kuliah 3 -data warehouse dan olap.pptx
TRANSCRIPT
Agenda
•Pengertian data warehouse•Model data multidimensi•Operasi-operasi dalam OLAP•Arsitektur data warehouse•Kegunaan data warehouse
Apa itu Data Warehousing?
• Data warehouse adalah koleksi dari data yang subject-oriented, terintegrasi, time-variant, 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, on-line
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 5-10 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 — Non-Volatile
• 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 (on-line 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 (on-line transaction processing)– Major task of traditional relational DBMS
– Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc.
• OLAP (on-line 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. read-only but complex queries
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 tabel-tabel dimensi terkait.
• n-D base cube dinamakan base cuboid. 0-D cuboid merupakan cuboid pada level paling tinggi, yang menampung ringkasan data dalan level paling tinggi, dinamakan apex cuboid. Lattice dari cuboid-cuboid 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
0-D(apex) cuboid
1-D cuboids
2-D cuboids
3-D cuboids
4-D(base) cuboid
Pemodelan Konseptual Data Warehouse
• Star schema: Sebuah tabel fakta di tengah-tengah dihubungkan dengan sekumpulan tabel-tabel dimensi.
• Snowflake schema: perbaikan dari skema star ketika hirarki dimensional dinormalisasi ke dalam sekumpulan tabel-tabel dimensi yang lebih kecil
• Fact constellations: Beberapa tabel fakta dihubungkan ke tabel-tabel dimensi yang sama, dipandang sebagai kumpulan dari skema star, sehingga dinamakan skema galaksi atau fact constellation.
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 hierarchyday < {month <
quarter; week} < year
• Set_grouping hierarchy{1..10} < inexpensive
Data Multidimensional
• Sales volume sebagai fungsi dari product, month, dan region
Pro
duct
Regio
n
Month
Dimension: Product, Location, TimeHierarchical summarization paths
Industry Region Year
Category Country Quarter
Product City Month Week
Office Day
Cuboid yang terkait dengan kubus
all
product date country
product,date product,country date, country
product, date, country
0-D(apex) cuboid
1-D cuboids
2-D cuboids
3-D(base) cuboid
Operasi-operasi OLAP
• Roll up (drill-up): summarize data
– by climbing up hierarchy or by dimension reduction
• Drill down (roll down): reverse of roll-up
– 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 back-end relational tables (using SQL)
Rancangan Data Warehouse: Business Analysis Framework
• Four views regarding the design of a data warehouse – Top-down 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 end-user
Proses Perancangan Data Warehouse
• Top-down, bottom-up approaches or a combination of both– Top-down: Starts with overall design and planning (mature)– Bottom-up: 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
Data Warehouse Back-End 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 corporate-wide 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 high-level corporate data model
Data Mart
Data Mart
Distributed Data Marts
Multi-Tier Data Warehouse
Enterprise Data Warehouse
Model refinementModel refinement
OLAP Server Architectures
• Relational OLAP (ROLAP) – Use relational or extended-relational 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) – Array-based multidimensional storage engine (sparse matrix
techniques)– fast indexing to pre-computed summarized data
• Hybrid OLAP (HOLAP)– User flexibility, e.g., low level: relational, high-level: 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, slice-dice, 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 On-Line 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
– OLAP-based exploratory data analysis
• mining with drilling, dicing, pivoting, etc.– On-line 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 cleaning
Data integration
Layer3
OLAP/OLAM
Layer2
MDDB
Layer1
Data Repository
Layer4
User Interface
Filtering&Integration Filtering
Databases
Mining query Mining result
Referensi
• Data Mining: Concepts and Techniques 3rd Ed Jiawei Han, Micheline Kamber and, Jian Pei 2012
• Introduction to Data Mining by Tan, Steinbach, Kumar, 2004