Diapositiva 1

Data Wharehousing OLAP Data Wharehousing OLAP Data MiningData Mining

S. CostantiniS. CostantiniUniversità degli Studi di L’AquilaUniversità degli Studi di L’Aquila

[email protected]@di.univaq.it

S. Costantini / Data Wharehousing

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Ringraziamenti (Acknowledgment)

• Part of this material is taken from: Database Systems: The Complete Book, by Hector Garcia-Molina, Jeff Ullman, and Jennifer Widom, edited by Prentice-Hall.

• URL: http://www-db.stanford.edu/~ullman/dscb.html

http://www-db.stanford.edu/people/hector.html



http://www-db.stanford.edu/~ullman



http://www-db.stanford.edu/people/widom.html




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Cos’è in sostanza un Data Wharehouse?

• E’ una vista materializzata• Aggiornata a intervalli stabiliti (a

seconda dell’applicazione)• E’ un cosiddetto “sistema di

integrazione di dati” perché può contenere dati provenienti da vari database (detti “sorgenti”)


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Perché i Data Warehouse?

• Perché le query di analisi statistica ed esame dei dati per estrarne varie informazioni (dette query “OLAP”, vedi seguito) sono pesanti e diminuiscono troppo la performance del sistema. Però non necessitano della versione più aggiornata dei dati.


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Perché i Data Wharehouse

• Allora conviene separare le query usuali dalle query OLAP, creando per queste ultime un Data Wharehouse

• Per le query OLAP il modello relazionale non è ottimale, quindi nel creare un Data Wharehouse il modello dei dati viene modificato.


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Observation• Traditional database systems are tuned

to many, small, simple queries.• Some new applications use fewer, more

time-consuming, complex queries.• New architectures have been

developed to handle complex “analytic” queries efficiently.


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The Data Warehouse• The most common form of data

integration.– Copy sources into a single DB (warehouse)

and try to keep it up-to-date.– Usual method: periodic reconstruction of

the warehouse, perhaps overnight.– Frequently essential for analytic queries.


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OLTP• Most database operations involve On-

Line Transaction Processing (OTLP).– Short, simple, frequent queries and/or

modifications, each involving a small number of tuples.

– Examples: Answering queries from a Web interface, sales at cash registers, selling airline tickets.


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OLAP• Of increasing importance are On-Line

Application Processing (OLAP) queries.– Few, but complex queries --- may run for

hours.– Queries do not depend on having an

absolutely up-to-date database.


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OLAP Examples1. Amazon analyzes purchases by its

customers to come up with an individual screen with products of likely interest to the customer.

2. Analysts at Wal-Mart look for items with increasing sales in some region.


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Data Warehouses• Doing OLTP and OLAP in the same database

system is often impractical– Different performance requirements– Analysis queries require data from many sources

• Solution: Build a “data warehouse”– Copy data from various OLTP systems– Optimize data organization, system tuning for OLAP– Transactions aren’t slowed by big analysis queries– Periodically refresh the data in the warehouse


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Common Architecture• Relational Databases handle OLTP.• Local databases copied to a central

warehouse overnight.• Analysts use the warehouse for OLAP.


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Definition of data warehousing

A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s decision making process.


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Loading the Data Warehouse

Source Systems

Data Staging Area

Data Warehouse(OLTP)

Data is periodically extracted

Data is cleansed and transformed

Users query the data warehouse


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Data Mining• Data mining is a popular term for

queries that summarize big data sets in useful ways.

• Examples:1. Clustering all Web pages by topic.2. Finding characteristics of fraudulent

credit-card use.


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Data Warehouse

Customers

Etc…

Vendors Etc…

Orders

DataWarehouse

Enterprise“Database”

Transactions

Copied, organizedsummarized

Data Mining

Data Miners:• “Farmers” – they know• “Explorers” - unpredictable


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Market-Basket Data• An important form of mining from

relational data involves market baskets = sets of “items” that are purchased together as a customer leaves a store.

• Summary of basket data is frequent itemsets = sets of items that often appear together in baskets.


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Data Mining Flavors• Directed – Attempts to explain or

categorize some particular target field such as income or response.

• Undirected – Attempts to find patterns or similarities among groups of records without the use of a particular target field or collection of predefined classes.


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Data Mining Examples in Enterprises

• Government– Track down criminals (Police also)– Treasury Dept – suspicious int’l funds

transfer• Phone companies• Supermarkets & Superstores • Mail-Order, On-Line Order


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Data Mining Examples in Enterprises

• Financial Institutions • Insurance Companies • Web sites• Many others…

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