la creciente cantidad de información disponible hoy en día, existe una clara necesidad de sistemas de Recuperación de Información (RI) que sean capaces de procesar esa información de una manera efectiva y eficiente. En este contexto, eficiente significa cantidad de tiempo y espacio requeridos para procesar datos, mientras que efectivo significa identificar de una manera precisa qué información es relevante para el usuario y cual no lo es. Tradicionalmente, eficiencia y efectividad se encuentran en polos opuestos - lo que es beneficioso para la eficiencia, normalmente perjudica la efectividad y viceversa - así que un reto para los sistemas de RI es encontrar un compromiso adecuado entre el procesamiento efectivo y eficiente de los datos. Esta tesis investiga el problema de la eficiencia de los sistemas de RI. Sugiere diferentes estrategias novedosas que pueden permitir la reducción de los índices de los sistemas de RI, enmarcadas dentro da las técnicas conocidas como compresión de índices. El índice es la estructura de datos que almacena la información utilizada en el proceso de recuperación. Se presentan dos aproximaciones diferentes para la compresión de los índices, referidas como reordenación de documentos y pruneado estático del índice. Ambas aproximaciones explotan características de colecciones de documentos para reducir el tamaño final de los índices, mediante la reasignación de los identificadores de los documentos de la colección o bien descartando selectivamente la información que es "menos relevante" para el proceso de recuperación. Las estrategias de compresión propuestas en este tesis se pueden agrupar en dos categorías: (i) estrategias que extienden el estado del arte en la eficiencia de una manera novedosa y (ii) estrategias derivadas de propiedades relacionadas con los principios de la efectividad en los sistemas de RI; estas estrategias son novedosas porque son derivadas desde principios de la efectividad como contraposición a los de la eficiencia, e porque revelan como la eficiencia y la efectividad pueden ser combinadas de una manera efectiva para la recuperación de información. Las contribuciones de esta tesis abarcan la elaboración de técnicas del estado del arte en compresión de índices y también en la derivación de técnicas de compresión basadas en fundamentos teóricos derivados de los principios de la efectividad de los sistemas de RI. Todas estas técnicas han sido evaluadas extensamente con numerosos experimentos que involucran conjuntos de datos y técnicas de referencia bien establecidas en el campo, las cuales permiten una comparación directa con el estado del arte. Finalmente, la optimalidad de las aproximaciones presentadas es tratada desde una perspectiva teórica

Efficient homology search for genomic sequence databases

Genomic search tools can provide valuable insights into the chemical structure, evolutionary origin and biochemical function of genetic material. A homology search algorithm compares a protein or nucleotide query sequence to each entry in a large sequence database and reports alignments with highly similar sequences. The exponential growth of public data banks such as GenBank has necessitated the development of fast, heuristic approaches to homology search. The versatile and popular blast algorithm, developed by researchers at the US National Center for Biotechnology Information (NCBI), uses a four-stage heuristic approach to efficiently search large collections for analogous sequences while retaining a high degree of accuracy. Despite an abundance of alternative approaches to homology search, blast remains the only method to offer fast, sensitive search of large genomic collections on modern desktop hardware. As a result, the tool has found widespread use with millions of queries posed each day. A significant investment of computing resources is required to process this large volume of genomic searches and a cluster of over 200 workstations is employed by the NCBI to handle queries posed through the organisation's website. As the growth of sequence databases continues to outpace improvements in modern hardware, blast searches are becoming slower each year and novel, faster methods for sequence comparison are required. In this thesis we propose new techniques for fast yet accurate homology search that result in significantly faster blast searches. First, we describe improvements to the final, gapped alignment stages where the query and sequences from the collection are aligned to provide a fine-grain measure of similarity. We describe three new methods for aligning sequences that roughly halve the time required to perform this computationally expensive stage. Next, we investigate improvements to the first stage of search, where short regions of similarity between a pair of sequences are identified. We propose a novel deterministic finite automaton data structure that is significantly smaller than the codeword lookup table employed by ncbi-blast, resulting in improved cache performance and faster search times. We also discuss fast methods for nucleotide sequence comparison. We describe novel approaches for processing sequences that are compressed using the byte packed format already utilised by blast, where four nucleotide bases from a strand of DNA are stored in a single byte. Rather than decompress sequences to perform pairwise comparisons, our innovations permit sequences to be processed in their compressed form, four bases at a time. Our techniques roughly halve average query evaluation times for nucleotide searches with no effect on the sensitivity of blast. Finally, we present a new scheme for managing the high degree of redundancy that is prevalent in genomic collections. Near-duplicate entries in sequence data banks are highly detrimental to retrieval performance, however existing methods for managing redundancy are both slow, requiring almost ten hours to process the GenBank database, and crude, because they simply purge highly-similar sequences to reduce the level of internal redundancy. We describe a new approach for identifying near-duplicate entries that is roughly six times faster than the most successful existing approaches, and a novel approach to managing redundancy that reduces collection size and search times but still provides accurate and comprehensive search results. Our improvements to blast have been integrated into our own version of the tool. We find that our innovations more than halve average search times for nucleotide and protein searches, and have no signifcant effect on search accuracy. Given the enormous popularity of blast, this represents a very significant advance in computational methods to aid life science research

Translation Alignment and Extraction Within a Lexica-Centered Iterative Workflow

This thesis addresses two closely related problems. The first, translation alignment, consists of identifying bilingual document pairs that are translations of each other within multilingual document collections (document alignment); identifying sentences, titles, etc, that are translations of each other within bilingual document pairs (sentence alignment); and identifying corresponding word and phrase translations within bilingual sentence pairs (phrase alignment). The second is extraction of bilingual pairs of equivalent word and multi-word expressions, which we call translation equivalents (TEs), from sentence- and phrase-aligned parallel corpora. While these same problems have been investigated by other authors, their focus has been on fully unsupervised methods based mostly or exclusively on parallel corpora. Bilingual lexica, which are basically lists of TEs, have not been considered or given enough importance as resources in the treatment of these problems. Human validation of TEs, which consists of manually classifying TEs as correct or incorrect translations, has also not been considered in the context of alignment and extraction. Validation strengthens the importance of infrequent TEs (most of the entries of a validated lexicon) that otherwise would be statistically unimportant. The main goal of this thesis is to revisit the alignment and extraction problems in the context of a lexica-centered iterative workflow that includes human validation. Therefore, the methods proposed in this thesis were designed to take advantage of knowledge accumulated in human-validated bilingual lexica and translation tables obtained by unsupervised methods. Phrase-level alignment is a stepping stone for several applications, including the extraction of new TEs, the creation of statistical machine translation systems, and the creation of bilingual concordances. Therefore, for phrase-level alignment, the higher accuracy of human-validated bilingual lexica is crucial for achieving higher quality results in these downstream applications. There are two main conceptual contributions. The first is the coverage maximization approach to alignment, which makes direct use of the information contained in a lexicon, or in translation tables when this is small or does not exist. The second is the introduction of translation patterns which combine novel and old ideas and enables precise and productive extraction of TEs. As material contributions, the alignment and extraction methods proposed in this thesis have produced source materials for three lines of research, in the context of three PhD theses (two of them already defended), all sharing with me the supervision of my advisor. The topics of these lines of research are statistical machine translation, algorithms and data structures for indexing and querying phrase-aligned parallel corpora, and bilingual lexica classification and generation. Four publications have resulted directly from the work presented in this thesis and twelve from the collaborative lines of research

Images on the Move: Materiality - Networks - Formats

In contemporary society, digital images have become increasingly mobile. They are networked, shared on social media, and circulated across small and portable screens. Accordingly, the discourses of spreadability and circulation have come to supersede the focus on production, indexicality, and manipulability, which had dominated early conceptions of digital photography and film. However, the mobility of images is neither technologically nor conceptually limited to the realm of the digital. The edited volume re-examines the historical, aesthetical, and theoretical relevance of image mobility. The contributors provide a materialist account of images on the move - ranging from wired photography to postcards to streaming media