Transcript original - Kansas State University

Lecture 42 of 42
Data Mining, Information Retrieval and OLAP
Discussion: Term Projects
Thursday, 03 May 2007
William H. Hsu
Department of Computing and Information Sciences, KSU
KSOL course page: http://snipurl.com/va60
Course web site: http://www.kddresearch.org/Courses/Spring-2007/CIS560
Instructor home page: http://www.cis.ksu.edu/~bhsu
Reading for Next Class:
Chapter 18, Silberschatz et al., 5th edition
CIS 560: Database System Concepts
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Chapter 18: Data Analysis and Mining
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Decision Support Systems
Data Analysis and OLAP
Data Warehousing
Data Mining
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Decision Support Systems
 Decision-support systems are used to make business
decisions, often based on data collected by on-line transactionprocessing systems.
 Examples of business decisions:
 What items to stock?
 What insurance premium to change?
 To whom to send advertisements?
 Examples of data used for making decisions
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Retail sales transaction details
Customer profiles (income, age, gender, etc.)
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Cross Tabulation of sales by item-name
and color
 The table above is an example of a cross-tabulation (cross-tab),
also referred to as a pivot-table.
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Values for one of the dimension attributes form the row headers
Values for another dimension attribute form the column headers
Other dimension attributes are listed on top
Values in individual cells are (aggregates of) the values of the
dimension attributes that specify the cell.
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Data Cube
 A data cube is a multidimensional generalization of a cross-tab
 Can have n dimensions; we show 3 below
 Cross-tabs can be used as views on a data cube
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Online Analytical Processing
 Pivoting: changing the dimensions used in a cross-tab is called
 Slicing: creating a cross-tab for fixed values only
 Sometimes called dicing, particularly when values for multiple
dimensions are fixed.
 Rollup: moving from finer-granularity data to a coarser
granularity
 Drill down: The opposite operation - that of moving from
coarser-granularity data to finer-granularity data
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Hierarchies on Dimensions
 Hierarchy on dimension attributes: lets dimensions to be viewed
at different levels of detail
 E.g. the dimension DateTime can be used to aggregate by hour of
day, date, day of week, month, quarter or year
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Cross Tabulation With Hierarchy
 Cross-tabs can be easily extended to deal with hierarchies
 Can drill down or roll up on a hierarchy
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OLAP Implementation
 The earliest OLAP systems used multidimensional arrays in
memory to store data cubes, and are referred to as
multidimensional OLAP (MOLAP) systems.
 OLAP implementations using only relational database features are
called relational OLAP (ROLAP) systems
 Hybrid systems, which store some summaries in memory and
store the base data and other summaries in a relational database,
are called hybrid OLAP (HOLAP) systems.
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OLAP Implementation (Cont.)
 Early OLAP systems precomputed all possible aggregates in order
to provide online response
 Space and time requirements for doing so can be very high
 2n combinations of group by
 It suffices to precompute some aggregates, and compute others on
demand from one of the precomputed aggregates
 Can compute aggregate on (item-name, color) from an aggregate on (itemname, color, size)
 For all but a few “non-decomposable” aggregates such as median
 is cheaper than computing it from scratch
 Several optimizations available for computing multiple aggregates
 Can compute aggregate on (item-name, color) from an aggregate on
(item-name, color, size)
 Can compute aggregates on (item-name, color, size),
(item-name, color) and (item-name) using a single sorting
of the base data
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Extended Aggregation in SQL:1999
 The cube operation computes union of group by’s on every subset of
the specified attributes
 E.g. consider the query
select item-name, color, size, sum(number)
from sales
group by cube(item-name, color, size)
This computes the union of eight different groupings of the sales
relation:
{ (item-name, color, size), (item-name, color),
(item-name, size),
(color, size),
(item-name),
(color),
(size),
()}
where ( ) denotes an empty group by list.
 For each grouping, the result contains the null value
for attributes not present in the grouping.
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Extended Aggregation (Cont.)
 Relational representation of cross-tab that we saw earlier, but with
null in place of all, can be computed by
select item-name, color, sum(number)
from sales
group by cube(item-name, color)
 The function grouping() can be applied on an attribute
 Returns 1 if the value is a null value representing all, and returns 0 in all
other cases.
select item-name, color, size, sum(number),
grouping(item-name) as item-name-flag,
grouping(color) as color-flag,
grouping(size) as size-flag,
from sales
group by cube(item-name, color, size)
 Can use the function decode() in the select clause to replace
such nulls by a value such as all
 E.g. replace item-name in first query by
decode( grouping(item-name), 1, ‘all’, item-name)
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Extended Aggregation (Cont.)
 The rollup construct generates union on every prefix of specified
list of attributes
 E.g.
select item-name, color, size, sum(number)
from sales
group by rollup(item-name, color, size)
Generates union of four groupings:
{ (item-name, color, size), (item-name, color), (item-name), ( )
}
 Rollup can be used to generate aggregates at multiple levels of a
hierarchy.
 E.g., suppose table itemcategory(item-name, category) gives the
category of each item. Then
select category, item-name, sum(number)
from sales, itemcategory
where sales.item-name = itemcategory.item-name
group by rollup(category, item-name)
would give a hierarchical summary by item-name and by category.
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Extended Aggregation (Cont.)
 Multiple rollups and cubes can be used in a single group by clause
 Each generates set of group by lists, cross product of sets gives overall
set of group by lists
 E.g.,
select item-name, color, size, sum(number)
from sales
group by rollup(item-name), rollup(color, size)
generates the groupings
{item-name, ()} X {(color, size), (color), ()}
= { (item-name, color, size), (item-name, color), (item-name),
(color, size), (color), ( ) }
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Ranking
 Ranking is done in conjunction with an order by specification.
 Given a relation student-marks(student-id, marks) find the rank of
each student.
select student-id, rank( ) over (order by marks desc) as s-rank
from student-marks
 An extra order by clause is needed to get them in sorted order
select student-id, rank ( ) over (order by marks desc) as s-rank
from student-marks
order by s-rank
 Ranking may leave gaps: e.g. if 2 students have the same top mark,
both have rank 1, and the next rank is 3
 dense_rank does not leave gaps, so next dense rank would be 2
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Ranking (Cont.)
 Ranking can be done within partition of the data.
 “Find the rank of students within each section.”
select student-id, section,
rank ( ) over (partition by section order by marks desc)
as sec-rank
from student-marks, student-section
where student-marks.student-id = student-section.student-id
order by section, sec-rank
 Multiple rank clauses can occur in a single select clause
 Ranking is done after applying group by clause/aggregation
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Ranking (Cont.)
 Other ranking functions:
 percent_rank (within partition, if partitioning is done)
 cume_dist (cumulative distribution)
 fraction of tuples with preceding values
 row_number (non-deterministic in presence of duplicates)
 SQL:1999 permits the user to specify nulls first or nulls last
select student-id,
rank ( ) over (order by marks desc nulls last) as s-rank
from student-marks
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Ranking (Cont.)
 For a given constant n, the ranking the function ntile(n) takes the
tuples in each partition in the specified order, and divides them
into n buckets with equal numbers of tuples.
 E.g.:
select threetile, sum(salary)
from (
select salary, ntile(3) over (order by salary) as threetile
from employee) as s
group by threetile
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Windowing
 Used to smooth out random variations.
 E.g.: moving average: “Given sales values for each date, calculate
for each date the average of the sales on that day, the previous day,
and the next day”
 Window specification in SQL:
 Given relation sales(date, value)
select date, sum(value) over
(order by date between rows 1 preceding and 1
following)
from sales
 Examples of other window specifications:
 between rows unbounded preceding and current
 rows unbounded preceding
 range between 10 preceding and current row
 All rows with values between current row value –10 to current value
 range interval 10 day preceding
 Not including current row
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Windowing (Cont.)
 Can do windowing within partitions
 E.g. Given a relation transaction (account-number, date-time,
value), where value is positive for a deposit and negative for a
withdrawal
 “Find total balance of each account after each transaction on the
account”
select account-number, date-time,
sum (value ) over
(partition by account-number
order by date-time
rows unbounded preceding)
as balance
from transaction
order by account-number, date-time
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Data Warehousing
 Data sources often store only current data, not historical data
 Corporate decision making requires a unified view of all
organizational data, including historical data
 A data warehouse is a repository (archive) of information
gathered from multiple sources, stored under a unified schema, at
a single site
 Greatly simplifies querying, permits study of historical trends
 Shifts decision support query load away from transaction processing
systems
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Data Warehousing
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Design Issues
 When and how to gather data
 Source driven architecture: data sources transmit new information to
warehouse, either continuously or periodically (e.g. at night)
 Destination driven architecture: warehouse periodically requests new
information from data sources
 Keeping warehouse exactly synchronized with data sources (e.g.
using two-phase commit) is too expensive
 Usually OK to have slightly out-of-date data at warehouse
 Data/updates are periodically downloaded form online transaction
processing (OLTP) systems.
 What schema to use
 Schema integration
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More Warehouse Design Issues
 Data cleansing
 E.g. correct mistakes in addresses (misspellings, zip code errors)
 Merge address lists from different sources and purge duplicates
 How to propagate updates
 Warehouse schema may be a (materialized) view of schema from
data sources
 What data to summarize
 Raw data may be too large to store on-line
 Aggregate values (totals/subtotals) often suffice
 Queries on raw data can often be transformed by query optimizer to
use aggregate values
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Warehouse Schemas
 Dimension values are usually encoded using small integers and
mapped to full values via dimension tables
 Resultant schema is called a star schema
 More complicated schema structures
 Snowflake schema: multiple levels of dimension tables
 Constellation: multiple fact tables
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Data Warehouse Schema
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Data Mining
 Data mining is the process of semi-automatically analyzing large
databases to find useful patterns
 Prediction based on past history
 Predict if a credit card applicant poses a good credit risk, based on
some attributes (income, job type, age, ..) and past history
 Predict if a pattern of phone calling card usage is likely to be
fraudulent
 Some examples of prediction mechanisms:
 Classification
 Given a new item whose class is unknown, predict to which class it
belongs
 Regression formulae
 Given a set of mappings for an unknown function, predict the function
result for a new parameter value
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Data Mining (Cont.)
 Descriptive Patterns
 Associations
 Find books that are often bought by “similar” customers. If a new such
customer buys one such book, suggest the others too.
 Associations may be used as a first step in detecting causation
 E.g. association between exposure to chemical X and cancer,
 Clusters
 E.g. typhoid cases were clustered in an area surrounding a contaminated
well
 Detection of clusters remains important in detecting epidemics
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Classification Rules
 Classification rules help assign new objects to classes.
 E.g., given a new automobile insurance applicant, should he or she
be classified as low risk, medium risk or high risk?
 Classification rules for above example could use a variety of
data, such as educational level, salary, age, etc.
  person P, P.degree = masters and P.income > 75,000
 P.credit = excellent
  person P, P.degree = bachelors and
(P.income  25,000 and P.income  75,000)
 P.credit = good
 Rules are not necessarily exact: there may be some
misclassifications
 Classification rules can be shown compactly as a decision tree.
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Decision Tree
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Chapter 19: Information Retrieval
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Relevance Ranking Using Terms
Relevance Using Hyperlinks
Synonyms., Homonyms, and Ontologies
Indexing of Documents
Measuring Retrieval Effectiveness
Web Search Engines
Information Retrieval and Structured Data
Directories
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Information Retrieval Systems
 Information retrieval (IR) systems use a simpler data model
than database systems
 Information organized as a collection of documents
 Documents are unstructured, no schema
 Information retrieval locates relevant documents, on the basis of
user input such as keywords or example documents
 e.g., find documents containing the words “database systems”
 Can be used even on textual descriptions provided with nontextual data such as images
 Web search engines are the most familiar example of IR
systems
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Information Retrieval Systems (Cont.)
 Differences from database systems
 IR systems don’t deal with transactional updates (including
concurrency control and recovery)
 Database systems deal with structured data, with schemas that
define the data organization
 IR systems deal with some querying issues not generally addressed
by database systems
 Approximate searching by keywords
 Ranking of retrieved answers by estimated degree of relevance
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Keyword Search
 In full text retrieval, all the words in each document are considered to be
keywords.
 We use the word term to refer to the words in a document
 Information-retrieval systems typically allow query expressions formed
using keywords and the logical connectives and, or, and not
 Ands are implicit, even if not explicitly specified
 Ranking of documents on the basis of estimated relevance to a query is
critical
 Relevance ranking is based on factors such as
 Term frequency
 Frequency of occurrence of query keyword in document
 Inverse document frequency
 How many documents the query keyword occurs in
 Fewer  give more importance to keyword
 Hyperlinks to documents
 More links to a document  document is more important
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Relevance Ranking Using Terms
 TF-IDF (Term frequency/Inverse Document frequency) ranking:
 Let n(d) = number of terms in the document d
 n(d, t) = number of occurrences of term t in the document d.
 Relevance of a document d to a term t
TF (d, t) = log
n(d, t)
1+
n(d)
 The log factor is to avoid excessive weight to frequent terms
 Relevance of document to query Q
r (d, Q) =  TF (d, t)
tQ n(t)
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Relevance Ranking Using Terms (Cont.)
 Most systems add to the above model
 Words that occur in title, author list, section headings, etc. are given
greater importance
 Words whose first occurrence is late in the document are given lower
importance
 Very common words such as “a”, “an”, “the”, “it” etc are eliminated
 Called stop words
 Proximity: if keywords in query occur close together in the
document, the document has higher importance than if they occur far
apart
 Documents are returned in decreasing order of relevance score
 Usually only top few documents are returned, not all
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Similarity Based Retrieval
 Similarity based retrieval - retrieve documents similar to a given
document
 Similarity may be defined on the basis of common words
 E.g. find k terms in A with highest TF (d, t ) / n (t ) and use these terms to
find relevance of other documents.
 Relevance feedback: Similarity can be used to refine answer set
to keyword query
 User selects a few relevant documents from those retrieved by
keyword query, and system finds other documents similar to these
 Vector space model: define an n-dimensional space, where n is
the number of words in the document set.
 Vector for document d goes from origin to a point whose i th
coordinate is TF (d,t ) / n (t )
 The cosine of the angle between the vectors of two documents is
used as a measure of their similarity.
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Relevance Using Hyperlinks
 Number of documents relevant to a query can be enormous if only
term frequencies are taken into account
 Using term frequencies makes “spamming” easy
 E.g. a travel agency can add many occurrences of the words “travel” to its
page to make its rank very high
 Most of the time people are looking for pages from popular sites
 Idea: use popularity of Web site (e.g. how many people visit it) to
rank site pages that match given keywords
 Problem: hard to find actual popularity of site
 Solution: next slide
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Relevance Using Hyperlinks (Cont.)
 Solution: use number of hyperlinks to a site as a measure of the
popularity or prestige of the site
 Count only one hyperlink from each site (why? - see previous slide)
 Popularity measure is for site, not for individual page
 But, most hyperlinks are to root of site
 Also, concept of “site” difficult to define since a URL prefix like cs.yale.edu
contains many unrelated pages of varying popularity
 Refinements
 When computing prestige based on links to a site, give more weight to
links from sites that themselves have higher prestige
 Definition is circular
 Set up and solve system of simultaneous linear equations
 Above idea is basis of the Google PageRank ranking mechanism
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Relevance Using Hyperlinks (Cont.)
 Connections to social networking theories that ranked prestige of
people
 E.g. the president of the U.S.A has a high prestige since many people
know him
 Someone known by multiple prestigious people has high prestige
 Hub and authority based ranking
 A hub is a page that stores links to many pages (on a topic)
 An authority is a page that contains actual information on a topic
 Each page gets a hub prestige based on prestige of authorities that
it points to
 Each page gets an authority prestige based on prestige of hubs that
point to it
 Again, prestige definitions are cyclic, and can be got by
solving linear equations
 Use authority prestige when ranking answers to a query
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Synonyms and Homonyms
 Synonyms
 E.g. document: “motorcycle repair”, query: “motorcycle maintenance”
 need to realize that “maintenance” and “repair” are synonyms
 System can extend query as “motorcycle and (repair or maintenance)”
 Homonyms
 E.g. “object” has different meanings as noun/verb
 Can disambiguate meanings (to some extent) from the context
 Extending queries automatically using synonyms can be problematic
 Need to understand intended meaning in order to infer synonyms
 Or verify synonyms with user
 Synonyms may have other meanings as well
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Concept-Based Querying
 Approach
 For each word, determine the concept it represents from context
 Use one or more ontologies:
 Hierarchical structure showing relationship between concepts
 E.g.: the ISA relationship that we saw in the E-R model
 This approach can be used to standardize terminology in a
specific field
 Ontologies can link multiple languages
 Foundation of the Semantic Web (not covered here)
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Indexing of Documents
 An inverted index maps each keyword Ki to a set of documents
Si that contain the keyword
 Documents identified by identifiers
 Inverted index may record
 Keyword locations within document to allow proximity based ranking
 Counts of number of occurrences of keyword to compute TF
 and operation: Finds documents that contain all of K1, K2, ..., Kn.
 Intersection S1 S2 .....  Sn
 or operation: documents that contain at least one of K1, K2, …,
Kn
 union, S1 S2 .....  Sn,.
 Each Si is kept sorted to allow efficient intersection/union by
merging
 “not” can also be efficiently implemented by merging of sorted lists
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Measuring Retrieval Effectiveness
 Information-retrieval systems save space by using index
structures that support only approximate retrieval. May result
in:
 false negative (false drop) - some relevant documents may not
be retrieved.
 false positive - some irrelevant documents may be retrieved.
 For many applications a good index should not permit any false
drops, but may permit a few false positives.
 Relevant performance metrics:
 precision - what percentage of the retrieved documents are
relevant to the query.
 recall - what percentage of the documents relevant to the query
were retrieved.
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Measuring Retrieval Effectiveness (Cont.)
 Recall vs. precision tradeoff:
 Can increase recall by retrieving many documents (down to a low level of
relevance ranking), but many irrelevant documents would be fetched, reducing
precision
 Measures of retrieval effectiveness:
 Recall as a function of number of documents fetched, or
 Precision as a function of recall
 Equivalently, as a function of number of documents fetched
 E.g. “precision of 75% at recall of 50%, and 60% at a recall of 75%”
 Problem: which documents are actually relevant, and which are not
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Web Search Engines
 Web crawlers are programs that locate and gather information on
the Web
 Recursively follow hyperlinks present in known documents, to find
other documents
 Starting from a seed set of documents
 Fetched documents
 Handed over to an indexing system
 Can be discarded after indexing, or store as a cached copy
 Crawling the entire Web would take a very large amount of time
 Search engines typically cover only a part of the Web, not all of it
 Take months to perform a single crawl
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Web Crawling (Cont.)
 Crawling is done by multiple processes on multiple machines,
running in parallel
 Set of links to be crawled stored in a database
 New links found in crawled pages added to this set, to be crawled
later
 Indexing process also runs on multiple machines
 Creates a new copy of index instead of modifying old index
 Old index is used to answer queries
 After a crawl is “completed” new index becomes “old” index
 Multiple machines used to answer queries
 Indices may be kept in memory
 Queries may be routed to different machines for load balancing
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Information Retrieval and Structured
Data
 Information retrieval systems originally treated documents as a
collection of words
 Information extraction systems infer structure from documents,
e.g.:
 Extraction of house attributes (size, address, number of bedrooms,
etc.) from a text advertisement
 Extraction of topic and people named from a new article
 Relations or XML structures used to store extracted data
 System seeks connections among data to answer queries
 Question answering systems
CIS 560: Database System Concepts
Thursday, 03 May 2007
Computing & Information Sciences
Kansas State University
Directories
 Storing related documents together in a library facilitates
browsing
 users can see not only requested document but also related ones.
 Browsing is facilitated by classification system that organizes
logically related documents together.
 Organization is hierarchical: classification hierarchy
CIS 560: Database System Concepts
Thursday, 03 May 2007
Computing & Information Sciences
Kansas State University
A Classification Hierarchy For A Library System
CIS 560: Database System Concepts
Thursday, 03 May 2007
Computing & Information Sciences
Kansas State University
Classification DAG
 Documents can reside in multiple places in a hierarchy in an
information retrieval system, since physical location is not
important.
 Classification hierarchy is thus Directed Acyclic Graph (DAG)
CIS 560: Database System Concepts
Thursday, 03 May 2007
Computing & Information Sciences
Kansas State University
A Classification DAG For A Library
Information Retrieval System
CIS 560: Database System Concepts
Thursday, 03 May 2007
Computing & Information Sciences
Kansas State University
Web Directories
 A Web directory is just a classification directory on Web pages
 E.g. Yahoo! Directory, Open Directory project
 Issues:
 What should the directory hierarchy be?
 Given a document, which nodes of the directory are categories relevant to
the document
 Often done manually
 Classification of documents into a hierarchy may be done based on term
similarity
CIS 560: Database System Concepts
Thursday, 03 May 2007
Computing & Information Sciences
Kansas State University