Lecture 42 of 42

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



Chapter 18: Data Analysis and Mining Chapter 18: Data Analysis and Mining

Decision Support Systems Data Analysis and OLAP Data Warehousing Data Mining



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.)



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.



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



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



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



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



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.



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



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.



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)



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.



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), ( ) }



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



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




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




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



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



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



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



Data WarehousingData Warehousing



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



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



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



Data Warehouse SchemaData Warehouse Schema



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



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



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.



Decision TreeDecision Tree



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



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



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



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



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



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



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.



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



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



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



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



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)



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



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.



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



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



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



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



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



A Classification Hierarchy For A Library SystemA Classification Hierarchy For A Library System



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)



A Classification DAG For A Library Information Retrieval System

A Classification DAG For A Library Information Retrieval System



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

Lecture 42 of 42

Documents

Transcript of Lecture 42 of 42