Indexing%20and%20Searching%20XML%20Documents%20based%20on%20Content%20and%20Structure%20Synopses

1
Indexing and Searching XML Documents based on
Content and Structure Synopses

• Weimin He, Leonidas Fegaras, David Levine
• University of Texas at Arlington
• http//lambda.uta.edu

2
Outline

• Motivation
• Key Contributions
• Related Work
• Data Synopses Indexing
• Query Processing
• Experimental Results
• Conclusion

3
Why not Google?

• Need to query both structure and content
• an opportunity for more precise search
• Keyword queries are NOT adequate for XML search
• An example query beyond Google
• Find the price of the book whose authors
  lastname is Smith and whose title contains
  XML and SAX
• Semantic search using an XPath Query
• //bookauthor/lastname Smithtitle
  XML and SAX/price
• Simpler query formats cannot express complex
  containment relationships
• (lastname, Smith), (title, XML SAX), price
  
• Fully indexing XML data is neither efficient nor
  scalable

4
Key Contributions

• A framework for indexing and searching
  schema-less XML documents based on data synopses
  extracted from documents
• Two novel data synopsis structures that can
  achieve higher query precision and scalability
• A hash-based processing algorithm to speed up
  searching
• A prototype implementation to evaluate the
  performance of the indexing scheme and to
  validate the data synopsis precision

5
Related Work

• Extend keyword queries to XML
• XRank
• XKSearch
• Integrate IR constructs and scoring into XQuery
• TIX
• TeXQuery
• XML Summarization Techniques
• XSketch
• XCluster

6
System Architecture
7
Specification of Search Queries

• XPath is extended with a simple IR syntax
• Queries may contain predicates of the form e
  S
• e is an XPath expression
• S is a search predicate that takes the form
• term S1 and S2 S1 or S2 (S)
• A running query example
• //auction//itemlocation
  Dallasdescription mountain and
  bicycle/price
• Query result
• A list of document locations (path names) that
  satisfy the query

8
Data Indexing

• Structural Summary (SS)
• A tree that captures all unique paths in an XML
  document
• It is constructed from XML data incrementally
• Each SSnode corresponds to a unique full label
  path
• 9 /auction/sponsor/address

9
Data Indexing (cont.)

• Content Synopsis (CS)
• Summarizes the text associated with an SS node in
  an XML document
• Approximated as a bit matrix of size WL
• L is fixed but W may depend on the document size
• Stored as a B-tree that implements the mapping
• (SSnode, doc) ? bit-matrix
• Used in evaluating search predicates in the query
• Positional Filter (PF)
• Captures the position spans of all XML elements
  associated with an SS node in an XML document
• Represented as a bit matrix of size ML, where M
  2
• Stored as a B-tree that implements the mapping
• (SSnode, doc) ? bit-matrix
• Used in enforcing containment constraints among
  query predicates
• Do we need positional dimension?

10
Data Synopsis Example
Query //auction//itemlocation
Dallasdescription mountain and
bicycle/price
11
Containment Filtering
Query //auction//itemlocation
Dallasdescription mountain and
bicycle/price
12
Query Processing Overview

• Query Footprint (QF) Extraction
• Query //auction//itemlocation
  Dallasdescription mountain and
  bicycle/price
• QF //auction//item0location
  1description 2/price
• Structural Summary Matching
• Retrieve all structural summaries that match the
  QF
• We use the standard preorder numbering scheme to
  represent an SS
• An SS is stored as a B-tree that implements the
  mapping
• tag ? (SS, SSnode, begin, end, level)
• We use containment joins to retrieve the
  qualified full label paths that match the entry
  points in the QF
• /auction/item, /auction/item/location,
  /auction/item/description
• Containment Filtering
• Qualified document locations are collected and
  returned
• The unit of query processing is a mapping from a
  doc to a bit matrix of size ML (positions)
• An empty bit matrix means an unqualified document

13
Two-Phase Containment Filtering

• Many sources of inefficiency
• A large number of full label path may match a
  single generic XPath query
• A long list of data synopses has to be retrieved
  for each label path in a QF
• The retrieved lists of data synopses have to be
  correlated at each step during containment
  filtering
• Solution
• Aggregate data synopses lists from multiple
  documents into a single bit matrix, called
  Document Synopsis, of size WD
• path ? bit-matrix
• so that, given a term t and a full label
  path p, the document doc is a candidate if the
  document synopsis for p is set at
  hash(t),hash(doc)
• Need a two-phase containment filtering algorithm
  to prune unqualified document locations before
  the actual containment filtering

14
Document Synopsis
The document synopsis for /biblio/book/paragraph
15
Experimental Setup

• A prototype system is implemented in Java
• Employed Berkeley DB Java Edition 3.2.13 as a
  storage manager
• Datasets
• XMark
• XBench

Data Set Data Size (MB) Files Avg. File Size (KB) Avg. SS Size (Byte) Avg. CS Size (Byte) Avg. PF Size (Byte)
XBench 1050 2666 394 432 20564 178
XMark 55.8 11500 5 417 306 16
16
Query Workload
Dataset Query Query Expression
XMark Q1 /site//itemlocation "United"payment "Creditcard" and "Check"/description
XMark Q2 //regions//itemlocation "States"payment "Creditcard" or "Cash"/name
XMark Q3 /site//itemlocation "United"payment "Creditcard"/description
XMark Q4 //regions//itemlocation "States"payment "Check"/quantity
XMark Q5 /site//itemdescription//text "gold"/name
XMark Q6 /regions//itemdescription//text "character "/payment
XMark Q7 //closed_auctiontype "Regular"annotation//text "heat"/date
XMark Q8 //closed_auctionannotation//text "heat" or "country"/seller
XMark Q9 //closed_auctionannotation//text "heat" and "country"/buyer
XMark Q10 //closed_auctionannotation//text "country"/type
XBench Q11 /article//bodyabstract/p "hockey"section/p "hockey" and "patterns"/section
XBench Q12 //article//bodysection/p "regular"abstract/p "hockey" or "patterns"/abstract
XBench Q13 /article//bodysection/subsec/p "hockey"abstract/p "hockey"/abstract
XBench Q14 /article//bodysection/subsec/p "regular"abstract/p "patterns"/section
XBench Q15 /article//bodysection/p "patterns"abstract/p "patterns"/abstract
XBench Q16 /article//bodysection/p "hockey"abstract/p "patterns"/abstract
XBench Q17 //prologkeywords/keyword "bold" or "regular"title "regular"/authors
XBench Q18 //prologkeywords/keyword "bold"title "bold"/title
XBench Q19 //prologgenre "Travel" keywords/keyword "bold" or "stealth" //author/name
XBench Q20 //prologgenre "Travel" keywords/keyword "bold"/title
17
Indexing Scheme Comparison
ILI using a standard XML indexing scheme based
on full Inverted Lists DSI using our indexing
scheme based on Data Synopses
18
Query Precision Measurement
ODBF using one-dimensional Bloom Filters TDBF
using two-dimensional Bloom Filters
19
Efficiency of Optimization Algorithm
OPCF using one-phase containment filtering TPCF
using two-phase containment filtering
20
Future Research Directions

• Develop an effective ranking function
• Adopt top-k algorithms to improve search
  efficiency
• Apply our framework to structured P2P networks
• Evaluate our framework over INEX data