Presentation on theme: "CR32 Knowledge Management and Adaptive Systems 09: Data Warehousing based on an online presentation by Ronald J Norman"— Presentation transcript:
CR32 Knowledge Management and Adaptive Systems 09: Data Warehousing based on an online presentation by Ronald J Norman
2 by Professor Ronald J Norman of Grossmont College, CA, USARonald J Norman Prof Norman used these slides in his data mining course based on Data Mining Techniques (Second Edition) By Michael J. A. Berry and Gordon S. Linoff 2004 John Wiley & SonsMichael J. A. BerryGordon S. Linoff Management-oriented textbook...
3 Introduction Data, data, data…everywhere! Information…thats another story! Especially, the right the right time! Data warehousings goal is to make the right information the right time Data warehousing is a data store (eg., a filestore or database of some sort) and a process for bringing together disparate data from throughout an organization for decision-support purposes
4 Introduction Data warehouses are natural allies for data mining (work together well) Data mining can help fulfill some of the goal of data warehouses – right the right time Relational database management systems (RDBMS), such as Oracle, DB2, Sybase, Informix, Focus, SQL Server, etc. can be used for data warehousing; or just store as text/HTML
5 Definitions of a Data Warehouse - W.H. Inmon A subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process - Ralph Kimball A copy of transaction data, specifically structured for query and analysis 1. 2.
6 CW corpus as a Data Warehouse - W.H. Inmon Subject-oriented: English terminology on WWW Integrated: harvested from many sources, into a single standard format and file-store Time-variant : WWW pages change! Non-volatile : corpus is a static snap-shot - Ralph Kimball Copy of transaction data: cache structured for query and analysis: raw text yields word-frequency list 1. 2.
7 Data Warehouse For organizational learning to take place, data from many sources must be gathered together and organized in a consistent and useful way – hence, Data Warehousing (DW) DW allows an organization to archive snapshots of its data, and what it has noticed about its data Data Mining techniques make use of the data in a Data Warehouse
8 Data Warehouse Customers Etc… VendorsEtc… Orders Data Warehouse Enterprise Database Transactions Copied, organized summarized Data Mining Data Miners: Farmers – they know Explorers - unpredictable
9 Data Warehouse A data warehouse is a copy of transaction data specifically structured for querying, analysis, reporting, and more rigorous data mining Note that the data warehouse contains a copy of the transactions which are not updated or changed later by the transaction system Also note that this data is specially structured, and may have been transformed when it was copied into the data warehouse
10 Data Mart A Data Mart is a smaller, more focused Data Warehouse – a mini-warehouse. A Data Mart typically reflects the business rules of a specific business unit within an enterprise. Which English dominates the WWW, UK or US: each student captured a Data Mart for 1 domain.
11 Data Warehouse to Data Mart Data Warehouse Data Mart Decision Support Information Decision Support Information Decision Support Information
12 open source Data Warehouses A company may keep its DW private! Large data-sets are valuable Gold Standards for research and development Some Universities host public DWs Eg ICAME: International Computer Archive of Modern English ICAME also runs CORPORA forum Martin Krallinger etc on UK v US English: archive/2006-November/ html archive/2006-November/ html
13 Other Data repositories UPenn: Linguistic Data Consortium European equivalents: ELRA ELDA Leeds Electronic Text Centre Leeds Centre for Translation Studies
14 Generic Architecture of Data (synonym) Transaction data
15 Transaction (Operational) Data Operational (production) systems create (massive number of) transactions, such as sales, purchases, deposits, withdrawals, returns, refunds, phone calls, toll roads, web site hits, web site text, etc… Transactions are the base level of data – the raw material for understanding customer behavior Unfortunately, operational systems change, eg new formats, due to changing business needs Data warehousing strategies need to be aware of operational system changes
16 Operational Summary Data Summaries are for a specific time period and utilize the transaction data for that time period Other Examples???
17 Decision Support Summary Data The data that are used to help make decisions about the business –Financial Data, such as: Income Statements (Profit & Loss) Balance Sheets (Assets – Liabilities = Net Worth) –Sales summaries –Other examples??? Data warehouses maintain this type of data, however financial data of record (for audit purposes) usually comes from databases and not the data warehouse (confusing???) Generally, it is a bad idea to use the same system for analytic and operational purposes
18 Database Schema Database schema defines the structure of data, not the values of the data (e.g., first name, last name = structure; Ron Norman = values of the data) In RDBMS: –Columns = fields = attributes (A,B,C) –Rows = records = tuples (1-7)
19 Logical & Physical Database Schema Describes data in a way that is familiar to business users Describes the data the way it will be stored in an RDBMS which might be different than the way the logical shows it
20 Metadata General definition: Data about data !!! –Examples: A librarys card catalog (metadata) describes publications (data) A file system maintains permissions (metadata) about files (data) A form of system documentation including: –Values legally allowed in a field (e.g., AZ, CA, OR, UT, WA, etc.) –Description of the contents of each field (e.g., start date) –Date when data were loaded –Indication of currency of the data (last updated) –Mappings between systems (e.g., A.this = B.that) Invaluable, otherwise have to research to find it
21 Business Rules Highest level of abstraction from operational (transaction) data Describes why relationships exist and how they are applied Examples: –Need to have 3 forms of ID for credit –Only allow a maximum daily withdrawal of $200 –After the 3 rd log-in attempt, lock the log-in screen –Accept no bills larger than $20 –Others???
22 OLAP – Online Analytical Processing A definition: Data representation for ease of visualization OLAP goes beyond SQL with its analysis capabilities Key feature of OLAP: Relevant multi-dimensional views such as products, time, geography
23 OLAP Architecture
24 General Architecture for Data Warehousing Source systems Extraction, (Clean), Transformation, & Load (ETL) Central repository Metadata repository Data marts Operational feedback End users: analysis, OLAP, Data-Mining
25 CS490D25 DM vs. OLAP Data Mining: – can handle complex data types of the attributes and their aggregations – a more automated process Online Analytic Processing (visualization): –restricted to a small number of dimension and measure types –user-controlled process
26 CS490D26 DM + visualization Data Mining: – can handle complex data types of the attributes and their aggregations – reduces data to smaller number of patterns Visualization: –restricted to a small number of patterns –user-controlled process to select patterns which are interesting or useful
27 Q: Is it a Data Warehouse? Is ANY data-set a Data Warehouse? SIS? Library Catalogue? VLE? Text in a textbook?
28 Definitions of a Data Warehouse - W.H. Inmon A subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process - Ralph Kimball A copy of transaction data, specifically structured for query and analysis 1. 2.
29 CW corpus as a Data Warehouse - W.H. Inmon Subject-oriented: English terminology on WWW Integrated: harvested from many sources, into a single standard format and file-store Time-variant : WWW pages change! Non-volatile : corpus is a static snap-shot - Ralph Kimball Copy of transaction data: cache structured for query and analysis: raw text yields word-frequency list 1. 2.