article

XQueC: A query-conscious compressed XML database

Authors:

Angela Bonifati,

Ioana Manolescu,

Andrea PuglieseAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 7, Issue 2

Pages 10 - es

https://doi.org/10.1145/1239971.1239974

Published: 01 May 2007 Publication History

Abstract

XML compression has gained prominence recently because it counters the disadvantage of the verbose representation XML gives to data. In many applications, such as data exchange and data archiving, entirely compressing and decompressing a document is acceptable. In other applications, where queries must be run over compressed documents, compression may not be beneficial since the performance penalty in running the query processor over compressed data outweighs the data compression benefits. While balancing the interests of compression and query processing has received significant attention in the domain of relational databases, these results do not immediately translate to XML data.

In this article, we address the problem of embedding compression into XML databases without degrading query performance. Since the setting is rather different from relational databases, the choice of compression granularity and compression algorithms must be revisited. Query execution in the compressed domain must also be rethought in the framework of XML query processing due to the richer structure of XML data. Indeed, a proper storage design for the compressed data plays a crucial role here.

The XQueC system (XQuery Processor and Compressor) covers a wide set of XQuery queries in the compressed domain and relies on a workload-based cost model to perform the choices of the compression granules and of their corresponding compression algorithms. As a consequence, XQueC provides efficient query processing on compressed XML data. An extensive experimental assessment is presented, showing the effectiveness of the cost model, the compression ratios, and the query execution times.

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Cited By

Duc Luong DQuang Phuong VTung H(2023)AN IMPROVED INDEXING METHOD FOR QUERYING BIG XML FILESJournal of Computer Science and Cybernetics10.15625/1813-9663/19018(323-342)Online publication date: 25-Dec-2023
https://doi.org/10.15625/1813-9663/19018
Gong QZhang CLiang XReshniak VChen JRangarajan ARanka SVidal NWan LUllrich PPodhorszki NJacob RKlasky S(2023)Spatiotemporally Adaptive Compression for Scientific Dataset with Feature Preservation – A Case Study on Simulation Data with Extreme Climate Events Analysis2023 IEEE 19th International Conference on e-Science (e-Science)10.1109/e-Science58273.2023.10254796(1-10)Online publication date: 9-Oct-2023
https://doi.org/10.1109/e-Science58273.2023.10254796
Jiao PDi SGuo HZhao KTian JTao DLiang XCappello F(2022)Toward Quantity-of-Interest Preserving Lossy Compression for Scientific DataProceedings of the VLDB Endowment10.14778/3574245.357425516:4(697-710)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.14778/3574245.3574255
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Index Terms

XQueC: A query-conscious compressed XML database
1. Information systems
  1. Data management systems
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Database query processing and optimization (theory)

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cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 7, Issue 2

May 2007

152 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/1239971

Issue’s Table of Contents

Copyright © 2007 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 May 2007

Published in TOIT Volume 7, Issue 2

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Cited By

Duc Luong DQuang Phuong VTung H(2023)AN IMPROVED INDEXING METHOD FOR QUERYING BIG XML FILESJournal of Computer Science and Cybernetics10.15625/1813-9663/19018(323-342)Online publication date: 25-Dec-2023
https://doi.org/10.15625/1813-9663/19018
Gong QZhang CLiang XReshniak VChen JRangarajan ARanka SVidal NWan LUllrich PPodhorszki NJacob RKlasky S(2023)Spatiotemporally Adaptive Compression for Scientific Dataset with Feature Preservation – A Case Study on Simulation Data with Extreme Climate Events Analysis2023 IEEE 19th International Conference on e-Science (e-Science)10.1109/e-Science58273.2023.10254796(1-10)Online publication date: 9-Oct-2023
https://doi.org/10.1109/e-Science58273.2023.10254796
Jiao PDi SGuo HZhao KTian JTao DLiang XCappello F(2022)Toward Quantity-of-Interest Preserving Lossy Compression for Scientific DataProceedings of the VLDB Endowment10.14778/3574245.357425516:4(697-710)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.14778/3574245.3574255
Suciu DHaritsa J(2018)XML CompressionEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_783(4742-4748)Online publication date: 7-Dec-2018
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Shi SZhang R(2017)A data processing scheme based on CXML in mobile commerceJournal of Computational Methods in Sciences and Engineering10.3233/JCM-16070116:4(913-925)Online publication date: 17-Jan-2017
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Suciu DHaritsa J(2017)XML CompressionEncyclopedia of Database Systems10.1007/978-1-4899-7993-3_783-2(1-6)Online publication date: 25-Jan-2017
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Senthilkumar RNandagopal GRonald D(2015)QRFXFreeze: Queryable Compressor for RFXThe Scientific World Journal10.1155/2015/8647502015(1-8)Online publication date: 2015
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