efficient keyword search over virtual xml views

Efficient Keyword Search over Virtual XML Views

Feng Shao and Lin Guo and Chavdar Botev

and Anand Bhaskar and Muthiah Chettiar and Fan Yang

Cornell University

Jayavel Shanmugasundaram

Yahoo! Research

2008. 02. 14.Summarized by Dongmin Shin, IDS Lab., Seoul National University

Presented by Dongmin Shin, IDS Lab., Seoul National University

Copyright 2007 by CEBT

Index

Introduction Background System Overview QPT Generation Module PDT Generation Module Experiments Conclusion and Future Work

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Index


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Introduction

Fundamental assumption of tradi-tional information retrieval systems

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The set of documents being searched

is materialized.


Introduction

But

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The view is often virtual (unmaterial-ized)

Aggregator may not have resources to materialize all the data

If the view is materialized, the contents of the view may be out-of-date or maintaining the view may be expensive

The data sources may not wish to provide the entire data


Introduction

Efficiently evaluating keyword search queries

over virtual XML views

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Need


Index


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Background

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Background

XML Scoring

tf(e,k) : the number of distinct occurrences of the key-word k in element e and its descendants

idf(k) =

score(e,Q) =

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TF-IDF method


Index


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System Overview

(1) Keyword queries over virtual views

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(2) The parser redirects the query to the Query Pattern Tree(QPT) Generation Module

(3) QPT is sent to the Pruned Document Tree(PDT) Genera-tion Module

(4) Generate PDTs using only the path indices and inverted list indices

(5) Rewritten query and PDTs are sent to Evaluator(6) Produce the view that contains all view elements with pruned content

(7) Elements are scored, only those with highest scores are fully materialized using document storage


System Overview

XML Storage Dewey IDs

– Popular id format

– Hierarchical numbering scheme

– ID of an element contains the ID of its parent

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System Overview

XML Indexing Path indices

– Evaluate XML path and twig(i.e., branching path)

– Store XML paths with values in a relational table

– Use indices such as B+-tree

– One row for each unique

(Path, Value) pair

– IDList : the list of ids of

all elements on the path

– B+-tree index is built on the (Path, Value) pair

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System Overview

Inverted list indices– Store the list of XML elements that directly contain the keyword

for each keyword in the document collection

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Index


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QPT Generation Module

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Index


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PDT Generation Module

Output Only contains elements that correspond to nodes in the

QPT Only contains element values that are required during

query evaluation

Advantage Query evaluation is likely to be more efficient and scalable Allows us to use the regular(unmodified) query evaluator

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Key Idea An element e in the document corresponding to a node n in

the QPT is selected for inclusion only if it satisfies three types of constraints(1) Ancestor constraint – an ancestor element of e that corre-

sponds to the parent of n in the QPT should also be selected

(2) Descendant constraint – for each mandatory edge from n to a child of n in the QPT, at least one child/descendant element of e corresponding to that child of n should also be selected

(3) Predicate Constraint – if e is a leaf node, it satisfies all predi-cates associated with n

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PrepareList

(1) Issues a lookup on path indices for each QPT node that has no mandatory child edges

(2) Identifies nodes that have a ‘v’ annotation to obtain values and ids

(3) Looks up inverted lists indices and retrieves the list of Dewey IDs containing the keywords along with tf values

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Candidate Tree(CT)

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Step 1 : adding new IDs– Adds the current minimum IDs in pathLists

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Step 2 : creating PDT nodes– Create PDT nodes using CT nodes

– Top-down

– Check DM value of each CT node if it is “1”, create it in pdt cache If not, check children of that node

If DM value of that children node is “1”, create is in pdt cache of parent node

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Step 3 : removing CT nodes– Bottom-up

– Check if each node satisfies ancestor constraints If not, remove If so, propagate to the pdt cache of the ancestor

– If some node has no children and does not satisfy descendant constraints, remove

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– When we remove the root node “books”, all IDs in its pdt cache will be propagated to the result PDT

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Index


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Experiments

500MB INEX dataset

Varying parameters Size of data, # keywords, selectivity of keywords # of joins, join selectivity, level of nestings # of results, Avg. size of view element

Four alternative approaches Baseline GTP : general solution to integrate structure and keyword

search queries Efficient : proposed architecture Proj : techniques of projecting XML documents

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Experiments

EFFICIENT is a scalable and efficient soultion

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The cost of generating PDTs scales gracefully

Overhead of post-processing(scoring and ma-terializing) is negligible

The cost of the query evalua-tor dominates the entire cost


Experiments

Run time for EFFICIENT in-creases slightly Because it accesses more

inverted lists to retrieve tf values

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Run time for EFFICIENT in-creases Because the cost of the

query evaluation increases


Index


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Conclusion and Future Work

Conclusion A general technique for evaluating keyword search queries

over views Efficient over a wide range of parameters

Future Work Instead of using the regular query evaluator, we could use

the techniques proposed for ranked query evaluation Views may contain non-monotonic operators such as group-

by

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efficient keyword search over virtual xml views

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