INDEXING DATASPACES by Xin Dong

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INDEXING DATASPACESby Xin Dong Alon Halevy

• ITCS 6010
• FALL 2008
• Presented by VISHAL SHETH

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AGENDA

• Background
• Motivation
• Problem Definition
• Indexing Structure
• Experimental Evaluation
• Related Work
• Conclusion
• Future Work

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Background

• Indexing
• A technique used for faster execution of queries
  and result retrieval which can be created on one
  or more columns of DB table
• More indexes means faster query performance, but
  also longer transformation/load times
• Types of Indexes B-Tree, Bitmap
• Dataspace
• It is a data co-existence approach which forms a
  semantic web of inter-related / similar things.
  E.g. Music Dataspace
• DS Indexing v/s DB Indexing

DB INDEXING DS INDEXING
Indexing on tables of Relational DB of same source Indexing on dataspace having heterogeneous data sources
Data is structured Data may be structured or unstructured
Underlying DB Schema is very well defined (Relational) Underlying schema may/may not be known (DB, XML, Doc, PPT)
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Motivation

• Indexing of data from disparate data sources is a
  big problem and challenging
• To answer queries with keyword and structure
  efficiently
• Faster execution of queries on semantically
  different data

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• Indexing Heterogeneous Data
• Support queries over different types of data
• Data may or may not be having semantic similarity
• Data may be structured (XML/DB/Spreadsheet) or
  (un/partially)structured files (PPT/DOC/Email/LaTe
  x Files/WebPages)
• To extract associations / relationships between
  either structured or unstructured or both

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Solution to Indexing Heterogeneous Data

• Results of queries are typically from different
  sources (XML/tuples)
• Index (an inverted list) is built whose leaves
  are references to data items in the individual
  sources

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Solution Contd

• Data is modeled as a set of triples called as
  triple base which can take form of (instance,
  attribute, value) or (instance, association,
  instance)
• Instance is a real world object described by
  multi-valued attributes.
• Association is a directional relationship between
  two instances (two directions of a particular
  association are named differently)

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Example of a Triple Base
Legends a Article Instance, p Person
Instance, c Conference Instance a1 is
associated with p1, p2 and c1
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• Querying Heterogeneous Data
• Support queries over user independent data source
  structure
• Support queries that enable users to specify
  structure, or none at all

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Solution

• Two types of query proposed
• Predicate Queries
• Describes the desired instances by a set of
  predicates
• Each predicate specifies an attribute value or an
  associated instance
• Example Raghus Birch paper in Sigmod 1996
• Three predicates (title Birch), (author
  Raghu), (publishedIn 1996 Sigmod)
• Definition of a predicate query
• Each predicate is of the form (v, K1, . . .
  ,Kn). v (verb - attribute / association) and K1,
  . . . ,Kn (keywords)
• v attribute ? attribute predicate and v
  association ? association predicate
• Returned instances need to satisfy at least one
  predicate in the query.
• An instance satisfies an attribute predicate if
  it contains at least one of K1,. . . ,Kn in the
  values of attribute v or sub-attributes of v.
• An instance o satisfies an association predicate
  if there exists i, 1ltiltn, such that o has an
  association v or sub-association of v with an
  instance o that has an attribute value Ki.

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• Neighborhood Keyword Queries
• Extends keyword search by considering association
• A neighborhood keyword query is a set of
  keywords, K1, . . . ,Kn
• Definition of a Neighborhood Keyword query
• An instance satisfies a neighborhood keyword
  query if
• It contains at least one of K1, . . . ,Kn in
  attribute values. (relevant instance)
• OR
• The instance is associated (in either direction)
  with a relevant instance (associated instance)

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Inverted Lists

• It is a 2-D table with indexed keyword (as rows)
  and instances (as columns)
• Concept
• ith row represents indexed keyword Ki
• jth column represents instance Ij
• Cell (Ki, Ij) records no. of occurrences (called
  as occurrence count) of keyword Ki in the
  attributes of Ij
• Non zero cell value ? Instance Ij is indexed on
  Ki
• Keywords are sorted and arranged in an
  alphabetical order in the list
• Instances are ordered by their identifiers
• No structural information present
• Stored as sorted array or a prefix B-Tree

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Inverted Lists Contd
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Indexing Structure

• It is an extension to Inverted List addressing
  some of the issues (structural information). E.g.
  Tian Last Name or First Name ?
• It describes how attributes and association are
  indexed to support predicate queries
• Two ways
• Indexing Attribute ? ATtribute Inverted List
  (ATIL)
• Indexing Associations ? Attribute-Association
  Inverted List (AAIL)

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Indexing Attribute

• Indexing each attribute (excessive overhead)
• Specify the attribute name in the cells of IL
  (complex query answering)
• ATIL (k-Keyword, a-attribute, I-Instance)
• There is a row in IL for k//a//, when k appears
  in the value of a
• The cell (k//a//, I) records occurrence count
• E.g. Attribute Predicate (LastName, Tian)
• Query converted to Keyword query as
  Tian//LastName//
• Search yields p3 and not p1

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Indexing Association

• Perform keyword search on keywords, find a set of
  instances that contain these keywords and find
  associated instances for each instance (very
  expensive)
• AAIL (k-Keyword, r-association, I-Instance,
  a-attribute)
• There is a row in IL for k//r//, when k appears
  in the value of a
• The cell (k//r//, I) records occurrence count
• E.g. Query Raghus Paper
• It has an association predicate author
  Raghu and keyword raghu//author//
• Search yields a1
• ATIL association information ? Slightly slow in
  answering attribute predicates but speeds up
  answering association predicates

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Indexing Hierarchies

• Answering predicate queries having hierarchical
  structure
• E.g. Query (Name, Tian) Results p1 and p3
• Find all the descendants of an attribute
  (FirstName, LastName and NickName)
• Expand the scope of query by adding above
  attributes
• E.g. Tian//Name// OR Tian//FirstName// and so
  on
• This incurs multiple index lookups and hence
  expensive
• Solution
• Attribute IL with duplication (Dup-ATIL)
• Attribute IL with Hierarchies (Hier-ATIL)
• Hybrid Attribute IL (Hybrid-ATIL)

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Index With Duplication

• Duplicate a row with attribute name for each of
  its ancestors
• Dup-ATIL (k-Keyword, a0-attribute, a-ancestor of
  a0, I-Instance)
• There is a row in IL for k//a//
• The cell (k//a//, I) records occurrence count of
  k in values of a of I
• E.g. Query Name Tian ? Results retrieved
  p1 and p3
• Extensive index size (long hierarchy) ? problem?
• Appropriate when k occurs in many a0 with common
  ancestors

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Index with Hierarchy Path

• Keyword includes the hierarchy path
• Hier-ATIL (k-Keyword, a-attribute, I-Instance)
• Hierarchy path a0////an// for attribute an
• There is a row for k//a0////an//
• The cell (k//a0////an//, I) records occurrence
  count of k in Is an attributes
• E.g. Query Name Tian ? Prefix Search
  Tian//Name// ? Results p1 and p3
• Answering query by converting into prefix search
  can be more expensive than a keyword search
• Appropriate when k occurs in a few a with common
  ancestors

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Hybrid Index

• Combination of Dup-ATIL and Hier-ATIL
• Hybrid-ATIL (k-Keyword, a0-attribute, a-ancestor
  of a0, I-Instance)
• Build an IL that answers prefix-search query
  with rows lt threshold (t)
• Hierarchy path a0////an// for attribute an
• p k//a0////an// is an indexed keyword
• The cell (p//, I) records occurrence count of k
  in Is an attributes
• E.g. Query Name Jeff ? Prefix Search
  Jeff//Name// ? Result p3
• E.g. Query Name Tian ? Prefix Search
  Tian//Name// ? Result p1 and p3

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Neighborhood Keyword Queries

• Keyword Inverted List (KIL)
• Equal to Hybrid-AAIL
• Summarize prefixes ending with hierarchy path and
  also the one corresponding to keywords
• Keywords (k1,,kn) are transformed to a prefix
  search (k1//,, kn//)
• E.g. Query birch ? prefix-search birch//
  ? results a1, c1, p1, p2

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Experimental Evaluation

• Indexing structure text ? improves performance
  in answering both the type of queries
• Data set personal data on desktop some
  external sources
• Extracted associations and relationships from
  disparate items are stored in RDF file managed by
  Jena
• RDF Resource Description Framework
• Jena Java framework supporting Semantic Web
  applications
• RDF file had 105,320 object instances 300,354
  attribute values 468,402 association instances
  file size 52.4 MB
• Four types of queries
• PQAS Predicate Queries with Attribute (no
  sub-attributes)
• PQAC Predicate Queries with Attribute (with
  sub-attributes)
• PQR Predicate Queries with association
• NKQ Neighborhood Keyword Queries
• Hardware
• 4 CPUs (with 3.2 GHz Processor and 1 MB Cache
  memory)
• 1 GB memory (RAM)

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Performance

• Alternative approaches NAÏVE (Basic IL) and
  SEPIL (3 separate indexes (IL, structured index
  relationship index)
• Both returned instances with no occurrence count
  and hence an extra overhead
• Clauses Introducing some variation (E.g. change
  no. of keywords)

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Performance Contd

• Compare efficiency of ATIL with a technique that
  creates separate index for each attribute
• ATIL reduces indexing time by 63 and
  keyword-lookup time by 33

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Related Work

• Indexing XML
• Indexing on Structure
• Schema-driven queries (list all book authors)
• Does not index text values
• Indexing on Value
• Indexes text values and encodes
  parent-child/ancestor-descendant relation
• Indexing on both
• Combines indexes on structure and on text
• Indexing keyword queries in R-DB
• DISCOVER, DBXplorer and BANKS require
  join-network at run-time which is expensive

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Conclusion

• Novel indexing approach to support flexible
  querying over dataspaces
• Inverted list are used for creating indexes
• IL captures the structure including attributes of
  instances, relationships between instances and
  hierarchies of schema elements.
• The experimental results shows that IL speeds up
  query answering

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Future Work

• Extend indexes to support heterogeneous
  (attribute) values
• Appropriate ranking algorithms