Combining Efficient XML Compression with Query Processing

Skibiński, Przemysław; Swacha, Jakub

doi:10.1007/978-3-540-75185-4_24

Cited by 14 publications

(17 citation statements)

References 9 publications

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“…Even the similar approach in [2], using a structural summary and extent lists for each path class, loses document order and, thus, expensive joins are needed to reconstruct the document. This statement also holds for various XML compression techniques (e.g., [34,38]), which also get rid of the internal structure, but, in contrast, only provide narrow subsets of the mentioned interfaces.…”

Section: Related Workmentioning

confidence: 82%

“…However, all these approaches are coarse-granular, i.e., they are applied to the entire document (or file) at a time. Furthermore, they directly compress the plain, i.e., "verbose" representation, assume static and filebased application scenarios with single-user operations, are often context-dependent which requires large auxiliary data structures (vocabularies), and provoke potentially substantial compression/decompression overhead [38]. Therefore, these methods are not adequate for dynamic XML structures processed in a multi-user transactional DBMS context and, in turn, cannot be considered as storage formats for finegrained tree-like structures where each tree node can be addressed and manipulated separately.…”

Section: Applying Xml Compressionmentioning

confidence: 99%

“…There exists a large body of scientific contributions dealing with XML compression technologies [34,38] promising enormous gains in storage saving and, at the same time, enabling a kind of query processing (for very simple XPath expressions). However, all these approaches are coarse-granular, i.e., they are applied to the entire document (or file) at a time.…”

Section: Applying Xml Compressionmentioning

confidence: 99%

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Storing and indexing XML documents upside down

Mathis

Härder

Schmidt

2009

Comp. Sci. Res. Dev.

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XML documents contain substantial redundancy in their structure part, because each path from the root node to a leaf node is explicitly represented and typically large sets of such path instances belong to a path class, i.e., the nodes of the path instances are labeled by the same sequence of element (or attribute) names. To save storage space and I/O cost, we want to get rid of this structural redundancy to the extent possible. While all known methods for the physical representation (storage) of XML documents proceed from the root via the element/attribute hierarchy (internal nodes) down to the leaves (values), we follow an upsidedown approach which explicitly stores the values and only reconstructs the internal nodes, if needed. The cornerstones for such a solution are suitable node labels and a path synopsis which efficiently represents all path classes of an XML document. As a solution, we propose a compact internal storage format for native XML database systems where the inner structure of the stored documents is virtualized. Because this elementless storage format provides an efficient reconstruction of a document using its path synopsis, all processing properties are preserved and the semantics of navigational and declarative operations of XML languages remains unchanged. Adjusted indexes support the full spectrum of so-called content-and-structure single path queries.Financial support by the Research Center (CM) 2 of the University of Kaiserslautern is acknowledged Apart from greatly reduced storage consumption, our approach demonstrates its superiority, compared to competing methods, not only for a substantial fraction of those queries, but also for storing, reconstructing, and navigating XML documents. MotivationXML models semi-structured data and is becoming the standard for data exchange in many (Web) applications. Because the dramatically growing volumes thereby incurred have to be saved for a long time (for legal and other reasons) and messages are data, too, database systems are a proven technology to persistently store and conveniently manage such data. To avoid conversion, not only messages but also conventional DB data are increasingly kept in native XML format, often resulting in collections of huge XML documents. Furthermore, XML's flexibility, i.e., the ability to change the data mapping (freedom of cardinality determination, handling of varying or non-existing structures, etc.) without too much impact to applications [25], is also a driving factor to enable heterogeneous data stores and to facilitate data integration. For these reasons, XML databases currently get more and more momentum if data flexibility in various forms is a key requirement of the application.As XML documents permeate information systems and databases with increasing pace, they are also increasingly 1 3

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Section: Related Workmentioning

confidence: 82%

Section: Applying Xml Compressionmentioning

confidence: 99%

Section: Applying Xml Compressionmentioning

confidence: 99%

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Storing and indexing XML documents upside down

Mathis

Härder

Schmidt

2009

Comp. Sci. Res. Dev.

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“…Then, by using this sequence, the query executor checks whether the encoded values of XML tags are in an interval of the sequence or not. XQueC [9] exploits indexing and XML storage strategies since it is focused on search speed rather than compression efficiency. The above tools execute path-based queries, which allow syntactic match of document elements and are strictly tied to the XML Schema of the compressed document.…”

Section: B Related Workmentioning

confidence: 99%

A Framework for Query Processing over Compressed Knowledge Bases

Scioscia

Tinelli

2011

2011 IEEE 12th International Conference on Mobile Data Management

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Abstract-In semantic-based pervasive computing, annotated information is tied to micro-devices, such as RFID tags and wireless sensors, deployed in an environment. Compression techniques are essential, because of the verbosity of standard XMLbased languages for ontologies and semantic annotations. Beyond compression ratio, query efficiency is a key aspect. This paper presents a framework for querying knowledge bases expressed in OWL, serialized in RDF/XML syntax and compressed with a homomorphic encoding, in order to allow query evaluation without requiring decompression. Formalization of a significant set of queries demonstrates feasibility of the approach, while practical examples highlight its usefulness.

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“…Existing XML physical models are either non-queryable ( [1], [2], [3], [4]) or have other drawbacks, e.g. requiring large sections of data to be decompressed in order to access a single value ( [5], [6], [7], [8], [9], [10], [11]). In all cases these existing storage models require the entire data structure to be transferred as a single unit to allow any access to the data contained within.…”

Section: Introductionmentioning

confidence: 99%

Data Value Storage for Compressed Semi-structured Data

Tripney

Ross

Wilson

et al. 2013

Lecture Notes in Computer Science

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Abstract. Growing user expectations of anywhere, anytime access to information require new types of data representations to be considered. While semi-structured data is a common exchange format, its verbose nature makes files of this type too large to be transferred quickly, especially where only a small part of that data is required by the user. There is consequently a need to develop new models of data storage to support the sharing of small segments of semi-structured data since existing XML compressors require the transfer of the entire compressed structure as a single unit. This paper examines the potential for bisimilarity-based partitioning (i.e. the grouping of items with similar structural patterns) to be combined with dictionary compression methods to produce a data storage model that remains directly accessible for query processing whilst facilitating the sharing of individual data segments. Study of the effects of differing types of bisimilarity upon the storage of data values identified the use of both forwards and backwards bisimilarity as the most promising basis for a dictionary-compressed structure. A query strategy is detailed that takes advantage of the compressed structure to reduce the number of data segments that must be accessed (and therefore transferred) to answer a query. A method to remove redundancy within the data dictionaries is also described and shown to have a positive effect on memory usage.

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Combining Efficient XML Compression with Query Processing

Cited by 14 publications

References 9 publications

Storing and indexing XML documents upside down

Storing and indexing XML documents upside down

A Framework for Query Processing over Compressed Knowledge Bases

Data Value Storage for Compressed Semi-structured Data

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