Pengantar Data Warehouse dan OLAP
Pengantar Data Warehouse dan OLAP
Agenda
• 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 subjeksubjek 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, ukuranukuran 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
historical, summarized, multidimensional integrated, consolidated
usage repetitive adhoc access read/write
index/hash on prim. key lots of scans
unit of work short, simple transaction complex query # records accessed tens millions #users thousands hundreds DB size 100MBGB 100GBTB
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 item location supplier
time,item time,location
time,supplier
item,location
item,supplier
location,supplier
time,item,location
time,item,supplier
time,location,supplier
item,location,supplier
time, item, location, supplier
0D(apex) cuboid
1D cuboids
2D cuboids
3D cuboids
4D(base) cuboid
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_keydayday_of_the_weekmonthquarteryear
time
location_keystreetcityprovince_or_streetcountry
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_salesMeasures
item_keyitem_namebrandtypesupplier_type
item
branch_keybranch_namebranch_type
branch
Contoh skema Snowflake
time_keydayday_of_the_weekmonthquarteryear
time
location_keystreetcity_key
location
Sales Fact Table
time_key
item_key
branch_key
location_key
units_sold
dollars_sold
avg_salesMeasures
item_keyitem_namebrandtypesupplier_key
item
branch_keybranch_namebranch_type
branch
supplier_keysupplier_type
supplier
city_keycityprovince_or_streetcountry
city
Contoh Fact Constellation
time_keydayday_of_the_weekmonthquarteryear
time
location_keystreetcityprovince_or_streetcountry
location
Sales Fact Table
time_key
item_key branch_key
location_key
units_sold
dollars_sold
avg_salesMeasures
item_keyitem_namebrandtypesupplier_type
item
branch_keybranch_namebranch_type
branch
Shipping Fact Table
time_key
item_key shipper_key
from_location
to_location
dollars_cost
units_shipped
shipper_keyshipper_namelocation_keyshipper_type
shipper
Hirarki Konsep: Dimensi (Lokasi)
all
Europe North_America
MexicoCanadaSpainGermany
Vancouver
M. WindL. Chan
...
......
... ...
...
all
region
office
country
TorontoFrankfurtcity
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
Prod
uct
Region
Month
Dimension: Product, Location, TimeHierarchical summarization paths
Industry Region Year
Category Country Quarter
Product City Month Week
Office Day
Contoh Kubus Data
Total annual salesof TV in U.S.A.Date
Produc
t
Coun
trysum
sum TV
VCRPC
1Qtr 2Qtr 3Qtr 4QtrU.S.A
Canada
Mexico
sum
Cuboid yang terkait dengan kubus
all
product date country
product,date product,country date, country
product, date, country
0D(apex) cuboid
1D cuboids
2D cuboids
3D(base) cuboid
Browsing kubus data
• Visualization• OLAP capabilities• Interactive manipulation
Operasioperasi OLAP• Roll up (drillup): summarize data
– by climbing up hierarchy or by dimension reduction• 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 multidimensi.
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 ArchitectureMultiTiered Architecture
DataWarehouse
ExtractTransformLoadRefresh
OLAP Engine
AnalysisQueryReportsData mining
Monitor&
IntegratorMetadata
Data Sources FrontEnd Tools
Serve
Data Marts
Operational DBs
othersources
Data Storage
OLAP Server
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
Define a highlevel corporate data model
Data Mart
Data Mart
Distributed Data Marts
MultiTier Data Warehouse
Enterprise Data Warehouse
Model refinementModel refinement
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
Data Warehouse
Meta Data
MDDB
OLAMEngine
OLAPEngine
User GUI API
Data Cube API
Database API
Data cleaningData integration
Layer3OLAP/OLAM
Layer2MDDB
Layer1Data
Repository
Layer4User Interface
Filtering&Integration Filtering
Databases
Mining query Mining result
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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