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Academic literature on the topic 'Indexed data compression'
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Journal articles on the topic "Indexed data compression"
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Kaneiwa, Ken, and Koji Fujiwara. "The Compression of Indexed Data and Fast Search for Large RDF Graphs." Transactions of the Japanese Society for Artificial Intelligence 33, no. 2 (2018): E—H43_1–10. http://dx.doi.org/10.1527/tjsai.e-h43.
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M.K., Bouza. "Analysis and modification of graphic data compression algorithms." Artificial Intelligence 25, no. 4 (2020): 32–40. http://dx.doi.org/10.15407/jai2020.04.032.
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The article examines the algorithms for JPEG and JPEG-2000 compression of various graphic images. The main steps of the operation of both algorithms are given, their advantages and disadvantages are noted. The main differences between JPEG and JPEG-2000 are analyzed. It is noted that the JPEG-2000 algorithm allows re-moving visually unpleasant effects. This makes it possible to highlight important areas of the image and improve the quality of their compression. The features of each step of the algorithms are considered and the difficulties of their implementation are compared. The effectiveness of each algorithm is demonstrated by the example of a full-color image of the BSU emblem. The obtained compression ratios were obtained and shown in the corresponding tables using both algorithms. Compression ratios are obtained for a wide range of quality values from 1 to ten. We studied various types of images: black and white, business graphics, indexed and full color. A modified LZW-Lempel-Ziv-Welch algorithm is presented, which is applicable to compress a variety of information from text to images. The modification is based on limiting the graphic file to 256 colors. This made it possible to index the color with one byte instead of three. The efficiency of this modification grows with increasing image sizes. The modified LZW-algorithm can be adapted to any image from single-color to full-color. The prepared tests were indexed to the required number of colors in the images using the FastStone Image Viewer program. For each image, seven copies were obtained, containing 4, 8, 16, 32, 64, 128 and 256 colors, respectively. Testing results showed that the modified version of the LZW algorithm allows for an average of twice the compression ratio. However, in a class of full-color images, both algorithms showed the same results. The developed modification of the LZW algorithm can be successfully applied in the field of site design, especially in the case of so-called flat design. The comparative characteristics of the basic and modified methods are presented.
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Senthilkumar, Radha, Gomathi Nandagopal, and Daphne Ronald. "QRFXFreeze: Queryable Compressor for RFX." Scientific World Journal 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/864750.
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The verbose nature of XML has been mulled over again and again and many compression techniques for XML data have been excogitated over the years. Some of the techniques incorporate support for querying the XML database in its compressed format while others have to be decompressed before they can be queried. XML compression in which querying is directly supported instantaneously with no compromise over time is forced to compromise over space. In this paper, we propose the compressor, QRFXFreeze, which not only reduces the space of storage but also supports efficient querying. The compressor does this without decompressing the compressed XML file. The compressor supports all kinds of XML documents along with insert, update, and delete operations. The forte of QRFXFreeze is that the textual data are semantically compressed and are indexed to reduce the querying time. Experimental results show that the proposed compressor performs much better than other well-known compressors.
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Hernández-Illera, Antonio, Miguel A. Martínez-Prieto, Javier D. Fernández, and Antonio Fariña. "iHDT++: improving HDT for SPARQL triple pattern resolution." Journal of Intelligent & Fuzzy Systems 39, no. 2 (2020): 2249–61. http://dx.doi.org/10.3233/jifs-179888.
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RDF self-indexes compress the RDF collection and provide efficient access to the data without a previous decompression (via the so-called SPARQL triple patterns). HDT is one of the reference solutions in this scenario, with several applications to lower the barrier of both publication and consumption of Big Semantic Data. However, the simple design of HDT takes a compromise position between compression effectiveness and retrieval speed. In particular, it supports scan and subject-based queries, but it requires additional indexes to resolve predicate and object-based SPARQL triple patterns. A recent variant, HDT++, improves HDT compression ratios, but it does not retain the original HDT retrieval capabilities. In this article, we extend HDT++ with additional indexes to support full SPARQL triple pattern resolution with a lower memory footprint than the original indexed HDT (called HDT-FoQ). Our evaluation shows that the resultant structure, iHDT++ , requires 70 - 85% of the original HDT-FoQ space (and up to 48 - 72% for an HDT Community variant). In addition, iHDT++ shows significant performance improvements (up to one level of magnitude) for most triple pattern queries, being competitive with state-of-the-art RDF self-indexes.
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Moneta, G. L., A. D. Nicoloff, and J. M. Porter. "Compression Treatment of Chronic Venous Ulceration: A Review." Phlebology: The Journal of Venous Disease 15, no. 3-4 (2000): 162–68. http://dx.doi.org/10.1177/026835550001500316.
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Objective: To review the recent medical literature with regard to the use of compressive therapy in healing and preventing the recurrence of venous ulceration. Methods: Searches of Medline and Embase medical literature databases. Appropriate non-indexed journals and textbooks were also reviewed. Synthesis: Elastic compression therapy is regarded as the ‘gold standard’ treatment for venous ulceration. The benefits of elastic compression therapy in the treatment of venous ulceration may be mediated through favourable alterations in venous haemodynamics, micro-circulatory haemodynamics and/or improvement in subcutaneous Starling forces. Available data indicate compressive therapy is highly effective in healing of the large majority of venous ulcers. Elastic compression stockings, Unna boots, as well as multi-layer elastic wraps, have all been noted to achieve excellent healing rates for venous ulcers. In compliant patients it appears that approximately 75% of venous ulcers can be healed by 6 months, and up to 90% by 1 year. Non-healing of venous ulcers is associated with lack of patient compliance with treatment, large and long-standing venous ulceration and the coexistence of arterial insufficiency. Recurrence of venous ulceration is, however, a significant problem after healing with compressive therapy, even in compliant patients; approximately 20-30% of venous ulcers will recur by 2 years. Conclusions: Compressive therapy is capable of achieving high rates of healing of venous ulceration in compliant patients. Various forms of compression, including elastic, rigid and multi-layer dressings, are available depending on physician preference, the clinical situation and the needs of the individual patient. Compressive therapy, while effective, remains far from ideal. The future goals are to achieve faster healing of venous ulceration, less painful healing and freedom from ulcer recurrence.
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Selivanova, Irina V. "Limitations of Applying the Data Compression Method to the Classification of Abstracts of Publications Indexed in Scopus." Vestnik NSU. Series: Information Technologies 18, no. 3 (2020): 57–68. http://dx.doi.org/10.25205/1818-7900-2020-18-3-57-68.
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The paper describes the limitations of applying the method of classification of scientific texts based on data compression to all categories indicated in the ASJC classification used in the Scopus bibliographic database. It is shown that the automatic generation of learning samples for each category is a rather time-consuming process, and in some cases is impossible due to the restriction on data upload installed in Scopus and the lack of category names in the Scopus Search API. Another reason is that in many subject areas there are completely no journals and, accordingly, publications that have only one category. Application of the method to all 26 subject areas is impossible due to their vastness, as well as the initial classification of Scopus. Often in different subject areas there are terminologically close categories, which makes it difficult to classify a publication as a true area. These findings also indicate that the classification currently used in Scopus and SciVal may not be completely reliable. For example, according to SciVal in terms of the number of publications, the category “Theoretical computer science” is in second place among all publications in the subject area “Mathematics”. The study showed that this category is one of the smallest categories, both in terms of the presence of journals and publications with only this category. Thus, many studies based on the use of publications in ASJC may have some inaccuracies.
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Shibuya, Yoshihiro, and Matteo Comin. "Indexing k-mers in linear space for quality value compression." Journal of Bioinformatics and Computational Biology 17, no. 05 (2019): 1940011. http://dx.doi.org/10.1142/s0219720019400110.
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Many bioinformatics tools heavily rely on [Formula: see text]-mer dictionaries to describe the composition of sequences and allow for faster reference-free algorithms or look-ups. Unfortunately, naive [Formula: see text]-mer dictionaries are very memory-inefficient, requiring very large amount of storage space to save each [Formula: see text]-mer. This problem is generally worsened by the necessity of an index for fast queries. In this work, we discuss how to build an indexed linear reference containing a set of input [Formula: see text]-mers and its application to the compression of quality scores in FASTQ files. Most of the entropies of sequencing data lie in the quality scores, and thus they are difficult to compress. Here, we present an application to improve the compressibility of quality values while preserving the information for SNP calling. We show how a dictionary of significant [Formula: see text]-mers, obtained from SNP databases and multiple genomes, can be indexed in linear space and used to improve the compression of quality value. Availability: The software is freely available at https://github.com/yhhshb/yalff .
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Gupta, Shweta, Sunita Yadav, and Rajesh Prasad. "Document Retrieval using Efficient Indexing Techniques." International Journal of Business Analytics 3, no. 4 (2016): 64–82. http://dx.doi.org/10.4018/ijban.2016100104.
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Document retrieval plays a crucial role in retrieving relevant documents. Relevancy depends upon the occurrences of query keywords in a document. Several documents include a similar key terms and hence they need to be indexed. Most of the indexing techniques are either based on inverted index or full-text index. Inverted index create lists and support word-based pattern queries. While full-text index handle queries comprise of any sequence of characters rather than just words. Problems arise when text cannot be separated as words in some western languages. Also, there are difficulties in space used by compressed versions of full-text indexes. Recently, one of the unique data structure called wavelet tree has been popular in the text compression and indexing. It indexes words or characters of the text documents and help in retrieving top ranked documents more efficiently. This paper presents a review on most recent efficient indexing techniques used in document retrieval.
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Navarro, Gonzalo. "Indexing Highly Repetitive String Collections, Part I." ACM Computing Surveys 54, no. 2 (2021): 1–31. http://dx.doi.org/10.1145/3434399.
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Two decades ago, a breakthrough in indexing string collections made it possible to represent them within their compressed space while at the same time offering indexed search functionalities. As this new technology permeated through applications like bioinformatics, the string collections experienced a growth that outperforms Moore’s Law and challenges our ability to handle them even in compressed form. It turns out, fortunately, that many of these rapidly growing string collections are highly repetitive, so that their information content is orders of magnitude lower than their plain size. The statistical compression methods used for classical collections, however, are blind to this repetitiveness, and therefore a new set of techniques has been developed to properly exploit it. The resulting indexes form a new generation of data structures able to handle the huge repetitive string collections that we are facing. In this survey, formed by two parts, we cover the algorithmic developments that have led to these data structures. In this first part, we describe the distinct compression paradigms that have been used to exploit repetitiveness, and the algorithmic techniques that provide direct access to the compressed strings. In the quest for an ideal measure of repetitiveness, we uncover a fascinating web of relations between those measures, as well as the limits up to which the data can be recovered, and up to which direct access to the compressed data can be provided. This is the basic aspect of indexability, which is covered in the second part of this survey.
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Gupta, Pranjal, Amine Mhedhbi, and Semih Salihoglu. "Columnar storage and list-based processing for graph database management systems." Proceedings of the VLDB Endowment 14, no. 11 (2021): 2491–504. http://dx.doi.org/10.14778/3476249.3476297.
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We revisit column-oriented storage and query processing techniques in the context of contemporary graph database management systems (GDBMSs). Similar to column-oriented RDBMSs, GDBMSs support read-heavy analytical workloads that however have fundamentally different data access patterns than traditional analytical workloads. We first derive a set of desiderata for optimizing storage and query processors of GDBMS based on their access patterns. We then present the design of columnar storage, compression, and query processing techniques based on these desiderata. In addition to showing direct integration of existing techniques from columnar RDBMSs, we also propose novel ones that are optimized for GDBMSs. These include a novel list-based query processor, which avoids expensive data copies of traditional block-based processors under many-to-many joins, a new data structure we call single-indexed edge property pages and an accompanying edge ID scheme, and a new application of Jacobson's bit vector index for compressing NULL values and empty lists. We integrated our techniques into the GraphflowDB in-memory GDBMS. Through extensive experiments, we demonstrate the scalability and query performance benefits of our techniques.