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dc.contributor.advisor Vitter, Jeffrey S en_US
dc.contributor.author Gupta, Ankur en_US
dc.date.accessioned 2008-01-02T16:33:28Z
dc.date.available 2008-01-02T16:33:28Z
dc.date.issued 2007-12-14 en_US
dc.identifier.uri http://hdl.handle.net/10161/434
dc.description Dissertation en_US
dc.description.abstract The world is drowning in data. The recent explosion of web publishing, XML data, bioinformation, scientific data, image data, geographical map data, and even email communications has put a strain on our ability to manage the information contained there. In general, the influx of massive data sets for all kinds of data present a number of difficulties with storage, organization of information, and data accessibility. A primary computing challenge in these cases is how to compress the data but still allow it to be queried quickly.In real-life situations, many instances of data are highly compressible, presenting a major opportunity for space savings. In mobile applications, such savings are critical, since space and the power to access information are at a premium. In a streaming environment, where new data are being generated constantly, compression can aid in prediction as well. In the case of bioinformatics, understanding succinct representations of DNA sequences could lead to a more fundamental understanding of the nature of our own "data stream," perhaps even giving hints on secondary and tertiary structure, gene evolution, and other important topics.In this thesis, we focus our attention on the important problem of <i>compressed text indexing<\i>, where the goal is to compress a text document and allow arbitrary searching for patterns in the best possible time <i>without first decompressing the text<\i>. We develop a number of compressed data structures that either solve this problem directly, or are used as smaller components of an overall text indexing solution. Each component has a number of applications beyond text indexing as well. For each structure, we provide a theoretical study of its space usage and query performance on a suite of operations crucial to access the stored data. In each case, we relate its space usage to the <i>compressed size of the original data</i> and show that the supported operations function in near-optimal or optimal time. We also present a number of experimental results that validate our theoretical findings, showing that our methodology is competitive with the state-of-the-art. en_US
dc.format.extent 1341010 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.subject Computer Science en_US
dc.subject compression en_US
dc.subject text indexing en_US
dc.subject dictionaries en_US
dc.subject dynamic en_US
dc.subject optimal en_US
dc.title Succinct Data Structures en_US
dc.type Dissertation en_US
dc.department Computer Science en_US

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