On Efficient Partmatch Querying of XML Data - PowerPoint PPT Presentation

Title: On Efficient Partmatch Querying of XML Data


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On Efficient Part-match Querying of XML Data

• Michal Krátký, michal.kratky_at_vsb.cz
• Marek Andrt, marek.andrt_at_vsb.cz
• Department of Computer Science
• VBTechnical University of Ostrava
• Czech Republic

DATESO 2004
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Contents

• Introduction XML, query languages, indexing XML
  data, part-match querying.
• Multi-dimensional approach to indexing XML data.
• Extension of the multi-dimensional approach for
  keyword-based querying.
• Index data structures.
• Preliminary experimental results.

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Introduction

• Native XML database. Set of documents is a
  database, DTD (XML Schema) is its database
  schema.
• XML query languages (XPath, XQL, XQuery,).
• A common feature is a possibility to formulate
  paths in the XML graph (regular path expressions,
  XPath axes and so on).
• Approaches based on relational decomposition,
  trie, multi-dimensional, signatures and so on.

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Part-match querying XML data

• Some approaches for keyword or phrase based
  searching were published XQuery-IR (WebDb02),
  XKeyword (ICDE03) and so on.
• Knowledges from IR are applied.
• Query languages contain operators for matching
  term occurrence. For example contains(), .

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Multi-dimensional approach to indexing XML data

• A graph is a set of the paths. XML document is
  decomposed to paths and labelled paths.
• labelled path lp ? XLP
• s0,s1,...,slPN
• path p ? XP
• idU(u0),idU(u1),...,idU(ulLP),s
• idU(ui) unique number
• of a node ui

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Indexes

• Term index a storage of strings si of an XML
  document and their idT(si).
• Labelled path index a storage of points
  representing labelled paths.
• Path index a storage of points representing
  paths.

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Examplelabelled path index, path index

• books,book,id books,book,title and
  books,book,author. Points (0,1,2) (0,1,4) and
  (0,1,6) are created using idT of element and
  attribute names, idLP 0, 1 and 2.
• For example, the path to value The Two Towers.
  The labelled path books,book,title with idLP 1
  belongs. Vector (1,0,1,3,5) is created using
  idLP, unique numbers idU of elements, and idT of
  the term.

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Query for values of elements and attributes

• XPath query books/bookauthorJoseph Heller
• 3 phases of a query processing, finding
• ? idT of terms from the term index,
• ? idLP 2 of labelled path books,book,author
  from the labelled path index point query
  (0,1,6),
• ? points from the path index range query
• (2,0,0,0,12)(2,max,max,max,12).

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Enhanced querying

• XPath axes are processed by a range query or
  sequence of range queries. For example axis
  descendent (0,idU(u0),,idU(ul-1), idU(u),0,,
  0)(maxD,idU(u0),,idU(ul-1), idU(u),
  maxD,,maxD).
• Regular path expression. For example
  //titlenameChaudhri is processed by a
  complex range query. The query is possible to
  process in one run in the multi-dimensional data
  structure.

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Comparison of approaches

• Mainline approaches (XISS, XPath Accelerator)
  index single element (attribute). For example
  query /e1e2dog is processed by joining
  single results.
• Result formatting. For example a result of the
  query //name is all matched subtree.
• Operation Update and Insert are simple possible.

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Keyword-based searching

• Motivation
• /PLAYPERSONAE/PERSONAOTHELLO/TITLE
• Path-Labelled Path-Term (PLT) index is added.
• The index indexes an 3-dimensional space (idP,
  idLP, idT).
• idP is added into the point representing path
  (idP,idLP,idU0,idU1,,idUl,s).

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Path-Labelled Path-Term index Example
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Query processing plan Example
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Index data structures

• Paged and balanced multi-dimensional data
  structures (B)UB-trees, variants of R-trees.
• Problems
• ? indexing points with different dimensions.
• ? narrow range query the signature is
  applied for efficient processing Signature
  R-tree.
• Efficient processing of the complex range query.

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Efficient processing the complex range query

• Complex range query sequence of range queries
  qb1,qb2,,qbn.
• The query is possible to process in one run in
  the multi-dimensional data structure.

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Experimental results

• Protein Sequence Database XML document
• ? the document size is 683MB,
• ? number of elements 21,305,818,
• ? number of attributes1,290,647.
• ? maximal length of path 7.
• BUB-forest, R-forest, Signature BUB-tree and
  R-tree. Index structures trees indexing spaces
  of dimension n7 and n9.

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Experimental results
Queries ProteinDatabase/ProteinEntry/reference/r
efinfo/ authors/author'Smith, E.L.'
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Experimental results Regular path expression

• Query //uid'89071748' , 5 labelled paths were
  matched.
• Naive processing the complex range query DAC
  368
• Efficient processing the complex range query
  DAC 139
• Time 0.03s, Improvement 2.5x

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Preliminary experimental results Keyword-based
searching

• othello.xml
• ? document size is 250kB,
• ? maximal length of the path 6
• ? number of paths 4,967
• ? number of labelled paths 13
• ? number of terms 8,744
• ? PLT index 27,127

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Preliminary experimental results Keyword-based
searching

• Query /PLAYPERSONAE/PERSONAOTHELLO/TITLE
• Labelled path index result size 1, DAC 3
• PLT index result size 1, DAC 3
• Path index result size 1, DAC 13
• Path index result size 1, DAC 4

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Conclusion

• T(m log n), T(c m log n) vs. T(m1 m2), m1
  ,m2 m.
• Efficient processing a query with AND condition.
  Signature is applied.
• Multi-dimensional approach for term searching may
  be applied (e.g. comp).
• The update operation of XML documents.
• Comparison with another approaches for test
  collections (INEX, XMark, ).

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http//www.cs.vsb.cz/arg
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References

• M. Krátký, J. Pokorný, V. Snáel Implementation
  of XPath Axes in the Multi-dimensional Approach
  to Indexing XML Data. Accepted at International
  Workshop on Database Technologies for Handling
  XML information on the Web, DataX, Int'l
  Conference on EDBT, Heraklion - Crete, Greece,
  2004.
• M. Krátký, J. Pokorný, T. Skopal, V. Snáel The
  Geometric Framework for Exact and Similarity
  Querying XML data. In Proceedings of EurAsia-ICT
  2002. Shiraz, Iran, Springer Verlag, LNCS 2510.
• M. Krátký, T. Skopal, and V. Snáel
  Multidimensional Term Indexing for Efficient
  Processing of Complex Queries. Kybernetika,
  Journal of the Academy of Sciences of the Czech
  Republic, 2004, accepted.

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Paths, labelled paths

• Paths 0,1,2,003-04212 0,5,6,001-00863 and
  0,9,10,045-00012 belong to the labelled path
  books,book,id,
• . . .
• Paths 0,1,4,J.R.R. Tolkien 0,5,8,J.R.R.
  Tolkien and 0,9,12,Joseph Heller belong to the
  labelled path books,book,author.

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Complex queries

• Query for values and XPath axis processing,
• e.g. books/bookauthor'Joseph Heller'/title
• ? Combination of above described techniques
  query for value, XPath axis processing.
• Regular path expression queries
• for example books//author
• ? A sequence of range queries processes this
  query in the path and labelled path index books,
  author - books,,author - books,,,,author.

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(B)UB-tree, R-tree
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Narrow range query signature multi-dimensional
ds

• Regions intersecting a query hyper box are
  searched, O(NI logc n).
• Ratio cR of relevant NR and intersect NI regions
• 1 with an increasing dimension.
• Signatures are applied to better filtration of
  irrelevant regions signature md structures.

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Signature R-tree
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Experimental results
Queries ProteinDatabase/ProteinEntry/reference/r
efinfo/ authors/author'Smith, E.L.'
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Experimental results