http://dspace.infodocu.lbd.org.es/xmlui/handle/123456789/1412 Revistas relacionado con estructura de datos 2026-05-22T19:04:52Z http://dspace.infodocu.lbd.org.es/xmlui/handle/123456789/1428 Lossless compression of industrial time series with direct access Gómez-Brandón, Adrián; R. Paramá, José; Villalobos, Kevin; Illarramendi, Arantza; R. Brisaboa, Nieves 2021-01-01T00:00:00Z http://dspace.infodocu.lbd.org.es/xmlui/handle/123456789/1420 Energy consumption in compact integer vectors: A study case FUENTES-SEPÚLVEDA, JOSÉ; SUSANA, LADRA In the field of algorithms and data structures analysis and design, most of the researchers focus only on the space/time trade-off, and little attention has been paid to energy consumption. Moreover, most of the efforts in the field of Green Computing have been devoted to hardware-related issues, being green software in its infancy. Optimizing the usage of computing resources, minimizing power consumption or increasing battery life are some of the goals of this field of research. As an attempt to address the most recent sustainability challenges, we must incorporate the energy consumption as a first-class constraint when designing new compact data structures. Thus, as a preliminary work to reach that goal, we first need to understand the factors that impact on the energy consumption and their relation with compression. In this work, we study the energy consumption required by several integer vector representations. We execute typical operations over datasets of different nature. We can see that, as commonly believed, energy consumption is highly related to the time required by the process, but not always. We analyze other parameters, such as number of instructions, number of CPU cycles, memory loads, among others. 2019-01-01T00:00:00Z http://dspace.infodocu.lbd.org.es/xmlui/handle/123456789/1415 Universal Indexes for Highly Repetitive Document Collections Fariña, Antonio; Claude, Francisco; A. Martínez-Prieto, Miguel; Navarro, Gonzalo Indexing highly repetitive collections has become a relevant problem with the emergence of large repositoriesof versioned documents, among other applications. These collections may reach huge sizes, but are formedmostly of documents that are near-copies of others. Traditional techniques for indexing these collections failto properly exploit their regularities in order to reduce space.We introduce new techniques for compressing inverted indexes that exploit this near-copy regularity.They are based on run-length, Lempel-Ziv, or grammar compression of the differential inverted lists, insteadof the usual practice of gap-encoding them. We show that, in this highly repetitive setting, our compressionmethods significantly reduce the space obtained with classical techniques, at the price of moderate slowdowns.Moreover, our best methods are universal, that is, they do not need to know the versioning structure of thecollection, nor that a clear versioning structure even exists.We also introduce compressed self-indexes in the comparison. These are designed for general strings(not only natural language texts) and represent the text collection plus the index structure (not an invertedindex) in integrated form. We show that these techniques can compress much further, using a small fractionof the space required by our new inverted indexes. Yet, they are orders of magnitude slower. 2016-01-01T00:00:00Z