lecture 42 of 42
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
Lecture 42 of 42
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
Data Mining, Information Retrieval and OLAPDiscussion: Term Projects
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
Chapter 18: Data Analysis and Mining Chapter 18: Data Analysis and Mining
Decision Support Systems Data Analysis and OLAP Data Warehousing Data Mining
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
Decision Support SystemsDecision Support Systems
Decision-support systems are used to make business decisions, often based on data collected by on-line transaction-processing 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 Retail sales transaction details Customer profiles (income, age, gender, etc.)
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
Cross Tabulation of sales by item-name and color
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. 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.
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
Data CubeData 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
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
Online Analytical ProcessingOnline 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
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
Hierarchies on DimensionsHierarchies 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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Thursday, 03 May 2007CIS 560: Database System Concepts
Cross Tabulation With HierarchyCross Tabulation With Hierarchy
Cross-tabs can be easily extended to deal with hierarchies Can drill down or roll up on a hierarchy
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
OLAP ImplementationOLAP 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.
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
OLAP Implementation (Cont.)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 aggregatesCan compute aggregate on (item-name, color) from an aggregate on (item-
name, 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
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
Extended Aggregation in SQL:1999Extended 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 salesgroup 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.
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
Extended Aggregation (Cont.)Extended Aggregation (Cont.) Relational representation of cross-tab that we saw earlier, but with null in
place of all, can be computed byselect item-name, color, sum(number)from salesgroup 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 salesgroup 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)
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
Extended Aggregation (Cont.)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 salesgroup 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.
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
Extended Aggregation (Cont.)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), ( ) }
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
RankingRanking 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-rankfrom 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-rankfrom 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
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
Ranking (Cont.)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-rankfrom student-marks, student-sectionwhere student-marks.student-id = student-section.student-idorder by section, sec-rank
Multiple rank clauses can occur in a single select clause Ranking is done after applying group by clause/aggregation
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
Ranking (Cont.)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-rankfrom student-marks
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
Ranking (Cont.)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 threetilefrom employee) as s
group by threetile
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
WindowingWindowing
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
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
Windowing (Cont.)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-timerows unbounded preceding)
as balancefrom transactionorder by account-number, date-time
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
Data WarehousingData 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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Thursday, 03 May 2007CIS 560: Database System Concepts
Data WarehousingData Warehousing
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Thursday, 03 May 2007CIS 560: Database System Concepts
Design IssuesDesign 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 expensiveUsually OK to have slightly out-of-date data at warehouseData/updates are periodically downloaded form online transaction
processing (OLTP) systems.
What schema to use Schema integration
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Thursday, 03 May 2007CIS 560: Database System Concepts
More Warehouse Design IssuesMore 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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Thursday, 03 May 2007CIS 560: Database System Concepts
Warehouse SchemasWarehouse 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 tablesConstellation: multiple fact tables
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Thursday, 03 May 2007CIS 560: Database System Concepts
Data Warehouse SchemaData Warehouse Schema
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Thursday, 03 May 2007CIS 560: Database System Concepts
Data MiningData 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 formulaeGiven a set of mappings for an unknown function, predict the function
result for a new parameter value
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Thursday, 03 May 2007CIS 560: Database System Concepts
Data Mining (Cont.)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 causationE.g. association between exposure to chemical X and cancer,
ClustersE.g. typhoid cases were clustered in an area surrounding a contaminated
wellDetection of clusters remains important in detecting epidemics
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Thursday, 03 May 2007CIS 560: Database System Concepts
Classification RulesClassification 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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Thursday, 03 May 2007CIS 560: Database System Concepts
Decision TreeDecision Tree
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Thursday, 03 May 2007CIS 560: Database System Concepts
Chapter 19: Information RetrievalChapter 19: Information Retrieval
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
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
Information Retrieval SystemsInformation 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 non-textual data such as images
Web search engines are the most familiar example of IR systems
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
Information Retrieval Systems (Cont.)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 systemsApproximate searching by keywordsRanking of retrieved answers by estimated degree of relevance
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Thursday, 03 May 2007CIS 560: Database System Concepts
Keyword SearchKeyword 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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Thursday, 03 May 2007CIS 560: Database System Concepts
Relevance Ranking Using TermsRelevance 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
The log factor is to avoid excessive weight to frequent terms
Relevance of document to query Q
nn((dd))
nn((dd, , tt))1 +1 +TF TF ((dd, , tt) = ) = loglog
r r ((dd, , QQ) =) = TF TF ((dd, , tt))nn((tt))ttQQ
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Thursday, 03 May 2007CIS 560: Database System Concepts
Relevance Ranking Using Terms (Cont.)
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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Thursday, 03 May 2007CIS 560: Database System Concepts
Similarity Based RetrievalSimilarity 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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Thursday, 03 May 2007CIS 560: Database System Concepts
Relevance Using HyperlinksRelevance 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” easyE.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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Thursday, 03 May 2007CIS 560: Database System Concepts
Relevance Using Hyperlinks (Cont.)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 siteAlso, 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 prestigeDefinition is circularSet up and solve system of simultaneous linear equations
Above idea is basis of the Google PageRank ranking mechanism
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Thursday, 03 May 2007CIS 560: Database System Concepts
Relevance Using Hyperlinks (Cont.)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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Thursday, 03 May 2007CIS 560: Database System Concepts
Synonyms and HomonymsSynonyms 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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Thursday, 03 May 2007CIS 560: Database System Concepts
Concept-Based QueryingConcept-Based Querying
Approach For each word, determine the concept it represents from context Use one or more ontologies:
Hierarchical structure showing relationship between conceptsE.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)
Computing & Information SciencesKansas State University
Thursday, 03 May 2007CIS 560: Database System Concepts
Indexing of DocumentsIndexing 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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Thursday, 03 May 2007CIS 560: Database System Concepts
Measuring Retrieval EffectivenessMeasuring 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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Thursday, 03 May 2007CIS 560: Database System Concepts
Measuring Retrieval Effectiveness (Cont.)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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Thursday, 03 May 2007CIS 560: Database System Concepts
Web Search EnginesWeb Search Engines
Web crawlers are programs that locate and gather information on the Web Recursively follow hyperlinks present in known documents, to find
other documentsStarting from a seed set of documents
Fetched documentsHanded over to an indexing systemCan 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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Thursday, 03 May 2007CIS 560: Database System Concepts
Web Crawling (Cont.)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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Thursday, 03 May 2007CIS 560: Database System Concepts
Information Retrieval and Structured Data
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
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Thursday, 03 May 2007CIS 560: Database System Concepts
DirectoriesDirectories
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
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Thursday, 03 May 2007CIS 560: Database System Concepts
A Classification Hierarchy For A Library SystemA Classification Hierarchy For A Library System
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Thursday, 03 May 2007CIS 560: Database System Concepts
Classification DAGClassification 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)
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Thursday, 03 May 2007CIS 560: Database System Concepts
A Classification DAG For A Library Information Retrieval System
A Classification DAG For A Library Information Retrieval System
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Thursday, 03 May 2007CIS 560: Database System Concepts
Web DirectoriesWeb 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 manuallyClassification of documents into a hierarchy may be done based on term
similarity