The BIRD Numbering Scheme for XML and Tree Databases – Deciding and Reconstructing Tree Relations Using Efficient Arithmetic Operations

Weigel, Felix; Schulz, Klaus U.; Meuss, Holger

doi:10.1007/11547273_5

Cited by 23 publications

(24 citation statements)

References 17 publications

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“…Alternatively, failure to relabel a tree results in a label collision, when one label is supposed to represent two distinct nodes. A number of papers have addressed this issue through reusable labels, extra space for each label, and using numeric fractions/alphanumeric labels [30,22,14,4]. Therefore, the applicability of this model is limited to deep and static hierarchies.…”

Section: Nested Set Modelmentioning

confidence: 99%

Reusable Prime Number Labeling Scheme for Hierarchical Data Representation in Relational Databases

Morozov

Saiedian

Wang

2014

CIT

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Hierarchical data structures are important for many computing and information science disciplines including data mining, terrain modeling, and image analysis. There are many specialized hierarchical data management systems, but they are not always available. Alternatively, relational databases are far more common and offer superior reliability, scalability, and performance. However, relational databases cannot natively store and manage hierarchical data. Labeling schemes resolve this issue by labeling all nodes with alphanumeric strings that can be safely stored and retrieved from a database. One such scheme uses prime numbers for its labeling purposes, however the performance and space utilization of this method are not optimal. We propose a more efficient and compact version of this approach.

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Section: Nested Set Modelmentioning

confidence: 99%

Reusable Prime Number Labeling Scheme for Hierarchical Data Representation in Relational Databases

Morozov

Saiedian

Wang

2014

CIT

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“…If the tree becomes too deep or fan-outs vary significantly the label sizes quickly degenerate (up to O(n) where n is the number of nodes in the tree). Finally arithmetic labeling schemes (such as BIRD [45]) use some kind of arithmetic relation between labels to express child-parent, ancestor-descendant relationships. For example, prime numbers are assigned to the leaf nodes in the tree and labels of parent nodes become the product of the labels of their child nodes.…”

Section: Foundation: Tree Labelingmentioning

confidence: 99%

Labeling RDF Graphs for Linear Time and Space Querying

Furche

Weinzierl

Bry

2009

Semantic Web Information Management

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Abstract. Indices and data structures for web querying have mostly considered tree shaped data, reflecting the view of XML documents as tree-shaped. However, for RDF (and when querying ID/IDREF constraints in XML) data is indisputably graph-shaped. In this chapter, we first study existing indexing and labeling schemes for RDF and graph data in general with focus on support for efficient adjacency and reachability queries. For XML, labeling schemes are an important part of the widespread adoption of XML, in particular for mapping XML to existing (relational) database technology. However, the existing indexing and labeling schemes for RDF (and graph data in general) sacrifice one of the most attractive properties of XML labeling schemes, the constant time (and per-node space) test for adjacency (child) and reachability (descendant). In the second part, we introduce the first labeling scheme for RDF data that retains this property and thus achieves linear time and space processing of acyclic RDF queries on a significantly larger class of graphs than previous approaches (which are mostly limited to tree-shaped data). Finally, we show how this labeling scheme can be applied to (acyclic) SPARQL queries to obtain an evaluation algorithm with time and space complexity linear in the number of resources in the queried RDF graph.

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“…Other ad hoc numbering schemes for trees include PBiTree coding [115] and Balanced Index-based numbering scheme for Reconstruction and Decision (BIRD) coding [118], which require arithmetic operations (for example, division) to determine relationships between data nodes.…”

Section: Numbering Schemesmentioning

confidence: 99%

Efficiently Querying Large XML Data Repositories: A Survey

Gou

Chirkova

2007

IEEE Trans. Knowl. Data Eng.

139

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Abstract-Extensible Markup Language (XML) is emerging as a de facto standard for information exchange among various applications on the World Wide Web. There has been a growing need for developing high-performance techniques to query large XML data repositories efficiently. One important problem in XML query processing is twig pattern matching, that is, finding in an XML data tree D all matches that satisfy a specified twig (or path) query pattern Q. In this survey, we review, classify, and compare major techniques for twig pattern matching.1 Specifically, we consider two classes of major XML query processing techniques: the relational approach and the native approach. The relational approach directly utilizes existing relational database systems to store and query XML data, which enables the use of all important techniques that have been developed for relational databases, whereas in the native approach, specialized storage and query processing systems tailored for XML data are developed from scratch to further improve XML query performance. As implied by existing work, XML data querying and management are developing in the direction of integrating the relational approach with the native approach, which could result in higher query processing performance and also significantly reduce system reengineering costs.Index Terms-XML query processing, twig pattern matching.

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The BIRD Numbering Scheme for XML and Tree Databases – Deciding and Reconstructing Tree Relations Using Efficient Arithmetic Operations

Cited by 23 publications

References 17 publications

Reusable Prime Number Labeling Scheme for Hierarchical Data Representation in Relational Databases

Reusable Prime Number Labeling Scheme for Hierarchical Data Representation in Relational Databases

Labeling RDF Graphs for Linear Time and Space Querying

Efficiently Querying Large XML Data Repositories: A Survey

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