Information Resources Management

1
Information Resources Management

• April 24, 2001

2
Agenda

• Administrivia
• Object-Oriented Databases
• Data Warehousing
• Data Mining
• SQL Extensions
• XML

3
Administrivia

• Homework 8
• Homework 9
• Current Scores
• Final Review Session?

4
OODBMS vs. ORDBMS

• OODBMS - Object-Oriented
• ORDBMS - Object-Relational
• Appendix A

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OODBMS

• Persistent Objects
• By class
• By creation
• By marking
• By reference
• Storage/Retrieval Methods

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OODBMS - Benefits

• Match
• Programming
• Methodology
• Data types structures
• Ease of programming
• Inheritance

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OODBMS - Challenges

• Standards
• ODMG - Object Database Management Group
• Performance
• Database vs. persistent language
• Loss of integrity, queries
• Storage Space
• Maturity

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ORDBMS

• Extensions to relational model
• Complex data types
• Inheritance
• References
• Migration path
• Use existing applications and knowledge base

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ORDBMS - Benefits

• SQL
• Existing Systems
• Vendors

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ORDBMS - Challenges

• Standards
• Fit with the development language
• Programming Complexity

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Using a relational database to store data from an
object-oriented system has been likened to
parking your car in your garage. With an OODBMS
you park the car in the garage. If a (O)RDBMS is
used, to park your car in the garage, you must
first completely disassemble it and put each part
in its specific location on a shelf. This
process must then be reversed the next time you
want to go for a drive.
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OODBMS/ORDBMS Products
13
OODBMS/ORDBMS Products
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Other Links

• Object Database Management Group
• www.odmg.org
• Object Database Newsgroup
• comp.databases.object

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Data Mining

• Corporations have collosal amounts of data
• Usually only used for very specific purposes
  (operations)
• Automated attempt to learn from the data
• Find statistical rules and patterns in the data
• Example Giant Eagle Advantage Card

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Goals of Data Mining

• Explanatory - Why?
• Confirmatory - Is it?
• Exploratory - ???

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Approaches to Data Mining

• Classification
• identify rules that create groups
• Association
• find related conditions or events
• Correlation
• relationships between values

• User Guided
• hypothesis driven
• Automatic
• data driven - AI based

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Data Warehouse

• A subject-oriented, integrated, time-variant,
  nonvolatile collection of data
• Usually all data for a corporation
• Multidimensional database

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Data Warehousing

• Single location
• Long-term storage
• Greater availability
• Separate data processing from day-to-day
  operations (performance)
• All data is historical
• Support data mining, et al.

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Data Warehousing Questions

• What data needs to be kept?
• Where is it from?
• How good is it?
• How long should it be kept?
• Can it be summarized? When?
• Will it make sense? What is the schema?
• When is it updated?

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Data Warehousing - Benefits

• Support for decision making tools
• DSS, EIS, Data Mining
• Separation of information and day-to-day
  processing
• Unification - Centralization
• Improved quality and consistency

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Data Warehousing - Challenges

• Costs Storage, Setup, Maintenance
• Historical data issues
• Defining the warehouse schema
• Doing the conversion
• Implementation every time
• Keeping up with operational system changes
• Answering the questions

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Multidimensional Databases

• Two views
• Multidimensional tables
• Star schema
• Multidimensional table
• each cell is attribute
• dimensions are interesting categories

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Multidimensional Table

• Cell - sales
• Dimensions
• day
• person
• store
• item

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Star Schema

• Multiple tables
• Central table - data item (cell)
• Surrounding tables - information about each
  category (dimensions)

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Star Schema
Person
Day
Sales
Store
Item
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Star Schema

• Sales (Day, Person, Store, Item, sales)
• Day (Day, day info)
• Person (Person, person info)
• Store (Store, store info)
• Item (Item, item info)

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Building/Maintaining a Data Warehouse

• 1. Capture
• 2. Scrub
• 3. Transform
• 4. Load and Index

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Data Marts

• Making specific data available
• Different ones for different needs

DM1
DW
Operational Systems
DM2
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Data Mining

• Corporations have collosal amounts of data
• Usually only used for very specific purposes
  (operations)
• Automated attempt to learn from the data
• Find statistical rules and patterns in the data
• Example Giant Eagle Advantage Card

31
Goals of Data Mining

• Explanatory - Why?
• Confirmatory - Is it?
• Exploratory - ???

32
Approaches to Data Mining

• Classification
• identify rules that create groups
• Association
• find related conditions or events
• Correlation
• relationships between values

• User Guided
• hypothesis driven
• Automatic
• data driven - AI based

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Data Mining - Benefits

• Use data
• Learn new things
• Improve decision making

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Data Mining - Challenges

• Time (human and/or computer)
• Spurious results
• Separating the wheat from the chaff
• Availability of data
• Amount of data
• Changes in tools and technologies
• Validity over time

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Enhanced Data Analysis

• Beyond SUM, COUNT, and AVG
• SQL extensions (suggested)
• GROUP BY AS PERCENTILE
• Specific percentiles
• GROUP BY WITH CUBE
• Cross-tabulations
• Statistical package interface
• SAS, S, others

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Enhanced Data Analysis - Benefits

• Greater functionality
• Improved decision making

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Enhanced Data Analysis - Challenges

• Lack of standards
• Understandability
• Processing requirements
• Cost of poorly written queries
• ad hoc queries arent reviewed

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Extending Relational DBs

• Spatial and Geographic Databases
• Multimedia Databases
• Changing the data stored while retaining the
  benefits of relational databases

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Spatial Geographic DBs

• Spatial - CAD
• Geographic - GIS
• Similar issue
• How to store and retrieve such data

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Spatial Databases

• Geometric objects (2 or 3 dimensions)
• Locations
• Connections
• Nonspatial information about each object
• Substructures
• Spatial integrity constraints
• Two things cant occupy the same space

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GIS Databases

• Raster Data (fractal data)
• Pictures - possibly over time
• Maps
• Vector Data
• Locations
• Connections
• Nongeographic information

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Spatial Geographic DB -Benefits

• DBMS
• Specialized queries
• Spatial Geographic Data
• Standard Data
• Mix of the two
• Integrity constraints

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Spatial Geographic DB - Challenges

• Space requirements
• Level of detail
• Understandability - Complexity
• Processing requirements
• Compatibility between systems
• Lack of standards

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Multimedia Databases

• Images, Audio, Video
• Nonmultimedia data (text) about each
• Database Enhancements
• BLOBs (Binary Large Objects)
• Similarity-based queries
• Guaranteed steady rate
• Synchronization of audio and video

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Multimedia Databases - Benefits

• DBMS
• Greater compression may be possible
• Paperless office - document imaging
• Workflow redesign - improvements
• Greater availability

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Multimedia Databases - Challenges

• S T O R A G E
• Specialized DBMS
• Unity of database and network
• Usually requires ATM
• Specialized hardware
• juke boxes
• optical disks

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XML

• What is it?
• What isnt it?
• What are the goals?
• Who controls it?
• Whos using it?
• Beyond XML

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What is XML?

• eXtensible Markup Language
• Markup language for structured information
• structured - content role of that content
• markup - identify structures
• meta language for describing markup languages

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Huh?

• Storing structured data in a text file
• spreadsheet, address book, transactions (think
  EDI)
• Looks like HTML, lttagsgt, but isnt
• Text is universal, but not efficient
• Does disk space matter?
• What about network capacity?
• XML is license-free platform-independent

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What XML isnt

• HTML
• SGML - Standard Generalized Markup Language -
  printing
• Limited to current definitions (tags)
• XML is the way to add new definitions
• A relational database management system
• A database, or is it?

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Goals of XML

• Easy to use over Internet
• Wide variety of applications
• Compatible with SGML (subset)
• Easy to write programs that use XML documents
• No (or few) optional features
• Human-legible if necessary

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Goals of XML (2)

• Standards developed quickly
• Formal and concise
• Easy to create documents
• No need for shortcuts

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Who Controls XML?

• W3 Consortium
• www.w3.org/XML
• XML 1.0 specification

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Whos Using XML?

• Financial Products Markup Language
• FpML
• FpML.org
• A standard for financial derivatives
  business-to-business e-Commerce
• Others?

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Beyond XML

• Xlink - hyperlinks in XML
• XPointer Xfragments - point to parts of an XML
  document
• CSS - style sheet language
• XML and HTML
• XSL - advanced language for style sheets
• XSLT - XSL transformation language

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Beyond XML (2)

• DOM - standard function calls for manipulating
  XML (and HTML) from programs
• XML Namespaces - link a URL with every tag and
  attribute
• XML Schemas 1 2 - help in precisely developing
  own XML-based formats

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Homework 10

• Last One! (No HW 11)
• Research and evaluate products
• 100 points

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Final

• Next Tuesday, 5/1
• Approximately 1/3 from 4/3 - 4/24
• Remainder - comprehensive

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• Thank You