Data Warehouse

Pengantar
Data
Warehouse
dan OLAP
Agenda
•
•
•
•
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Pengertian data warehouse
Model data multidimensi
Operasi­operasi dalam OLAP
Arsitektur data warehouse
Kegunaan data warehouse
Apa itu Data Warehousing?
• Data warehouse adalah koleksi dari data yang subject­oriented, terintegrasi, time­variant, dan nonvolatile, dalam mendukung proses pembuatan keputusan.
• Sering diintegrasikan dengan berbagai sistem aplikasi untuk mendukung pemrosesan informasi dan analisis data dengan menyediakan platform untuk historical data.
• Data warehousing: proses konstruksi dan penggunaan data warehouse.
Data warehouse ­­ subject oriented
• Data warehouse diorganisasikan di seputar subjek­
subjek utama seperti customer, produk, sales.
• Fokus pada pemodelan dan analisis data untuk pembuatan keputusan, bukan pada operasi harian atau pemrosesan transaksi. • Menyediakan sebuah tinjauan sederhana dan ringkas seputar subjek tertentu dengan tidak mengikutsertakan data yang tidak berguna dalam proses pembuatan keputusan. Data warehouse ­­ terintegrasi
• Dikonstruksi dengan mengintegrasikan banyak sumber data yang heterogen. – relational database, flat file, on­line transaction record
• Teknik data cleaning dan data integration digunakan
– Untuk menjamin konsistensi dalam konvensi­
konvensi penamaan, struktur pengkodean, ukuran­
ukuran atribut dll diantara sumber data yang berbeda. • Contoh: Hotel price: currency, tax, breakfast covered, dll.
– Data dikonversi ketika dipindahkan ke warehouse.
Data Warehouse—Time Variant
• Data disimpan untuk menyediakan informasi dari perspektif historical, contoh 5­10 tahun yang lalu.
• Struktur kunci dalam data warehouse
– Mengandung sebuah elemen waktu, baik secara ekspisit atau secara implisit. – Tetapi kunci dari data operasional bisa mengandung elemen waktu atau tidak.
Data Warehouse — Non­Volatile
• Data warehouse adalah penyimpanan data yang terpisah secara fisik yang ditransformasikan dari lingkungan operasional.
• Data warehouse tidak memerlukan pemrosesan transaksi, recovery dan mekanisme kontrol konkurensi. • Biasanya hanya memerlukan dua operasi dalam pengaksesan data, yaitu initial loading of data dan access of data.
OLAP (on­line analitical processing)
• OLAP adalah operasi basis data untuk mendapatkan data dalam bentuk kesimpulan dengan menggunakan agregasi sebagai mekanisme utama. • Ada 3 tipe:
– Relational OLAP (ROLAP):
– Multidimensional OLAP (MOLAP) – Hybrid OLAP (HOLAP)  membagi data antara tabel relasional dan tempat penyimpanan khusus. Data Warehouse vs. Operational DBMS
• OLTP (on­line transaction processing)
– Major task of traditional relational DBMS
– Day­to­day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc.
• OLAP (on­line analytical processing)
– Major task of data warehouse system
– Data analysis and decision making
• Distinct features (OLTP vs. OLAP):
– User and system orientation: customer vs. market
– Data contents: current, detailed vs. historical, consolidated
– Database design: ER + application vs. star + subject
– View: current, local vs. evolutionary, integrated
– Access patterns: update vs. read­only but complex queries
OLTP vs. OLAP
OLTP OLAP users clerk, IT professional knowledge worker function day to day operations decision support DB design application­oriented subject­oriented data current, up­to­date detailed, flat relational isolated repetitive historical, summarized, multidimensional integrated, consolidated ad­hoc lots of scans unit of work read/write index/hash on prim. key short, simple transaction # records accessed tens millions #users thousands hundreds DB size 100MB­GB 100GB­TB usage access complex query Dari tabel dan spreadsheet ke Kubus Data
•
Data warehouse didasarkan pada model data multidimensional, dimana data dipandang dalam bentuk kubus data
•
Kubus data, seperti sales, memungkinkan data dipandang dan dimodelkan dalam banyak dimensi
– Tabel dimensi, seperti item (item_name, brand, type), or time(day, week, month, quarter, year) – Tabel fakta mengandung measures (seperti dollars_sold) dan merupakan kunci untuk setiap tabel­tabel dimensi terkait.
•
n­D base cube dinamakan base cuboid. 0­D cuboid merupakan cuboid pada level paling tinggi, yang menampung ringkasan data dalan level paling tinggi, dinamakan apex cuboid. Lattice dari cuboid­cuboid membentuk sebuah data cube.
Cube: A Lattice of Cuboids
all
time
time,item
0­D(apex) cuboid
item
time,location
location
item,location
time,supplier
time,item,location
supplier
location,supplier
item,supplier
time,location,supplier
time,item,supplier
1­D cuboids
2­D cuboids
3­D cuboids
item,location,supplier
4­D(base) cuboid
time, item, location, supplier
Pemodelan Konseptual Data Warehouse
• Star schema: Sebuah tabel fakta di tengah­tengah dihubungkan dengan sekumpulan tabel­tabel dimensi. • Snowflake schema: perbaikan dari skema star ketika hirarki dimensional dinormalisasi ke dalam sekumpulan tabel­tabel dimensi yang lebih kecil
• Fact constellations: Beberapa tabel fakta dihubungkan ke tabel­tabel dimensi yang sama, dipandang sebagai kumpulan dari skema star, sehingga dinamakan skema galaksi atau fact constellation. Contoh Skema Star
time
time_key
day
day_of_the_week
month
quarter
year
Sales Fact Table
time_key
item_key
branch_key
branch
branch_key
branch_name
branch_type
location_key
units_sold
dollars_sold
avg_sales
Measures
item
item_key
item_name
brand
type
supplier_type
location
location_key
street
city
province_or_street
country
Contoh skema Snowflake
time
time_key
day
day_of_the_week
month
quarter
year
item
Sales Fact Table
time_key
item_key
branch_key
branch
branch_key
branch_name
branch_type
location_key
units_sold
dollars_sold
avg_sales
Measures
item_key
item_name
brand
type
supplier_key
supplier
supplier_key
supplier_type
location
location_key
street
city_key
city
city_key
city
province_or_street
country
Contoh Fact Constellation
time
time_key
day
day_of_the_week
month
quarter
year
item
Sales Fact Table
time_key
item_key
item_key
item_name
brand
type
supplier_type
branch_key
branch_name
branch_type
item_key
shipper_key
location_key
location
to_location
units_sold
location_key
street
city
province_or_street
country
dollars_cost
dollars_sold
avg_sales
Measures
time_key
from_location
branch_key
branch
Shipping Fact Table
units_shipped
shipper
shipper_key
shipper_name
location_key
shipper_type
Hirarki Konsep: Dimensi (Lokasi)
all
all
Europe
region
country
city
office
Germany
Frankfurt
...
...
...
Spain
North_America
Canada
Vancouver ...
L. Chan
...
...
Mexico
Toronto
M. Wind
Tampilan datawarehouse dan hirarki
Specification of hierarchies
• Schema hierarchy
day < {month < quarter; week} < year
• Set_grouping hierarchy
{1..10} < inexpensive
Data Multidimensional
• Sales volume sebagai fungsi dari product, month, dan region
gi
on
Dimension: Product, Location, Time
Hierarchical summarization paths
Re
Industry Region Year
Product
Category Country Quarter
Product City Month Week
Office Day
Month
TV
PC
VCR
sum
1Qtr
2Qtr
Date
3Qtr
4Qtr
sum
Total annual sales
of TV in U.S.A.
U.S.A
Canada
Mexico
sum
Country
Pr
od
uc
t
Contoh Kubus Data
Cuboid yang terkait dengan kubus
all
product
product,date
date
0­D(apex) cuboid
country
product,country
1­D cuboids
date, country
2­D cuboids
product, date, country
3­D(base) cuboid
Browsing kubus data
• Visualization
• OLAP capabilities
• Interactive manipulation
Operasi­operasi OLAP
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Roll up (drill­up): summarize data
– by climbing up hierarchy or by dimension reduction
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Drill down (roll down): reverse of roll­up
– from higher level summary to lower level summary or detailed data, or introducing new dimensions
•
Slice and dice: – project and select •
Pivot (rotate): – reorient the cube, visualization, 3D to series of 2D planes.
•
Other operations
– drill across: involving (across) more than one fact table
– drill through: through the bottom level of the cube to its back­end relational tables (using SQL)
Ilustrasi
• Ilustrasi untuk operasi­operasi pada data multidimen
.
Rancangan Data Warehouse: Business Analysis Framework
• Four views regarding the design of a data warehouse – Top­down view
• allows selection of the relevant information necessary for the data warehouse
– Data source view
• exposes the information being captured, stored, and managed by operational systems
– Data warehouse view
• consists of fact tables and dimension tables
– Business query view • sees the perspectives of data in the warehouse from the view of end­user
Proses Perancangan Data Warehouse
• Top­down, bottom­up approaches or a combination of both
– Top­down: Starts with overall design and planning (mature)
– Bottom­up: Starts with experiments and prototypes (rapid)
• From software engineering point of view
– Waterfall: structured and systematic analysis at each step before proceeding to the next
– Spiral: rapid generation of increasingly functional systems, short turn around time, quick turn around
• Typical data warehouse design process
– Choose a business process to model, e.g., orders, invoices, etc.
– Choose the grain (atomic level of data) of the business process
– Choose the dimensions that will apply to each fact table record
– Choose the measure that will populate each fact table record
Multi­Tiered Architecture
other
source
s
Operational DBs
Metadata
Extract
Transform
Load
Refresh
Monitor
&
Integrator
Data
Warehouse
OLAP Server
Serve
Analysis
Query
Reports
Data mining
Data Marts
Data Sources
Data Storage
OLAP Engine Front­End Tools
Data Warehouse Back­End Tools and Utilities
• Data extraction:
– get data from multiple, heterogeneous, and external sources
• Data cleaning:
– detect errors in the data and rectify them when possible
• Data transformation:
– convert data from legacy or host format to warehouse format
• Load:
– sort, summarize, consolidate, compute views, check integrity, and build indicies and partitions
• Refresh
– propagate the updates from the data sources to the warehouse
Three Data Warehouse Models
• Enterprise warehouse
– collects all of the information about subjects spanning the entire organization
• Data Mart
– a subset of corporate­wide data that is of value to a specific groups of users. Its scope is confined to specific, selected groups, such as marketing data mart
• Independent vs. dependent (directly from warehouse) data mart
• Virtual warehouse
– A set of views over operational databases
– Only some of the possible summary views may be materialized
Data Warehouse Development: A Recommended Approach
Multi­Tier Data Warehouse
Distributed Data Marts
Data Mart
Data Mart
Model refinement
Enterprise Data Warehouse
Model refinement
Define a high­level corporate data model
OLAP Server Architectures
• Relational OLAP (ROLAP) – Use relational or extended­relational DBMS to store and manage warehouse data and OLAP middle ware to support missing pieces
– Include optimization of DBMS backend, implementation of aggregation navigation logic, and additional tools and services
– greater scalability
• Multidimensional OLAP (MOLAP) – Array­based multidimensional storage engine (sparse matrix techniques)
– fast indexing to pre­computed summarized data
• Hybrid OLAP (HOLAP)
– User flexibility, e.g., low level: relational, high­level: array
• Specialized SQL servers
– specialized support for SQL queries over star/snowflake schemas
Data Warehouse Usage
• Three kinds of data warehouse applications
– Information processing
• supports querying, basic statistical analysis, and reporting using crosstabs, tables, charts and graphs
– Analytical processing
• multidimensional analysis of data warehouse data
• supports basic OLAP operations, slice­dice, drilling, pivoting
– Data mining
• knowledge discovery from hidden patterns • supports associations, constructing analytical models, performing classification and prediction, and presenting the mining results using visualization tools.
• Differences among the three tasks
From On­Line Analytical Processing to On Line Analytical Mining (OLAM)
• Why online analytical mining?
– High quality of data in data warehouses
• DW contains integrated, consistent, cleaned data
– Available information processing structure surrounding data warehouses
• ODBC, OLEDB, Web accessing, service facilities, reporting and OLAP tools
– OLAP­based exploratory data analysis
• mining with drilling, dicing, pivoting, etc.
– On­line selection of data mining functions
• integration and swapping of multiple mining functions, algorithms, and tasks.
• Architecture of OLAM
An OLAM Architecture
Mining query
Mining result
User Interface
User GUI API
OLAM
Engine
Layer4
OLAP
Engine
Layer3
OLAP/OLAM
Data Cube API
Layer2
MDDB
Filtering&Integration
Databases
Database API
Meta Data
Filtering
Data Data integration Warehouse
Data cleaning
MDDB
Layer1
Data Repository
Referensi
• Data Mining: Concepts and Techniques by Jiawei Han and Micheline Kamber, 2001
• Introduction to Data Mining by Tan, Steinbach, Kumar, 2004
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