ViST: a dynamic index method for querying XML data by tree structures Authors: Haixun Wang, Sanghyun Park, Wei Fan, Philip Yu Presenter: Elena Zheleva, November 2003

Overview Modeling XML Queries Structure-encoded sequences Indexing ViST Experimental Results

Modeling XML Queries

DTD of purchase records: (!ELEMENT purchases (purchase*)) (!ELEMENT purchase (seller, buyer)) (!ATTRIST seller ID ID location CDATA name CDATA) (!ELEMENT seller (item*)) (!ATTRIST buyer ID ID location CDATA name CDATA) (!ELEMENT item (item*)) (!ATTRIST item name CDATA manufacturer CDATA)

Modeling XML Queries Focus in XML query language design: ability to express complex structural or graphical queries

Modeling XML Queries Querying XML data = finding sub structures of the data graph that match the sequence Structure-encoded sequences: a sequential representation of both XML data and XML queries

Structure-Encoded Sequences

Maps the data and the queries Matches the subsequence Purpose: to avoid as many join operations as possible Def. Sequence of (symbol, prefix) pairs

Mapping Data Represent XML document/tree in preorder Represent in structure-encoded seq

Mapping Queries Benefit of sequence matching: query gets processed as whole Path Expression

Structure-Encoded Sequences Query Data

Querying XML through Structure-Encoded Sequence Matching

Indexing

Role of Indexing To provide an algorithm to perform this sequence matching Desired features for algorithm: –Efficient support for subsequence matching –Use well-supported DB indexing techniques such as B+ trees –Allow dynamic index insertion

What is indexing useful for Auxiliary access structures –Used to speed up the retrieval of records –In response to certain search conditions Provide efficient support for arbitrary structured queries –Using wild-cards // and *

Indexing State-of the-art approaches –Indexes on paths –Indexes on nodes –Indexes on both (structures) – ViST

ViST

Algorithms Naïve Algorithm based on Suffix Trees RIST: Relationships Indexed Suffix Tree ViST: Virtual Suffix Tree

Algorithm Using Suffix Trees Suffix Tree: a compact index to all distinct, contiguous substrings of a string D-Ancestorship – in XML doc tree Through structure-encoded sequence S-Ancestorship – in suffix tree

Example Using Suffix Trees

Algorithm Using Suffix Trees Searches –first by S-Ancestorship: searching under suffix tree –then by D-Ancestorship: matching nodes and prefixes Disadvantages: –Costly – traverse large portion of subtree –Most commercial DBMSs do not support

RIST: Indexing by Ancestor- Descendant Relationships Jumps directly to the nodes Y to which X is both a D-Ancestor and S-Ancestor Index Construction: uses B+ trees

RIST: Indexing by Ancestor- Descendant Relationships Subsequence Matching Determine D-Ancestorship by prefixes Determine S-Ancestorship by label x – suffix tree node (root of S-tree) nx – prefix traversal order sizex – number of descendants

ViST: the Virtual Suffix Tree Same sequence algorithm as RIST BUT supports dynamic insertions Uses dynamic method to assign labels Once assigned, the labels are fixed and are not affected by subsequent data insertion or deletion Labeling the suffix tree w/o building it Relies on statistical information about the XML data

ViST: the Virtual Suffix Tree Index structure contains the sequence: Sequence to be inserted: Dynamic scope of x =

ViST: the Virtual Suffix Tree

Experimental Results Datasets used –DBLP: CS bibliography DBDBLP 289,627 records/publications Each publication – tree of max depth 6 Avg length of structure-encoded seq = 31 –XMARKXMARK 1 record Complicated tree structure –Synthetic

Experimental Results Comparison Methods –Index Fabric Algorithm – XML paths –XISS – uses nodes as basic query unit –ViST – appx. 1/10 of time to perform queries due to (multiple) join operations

Experimental Results - remove Index Structure and Size (1/3 less from suffix tree) –DocId B+ Tree – N elements –Combined D-ancestor and S-ancestor B+ tree - N x L elements Index Construction

Conclusion XML Queries = Subsequence Matching Advantages of ViST – algorithm for subsequence matching –Avoids expensive join operations –Index on both content and structure of XML documents –B+ trees – supported by disk-based data –Dynamic data insertion and deletion