WHOWEDA%20:%20Warehouse%20of%20Web%20Data

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• WHOWEDA Warehouse of Web Data
• Sanjay Kumar Madria
• Department of Computer Science
• Purdue University, West Lafayette, IN 47907
• skm_at_cs.purdue.edu

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www.is.a.mess
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WWW

• Huge, widely distributed, hetreogenous collection
  of semi-structured multimedia documents in the
  form of web pages connected via hyperlinks.

4
Characteristics of WWW

• WWW is a set of directed graphs
• data in the WWW has a heterogeneous nature
• unstructured versus structured information
• no central authority to manage information
• Dynamic verses static information
• Web information discoveries - search engines

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As WWW grows, more chaotic it becomes

• Web is fast growing, distributed,
  non-administered global information resource
• WWW allows access to text, image, video, sound
  and graphic data
• more business organizations creating web servers
  - e-commerce
• more chaotic environment to locate information of
  interest
• lost in hyperspace syndrome

6
WWW data - Does it affect the corporate world?

• Lack of credibility of data
• Different sites with different data
• Same site different data
• Historical information is not available
• Previous versions of web data
• How does web data change with time
• Summarization over time
• Data to information
• Reduction in productivity
• Analysis is manual

7
How users find web sites

• Indexes and search engines 75
• UseNet newsgroups 44
• Cool lists 27
• New lists 24
• Listservers 23
• Print ads 21
• Word-of-mouth and e-mail 17
• Linked web advertisement 4

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Limitations of Search Engines

• Do not exploit hyperlinks - recently google
• search is limited to string matching
• key-world oriented search queries are evaluated
  on archived data rather than up-to-date data no
  indexing on current data
• low accuracy
• replicated results
• no further manipulation possible

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Continue .

• ERROR 404!
• No efficient document management
• Query results cannot be further manipulated
• No efficient means for knowledge discovery

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Current Research Projects

• Web Query System
• W3QS, WebSQL, AKIRA, NetQL, RAW,
• WebLog, XML-QL
• Semistructured Data
• LOREL, UnQL, WebOQL, Website Management System
• STRUDEL
• Web Warehouse
• - WHOWEDA

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WHOWEDA -Key Objectives

• Design a suitable data model to represent web
  information
• development of web algebra and query language
• Maintenance of Web data
• Development of knowledge discovery and web mining
  tools
• Web warehouse

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WHOWEDA - What?

• WareHouse Of Web Data
• Subject - oriented
• Integrated
• Temporal
• Granularity - Lower, higher
• Some summary
• Not updatable
• Alternative information sources

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Web Warehouse?

• Subject-oriented, integrated, time-variant,
  non-volatile repository of web data for direct
  querying and analysis for some sort of decision
  making
• A process whereby organizations or individuals
  extract value from their Web informational assets
  through the use of special stores called web
  warehouses

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WHOWEDA! www.cais.ntu.edu.sg8000/whoweda

• A WareHouse Of WEb DAta
• Web Information Coupling Model (WICM)
• Web Objects
• Web Schema
• Web Information Coupling Algebra
• Web Information Maintenance
• Web Mining and Knowledge discovery

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User
WWW
Warehouse Concept Mart
Web Querying Analysis Component
Web Information Coupling System
Web Information Maintenance System
Web Information Mining System
Web Mart
Web Mart
Web Warehouse
Web Mart
Web Mart
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User
WWW
Web Query Display
Warehouse Concept Mart
Global Web Manipulation
Global Web Coupling
Pre processing
Global Ranking
Data Visualization
Schema Tightness
Web Warehouse
Data Visualization
Web Union
Web Select
Web Intersection
Web Project
Local Web Manipulation
Local Web Coupling
Schema Tightness
Local Ranking
Schema Search
Web Join
Schema Match
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Web Objects

• Node - url, title, format, size, date, text
• Link - source-url, target-url, label, link-type
• Web tuple
• Web table
• Web schema
• Web database

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Web Schema

• Metadata in the warehouse
• Structural summary of web table
• Information Coupling using a Query graph
• Query graph -gtWeb schema
• directed graph represented by Ordered 4-tuple
• Set of node variables
• Set of link variables
• Connectivities
• Predicates

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url contains headlines
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Schema- example

• Node variables Xn x, y, z, w
• Link variable Xl e, f, g
• Connectivities C xltegty and xltfg-gtz and
  xltfh-gtw
• The symbol represents an unbound node variable
  or link variable a variable not restricted by
  any predicate.
• - represents one unbound links
• - represents more than one unbound links

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• Predicates
• Px.urlhttp//www.mediacity.com.sg/i-square,
• y.url CONTAINS headlines
• e.target_url CONTAINS "article",
• f.target.url CONTAINS "newshub/specials",
• g.label CONTAINS "Local News",
• z.url CONTAINS "local",
• h.label CONTAINS "World News",
• w.url CONTAINS "world"

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Query Graph - Example 1

• Query graph - same as schema except that it has
  one more parameter to control the results
  returned.
• Informally, it is directed connected graph
  consists of nodes, links and keywords imposed on
  them.
• Produce a list of diseases with their symptoms,
  evaluation procedures and treatment starting from
  the web site at http//www.panacea.org/
• Web table Diseases

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Treatment list
q
Treatment
g
http//www.panacea.org/
Issues
Symptoms list
f
y
x
z
Symptoms
List of Diseases
e
Evaluation
Evaluation
w
p
27
Treatment list
q1
g1
Treatment
http//www.panacea.org/
Issues
f1
Symptoms list
x0
z1
y1
Symptoms
AIDS
List of Diseases
e1
Evaluation
Evaluation
w1
p2
Elisa Test
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Example 2

• Produce a list of drugs, and their uses and side
  effects starting from the web site at
  http//www.panacea.org/
• Web table Drugs

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Side effects of Indavir
Drug list
http//www.panacea.org/
Issues
r1
AIDS
a0
b1
c1
d1
Indavir
Side effects
List of Diseases
Use
s1
k1
Uses of Indavir
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Query Language

• Starting from the CS dept. home page at NTU, find
  all documents that are linked through paths of
  length less than two containing only local links,
  and have in their text database.

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• COUPLE WEBTABLE W FROM WWW
• SUCH THAT NODE I, J IN WWW and LINK e,f,g IN WWW
  AND Iltef,ggtJ WHERE I.url EQUALS
  http//www.ntu.edu.sg AND J.text CONTAINS
  database AND f.link-type EQUALS local AND
  g.link-type EQUALS local

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Web Algebra

• Formal foundation of data representation and
  manipulation in a web warehouse
• Web operators
• Information access operator
• Information manipulation operators
• Web schema operators
• Data visualization operators

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Information access operator

• Global Web Coupling

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Information Manipulation

• - Web select
• Web project
• Local web coupling
• Web join
• Web cartesian product
• Web union
• Web intersect
• Local Web coupling

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Web Select

• Extracts web tuples from web tables satisfying
  certain conditions on node and link variables and
  on connectivities
• Input is select Schema
• Output is a web table satisfying the select schema

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• select W1 tuples that contain world news about
  Indonesia since May 1 1998.
• sMsW1 where
• Ms lt Xsn, Xsl, Cs, Ps gt,
• Xsn x, w , Xsl ,
• Cs ,
• Ps x.date gt "1May1998", w.text CONTAINS
  Indonesia

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• Xn x, y, z, w ,Xl e, f, g
• C xltegty and xltfg-gtz and xltfh-gtw
• Px.urlhttp//www.mediacity.com.sg/i-square,
  x.date gt "1May1998",
• e.target_url CONTAINS "article", f.target.url
  CONTAINS "newshub/specials",
• g.label CONTAINS "Local News",
• z.url CONTAINS "local",
• h.label CONTAINS "World News",
• w.url CONTAINS "world",
• w.text CONTAINS Indonesia

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Web Information Coupling System

• A database system to couple related web
  information
• Global web Coupling and Local Web Coupling

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Global Coupling - Information Access

• To integrate data from the Web
• To create historical data
• To couple related information from the WWW
  satisfying a query graph
• Operator to create web tables
• From web with no schema to web table with web
  schema

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Why local web coupling?

• Directly querying the WWW to gather these
  information is an expensive and repetitive affair
  
• Web documents containing similar information can
  reside in different web tables in a web warehouse
  
• A mechanism to gather these similar information
  by additional manipulation of the materialized
  web tables

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Local Web Couple operator

• Two web tuples and can be coupled if
  there exist atleast one pair of nodes from
  and which contains similar information.

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Local Web Couple operator

• The web couple operator is basically a web
  cartesian product followed by web select
• We denote web couple by the symbol

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Web Coupling
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Example 1

• Produce a list of diseases and their symptoms
  starting from the web site at http//www.panacea.o
  rg/
• Web table Diseases

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Issues
http//www.panacea.org/
symptoms
e
z
x
y
symptoms
List of Diseases
Web Schema or Query Graph of Diseases
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Web table Diseases
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Example 2

• Produce a list of drugs, and their side effects
  starting from the web site at http//www.panacea.o
  rg/
• Web table Drugs

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Drug list
Side effects
http//www.panacea.org/
Issues
r
c
a
b
d
Side effects
List of Diseases
Web Schema or Query Graph of Drugs
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Web table Drugs
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Issues
http//www.panacea.org/
Symptoms of AIDS
e0
AIDS
z0
x0
y0
symptoms
List of Diseases
Side effects of Ritonavir
Drug list
http//www.panacea.org/
Issues
r2
AIDS
a0
b1
c2
d2
Ritonavir
Side effects
Issues
http//www.panacea.org/
Symptoms of Cancer
e1
Cancer
z1
x0
y1
symptoms
List of Diseases
http//www.panacea.org/
Issues
Heart Disorder
r4
a0
b4
c4
d4
Side effects
Beta Carotene
Symptoms Side effects
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• M2 lt Xn, Xl, C,P gt for W2
• Xn s, t, u, Xl k, l, m, n ,
• C sltklgtt and sltmngtu ,
• Ps.url http//www.asia1.com.sg/straitstimes/,
  
• k.label REGION,
• l.target_url http//www.asia1.com.sg/straitstime
  s/pages/sea.html, m.label WORLD,
• n.target_urlhttp//www.asia1.com.sg/straitstimes
  /pages/wrld.html

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• W1 qq W2 where
• q (x.dates.date) (w.text CONTAINS
  Indonesia) (t.text CONTAINS Indonesia)
• Schema of the coupled table is

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• Xn x, y, z, w, s, t, u , Xl e, f,
  g, k, l, m, n , C xltegty and xltfg-gtz and
  xltfh-gtw and sltklgtt and sltmngtu
• P x.urlhttp//www.mediacity.com.sg/i-square
  , e.target_url CONTAINS "article",
• f.target.url CONTAINS "newshub/specials",
• g.label CONTAINS "Local News",
• z.url CONTAINS "local",
• h.label CONTAINS "World News",
• w.url CONTAINS "world",
• s.url http//www.asia1.com.sg/straitstimes/,

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• k.label REGION, l.target_url
  http//www.asia1.com.sg/straitstimes/pages/sea.h
  tml,
• m.label WORLD,
• n.target_url http//www.asia1.com.sg/straitstim
  es/pages/wrld.html,
• x.date s.date,
• w.text CONTAINS Indonesia,
• t.text CONTAINS Indonesia"

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Local Web Coupling

• Initiated explicitly by the user
• User provides the pair of node variables and the
  keyword set based on which coupling is to be
  performed
• Coupling nodes in each pair of web tuples in the
  input web tables must satisfy one of the coupling
  conditions

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Types of web coupling

• System driven web coupling system to decide the
  coupling nodes. If atleast a pair of coupling
  nodes cannot be identified then the web tables
  cannot be coupled.
• User driven web coupling user decides the
  coupling nodes.
• Coupling is performed only on those user
  specified node variable(s).

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Attribute driven web coupling

• Attribute driven web coupling user specifies the
  coupling attributes and coupling is performed
  only on those user specified coupling
  attribute(s).
• COUPLE TABLE3
• FROM TABLE1 AND TABLE 2
• ON ATTRIBUTE TEXT
• AT SCHEMA/TUPLE(optional)

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Value Driven web coupling

• Value driven web coupling user specifies the
  values of the attributes of the nodes on which
  coupling should be performed.
• COUPLE TABLE3
• FROM TABLE1 AND TABLE 2
• ON VALUE Software Agents
• AT SCHEMA/TUPLE(optional)

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Schema level web coupling

• We inspect the schemas to decide whether the two
  web tables can be coupled.
• If coupling conditions cannot be identified then
  the two web tables cannot be coupled.
• We do not inspect the web tuples in the web
  table.
• Number of web tuples coupled will be nm.

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Tuple level web coupling

• We inspect the web tuples of the two input web
  tables to identify nodes with similar
  information.
• The number of web tuples in the coupled web table
  ltnm

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Why two levels?

• A schema does not capture all the information of
  the web documents in a web table not always
  possible to identify coupling condition by
  inspecting the schemas.
• possible to find existence of coupling nodes
  which are not defined in the schemas.

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Why two levels?

• Tuple level coupling gives us a mean to correlate
  web documents containing similar information from
  the web tables (that cannot be identified from
  their schemas) at the expense of additional
  processing.

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Join Processing in Web Databases

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Web Join

• Concatenate tuples based on identical nodes or
  documents
• Input are two web tables and their schemas
• Output is a joined table
• Types
• Pi-web join, theta-web join, outer joins, web
  composition, semi web join

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Web Join

• Used for combining related data from various web
  tables
• Mechanism to detect changes
• Mechanism to find alternative web document in
  case of Document Not Found error

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Web Join Operator

• Information manipulation operator
• Manipulate information residing in a web database
  to derive additional information
• Harness useful, composite information from two
  web tables
• Capitalize on the reuse of retrieved data from
  the WWW in order to reduce execution time of
  queries

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Joinable Nodes

• Node variables participating in the web join
  process
• Expressed as a pair
• Each node in the pair should have identical URLs

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Web Join

• Combine two web tables by concatenating a web
  tuple of one web table with a web tuple of other
  web table whenever there exist joinable nodes
• Joinable nodes are identified from the schemas of
  the two web tables
• URLs of the joinable nodes are identical

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Treatment list
q
Treatment
g
http//www.panacea.org/
Symptoms list
Issues
List of Diseases
f
y
x
z
Symptoms
e
Evaluation
Evaluation
Drug list
w
p
Issues
r
Side effects
b
c
d
Side effects
Use
s
Uses
k
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AIDS treatment
q1
g1
Symptoms of AIDS
http//www.panacea.org/
f1
y1
x0
z1
AIDS
e1
AIDS
Evaluation
Elisa Test
w1
p2
r1
Side effects of Indavir
b1
c1
d1
Indavir
s1
Uses of Indavir
k1
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Pi-Web Join
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Example 1

• Produce a list of diseases with their symptoms,
  evaluation procedures and treatment starting from
  the web site at http//www.panacea.org/
• Web table Diseases

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http//www.panacea.org/
z
Query Graph (Web Schema) for Example 1
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Treatment list
q1
http//www.panacea.org/
Symptoms list
x0
z1
AIDS
List of Diseases
Evaluation
p2
Elisa Test
A web tuple in Diseases
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Example 2

• Produce a list of drugs, and their uses and side
  effects starting from the web site at
  http//www.panacea.org/
• Web table Drugs

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Query Graph (Web Schema) of Drugs
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A web tuple in Drugs
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Web Project

• Eliminate nodes from web tuples which are
  irrelevant
• Based on project conditions
• Set of node variables
• Start node variable and end-node variable
• Node variable and depth of links
• Used to isolate data of interest in a web table,
  allowing subsequent web queries to run over
  smaller, more structured web table

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http//www.panacea.org/
Symptoms list
x0
z1
AIDS
List of Diseases
Evaluation
p2
A web project on Diseases
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treatment
q
http//www.panacea.org/
z
x
symptoms
Disease List
p
evaluation
Side effects
Drug list
b
d
Joined schema
k
Uses
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Treatment list
q1
http//www.panacea.org/
Symptoms list
x0
z1
AIDS
List of Diseases
AIDS
Evaluation
p2
Side effects of Indavir
Drug list
Elisa Test
b1
d1
Indavir
Side effects
Use
k1
Uses of Indavir
Joined Tuple
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Motivation of Pi-web Join

• Quite often web join operation couples irrelevant
  nodes
• In a complex web query with several web join
  operation, the size of the resultant web table
  can become very large with many contaminated
  nodes
• Pi-web join resolves the above limitation by
  eliminating contaminated nodes
• Reduces the size of joined web table

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Pi-web Join

• Web join followed by web project
• The projection conditions are specified by the
  user conditions are similar to web project
• We do not eliminate the joinable nodes
• By retaining the joinable nodes we preserve the
  correlation between the information captured from
  two web tables
• Pi-web join may result in a web bag

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Example 3

• Produce a list of diseases with their symptoms
  and side-effects starting from the web site at
  http//www.panacea.org/

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Procedure

• Perform web join on Diseases and Drugs
• Project node variables b, k, q, p, node variables
  between a and q, node variables between b and k,
  node variables between b and d

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http//www.panacea.org/
z
x
symptoms
Disease List
Side effects
d
Pi-joined schema
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http//www.panacea.org/
Symptoms list
x0
z1
AIDS
List of Diseases
Side effects of Indavir
d1
Pi-joined Tuple
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Benefits of Pi-web Join

• Minimize the amount of data transmitted over the
  network in distributed web join processing
• Reduction in storage cost associated with a
  joined web table
• Reduces cognitive overhead associated with
  locating relevant nodes
• Improve completeness of schema by removing
  unbound nodes and links

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Web Bags

• Existence of identical web tuples.
• Created due to web project operation.
• Structure based mining
• Used for discovering
• Visible nodes
• Luminous nodes
• Luminous paths

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Definitions

• Visibility of a web document or node D in a web
  table W measures the number of different web
  documents in W that have links to D
• Luminosity - Reverse of visibility, the number of
  other distinct documents that are linked from D
• Luminous paths - a set of inter-linked nodes
  which occurs number of times in a web table

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Steps to find visible nodes

• Input Web table W, node variable x, visibility
  threshold v
• Output Set of visible nodes and inter-site
  support for each node
• Create a web table from W where each web tuple
  contains distinct instances of node x and the
  preceeding node which is linked to x (use project
  and create distinct tuples if node x has more
  than 1 incoming edge)
• Eliminate the nodes linked to x in each tuple of
  the web table using web project

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Continue...

• Check if the collection of web tuples of node x
  thus created is a web bag by comparing their URLs
• Create multiplets for each collection of
  identical nodes
• For each multiplet calculate the node visibility
  (using the mathematical formula defined, see
  FODO-98)
• Determine the multiplets with node visibility
  greater than the threshold
• Create the visible node set and calculte the
  inter-site support using the formula

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Steps to find luminous nodes

• Input Web table W, node variable x, luminosity
  threshold l
• Output Set of luminous nodes with inter-site
  support
• Steps are similar to that of visible node
  discovery
• We consider the nodes linked from x in place of
  nodes linked to x

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Steps to find luminous paths

• Input - web table W, nodes x and y
• Output - threshold value for luminous path
• Project nodes between x and y and check for web
  bag else go to next slide
• Create the collection of multiplets
• Compute path luminosity for each multiplet using
  the formula
• If the path luminosity value of a multiplet is
  greater than or equal to threshold then a path
  in the multiplet is a luminous path

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Steps to find luminous paths

• Otherwise, we create a collection of linear web
  tuples from the above collection of web tuples
• This is to identify if there exist a subset of
  inter-linked nodes between x and y that are
  luminous paths
• We repeat the procedure to compute path
  luminosity for these set of inter-linked nodes

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Web Schema
Cancer
http//www.panacea.org/
e
f
x
y
z
Cancer
Diseases
98
Cancer
http//www.panacea.org/
Diseases
f0
x0
y0
z1
Cancer
e0
http//www.cancer.org/desc.html
Cancer
Diseases
f0
z1
x0
y0
Cancer
e0
http//www.cancer.org/desc.html
Cancer
Diseases
f0
z2
x0
y0
Cancer
e0
Cancer
Diseases
f0
x0
y0
z1
Cancer
e0
http//www.cancer.org/desc.html
Cancer
Diseases
f0
z4
x0
y0
Cancer
e0
Web Table
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Projected schema
100
Cancer
Web Table after eliminating x and y
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Projected schema
Cancer
http//www.panacea.org/
e
z
x
y
Diseases
102
http//www.cancer.org/desc.html
http//www.cancer.org/desc.html
http//www.disease.com/cancer/skin.htm
http//www.cancer.org/desc.html
http//www.jhu.edu/medical/research/cancer.htm
http//www.panacea.org/
Diseases
Cancer
x0
y0
z4
Web Bag
103
After removal of identical tuples
http//www.cancer.org/desc.html
104
Cancer
z1
http//www.cancer.org/desc.html
Cancer
http//www.cancer.org/desc.html
z1
http//www.disease.com/cancer/skin.htm
http//www.cancer.org/desc.html
http//www.jhu.edu/medical/research/cancer.htm
105
http//www.cancer.org/desc.html
106
Visible Nodes
Cancer
http//www.cancer.org/desc.html
z1
Cancer
z2
http//www.disease.com/cancer/skin.htm
Cancer
z1
http//www.cancer.org/desc.html
Cancer
z4
http//www.jhu.edu/medical/research/cancer.htm
107
Luminous Paths
108
Change Management
109
More Operators . . .

• Web schema operators
• Schema tightness operator, Schema match operator,
  Schema search operator
• Data visualization operators
• Ranking operators (Global Local), Web Nest, Web
  Un-nest, Web Coalesce, Web Expand, Web Pack, Web
  Unpack, Web Sort

110
Partitioning of web tables

• Partitioning web tables
• restructured easily
• indexed easily
• monitored easily
• reorganized easily
• By
• time
• schema tree structure
• keywords

111
Warehouse Concept Mart (WCMart)

• Subject oriented
• Concept generation.
• Manually -gt Autonomous.
• Used for
• Ranking tuples
• Global web coupling
• Content based mining

112
Mining in Web Warehouse

• Web Structure Mining
• Web Content Mining
• Web usage Mining

113
Web Data Refinement

• Improve web schema - schema tightness operator
• Partition web tables based on content and
  structure

114
Partitioning of web tables

• Partitioning web tables
• restructured easily
• indexed easily
• monitored easily
• reorganized easily
• By
• time
• schema tree structure
• keywords

115
WWW
Warehouse Concept Mart
Global Web Coupling
Webtable (Jan)
Webtable (Feb)
Webtable (Mar)
Webtable (Apr)
116
Webtable (Jan)
Webtable (Feb)
Webtable (Mar)
Webtable (Apr)
Lower-level Granularity
Web Information Manipulation Operators
Higher level Granularity
Summarized data
117
User
WWW
Warehouse Concept Mart
Web Querying Analysis Component
Web Information Coupling System
Web Information Mining System
Web Warehouse
118
What type of information can be summarized?

• Structural
• Content-based
• time-variant analysis
• snapshot analysis
• compare one period with another
• trend analysis

119
Structural Summarization

• Most volatile documents
• Sites which change frequently
• Rate of change over time
• a pointer to directly access documents which
  change rapidly
• Most visible nodes, luminous nodes, luminous
  paths
• Change with time
• Decrease or increase - Analyze the reason

120
Content Summarization

• What can be aggregrated in a web page?
• Number of links with identical labels
• Number of keywords
• Changes in content with time
• Comparing the changes
• Open question
• XML will improve the ability of analysis of web
  data

121
Summary

• Current status
• Mechanism for accessing and manipulating web
  information in WHOWEDA
• Implementing various web operators and query
  language
• Future research
• What types of information can be summarized?
• What types of knowledge can be mined?
• Refine web warehouse architecture
• www.cais.ntu.edu.sg8000/whoweda