1. Decision Support System Course
Dr. Aref Rashad
Part:3
Data Component
February 2013
Decision Support Systems Course .. Dr. Aref Rashad

2. Components of a DSS February 2013
Decision Support Systems Course .. Dr. Aref Rashad

3. Values Matrix
help designers of DSS to know what information to include

4. Characteristics of Useful Information
• Timeliness
• Sufficiency
• Level of Detail and Aggregation
• Redundancy
• Understandability
• Freedom from Bias
• Reliability
• Decision Relevance
• Cost Efficiency
• Comparability
• Quantifiability
• Appropriateness of Format

5. Timeliness of Data
Timeliness addresses whether the information is available to the decision maker soon enough for it to be meaningful

6. Sufficiency Level of Detail Understandability
whether the data are adequate to support the decision under consideration.
Level of Detail
The aggregation level of the data is also an important factor for determining the usefulness of information in a DSS
Understandability
The key is to simplify the representation in the database without losing the meaning of the data.
February 2013
Decision Support Systems Course .. Dr. Aref Rashad

7. Freedom from Bias Decision Relevance Comparability
It is not appropriate for the designer to bias the analyses if it can be avoided. Bias can be caused by a wide variety of problems in the data, such as non representativeness with regard to time horizon, variables, comparability, or sampling procedures
Decision Relevance
Perhaps the most obvious issue to consider when building a database is the relevance of the information to the choices under consideration
Comparability
When deciding whether data are valuable, we need to assess whether they can be compared to other relevant data. Comparable means that, in important ways, measurement conditions have been held constant
February 2013
Decision Support Systems Course .. Dr. Aref Rashad

8. Reliability Redundancy Cost Efficiency
Decision makers will assume that the data are correct if they are included in the database; designers therefore need to ensure that they are accurate. They should verify the input of data and the integrity of the database
Redundancy
In a perfect world, the less information is repeated, the less storage is used. This goal is laudable because it should not limit the user's ability to link data from multiple sources.
Cost Efficiency
The benefit of improved decision-making capability must outweigh the cost of providing it or there is no advantage in the improvement. Said differently, data are only cost efficient in a database if there is positive value in the changed decision behavior associated with acting on the data in question after the cost of obtaining those data are subtracted.
February 2013
Decision Support Systems Course .. Dr. Aref Rashad

9. Appropriateness of Format
Quantifiability
Quantifiability does not assume that all valuable measures are quantified. Rather, it means the data are quantified at the appropriate level and that only appropriate operations can be performed on them. The level of quantification, referred to as the scale, dictates the types of meaningful mathematical operations that can be performed with the data.
Appropriateness of Format
The final determinant of the value of information is whether it is displayed in an appropriate fashion. This refers to the medium for their presentation, the ordering in which data are presented to the decision maker and the amount of graphics that are used.
February 2013
Decision Support Systems Course .. Dr. Aref Rashad

10. Data Sources Access needed to multiple sources Often enterprise-wide
Disparate and heterogeneous databases
XML becoming language standard
Web
Intelligent agents
Document management systems
Content management systems
Commercial databases
Sell access to specialized databases

12. Databases
These databases are collections of interrelated data. The goal behind the database concept is to store related data together in a format independent of the DSS
These data are linked together so that information from different physical locations on the storage medium can be joined together for transmission to the users‘ screens with a minimum amount of trouble.
February 2013
Decision Support Systems Course .. Dr. Aref Rashad

13. Database Management Systems
The DBMS serves as a buffer between the needs of the applications and the physical storage of the data. It captures and extracts data from the appropriate physical location and feeds it to the application program in the manner requested.
Software program
Supplements operating system
Manages data
Queries data and generates reports
Data security
Combines with modeling language for construction of DSS

14. Database Models Hierarchical Top down, like inverted tree
Fields have only one “parent”, each “parent” can have multiple “children”
Fast
Network
Relationships created through linked lists, using pointers
“Children” can have multiple “parents”
Greater flexibility, substantial overhead
Relational
Flat, two-dimensional tables with multiple access queries
Examines relations between multiple tables
Flexible, quick, and extendable with data independence
Object oriented
Data analyzed at conceptual level
Inheritance, abstraction, encapsulation

16. Enterprise Data Model

17. Data Warehouse A data warehouse is a database management system
Exists separate from the operations systems.
It is subject and time variant and integrated, as are the operational data.
It is nonvolatile and hence able to support a variety of analyses consistently
The difficult steps in building the data warehouse:
What data are relevant to particular decisions,
How the data should be represented and blended,
How to ensure they are meaningful, consistent, and accurate
The goal of the data warehouse is to bring together data from a variety of sources and merge it in a way to make it useful for decision makers.
February 2013
Decision Support Systems Course .. Dr. Aref Rashad

18. Data Warehouse Subject oriented
Scrubbed so that data from heterogeneous sources are standardized
Time series; no current status
Nonvolatile: Read only
Summarized
Not normalized; may be redundant
Data from both internal and external sources is present
Metadata included: Data about data
Business metadata
Semantic metadata

19. Process of Building a Data Warehouse

20. Data Scrubbing
The first step in building the data warehouse is to load data from the disparate data bases. The next step is to scrub or clean the data
• Eliminate problems of misspelling, transposition of letters, variations in spelling, and typographical errors.
Identify poorly documented data.
• Remove duplicate records
Remove obsolete data
Identify records not using corporate standards for coding
February 2013
Decision Support Systems Course .. Dr. Aref Rashad

21. Data Scrubbing Identify missing or inconsistent data.
Merge third-party information.
Remove spurious and invalid records
Enrich data with attributes .
Validate data (especially with external databases)
Identify and tag similar records suspected to be duplicates.
February 2013
Decision Support Systems Course .. Dr. Aref Rashad

22. Data Adjustment
In data warehouse we need to know not only the data at any given point in time but also the relative data at any given point in time.
Examples:
Currency ; needs to be consistent
Provision of additional dimensions to the data that might make analyses richer.
Time; needs to be included in the data warehouse
The goal of these adjustments is to provide the best picture of the organization; its customers, suppliers, and competitors; and as much other outside influences as possible so that the analyses are as reliable as possible.
February 2013
Decision Support Systems Course .. Dr. Aref Rashad

23. Architecture May have one or more tiers
Determined by Application Server, Data Server and client workstation.
One tier, where all run on same platform, is rare
Two tier usually combines Application Server, Data Server
Three tier separates these functional parts

26. Data Warehouse Tasks

27. Online Analytical Processing (OLAP)
Interactive analysis of data, allowing data to be summarized and viewed in different ways online
Data that can be modeled as dimension attributes and measure attributes are called multidimensional data.
Measure attributes
measure some value
can be aggregated upon
e.g. the attribute number of the sales
Dimension attributes
define the dimensions on which measure attributes (or aggregates thereof) are viewed
e.g. the attributes item_name, color, and size of the sales
February 2013
Decision Support Systems Course .. Dr. Aref Rashad

28. Store  Region  Country
Dimensions:
Time, Product, Store
Attributes:
Product (upc, price, …)
Store … …
Hierarchies:
Product  Brand  …
Day  Week  Quarter
Store  Region  Country
February 2013
Decision Support Systems Course .. Dr. Aref Rashad

29. Online Analytical Processing (OLAP)
Pivoting: changing the dimensions used in a cross-tabulation
Dicing: defining dimension increments
Slicing: creating a cross-tab for fixed values only
Rollup: moving from finer-granularity data to a coarser granularity
Drill down: The opposite operation - that of moving from coarser-granularity data to finer-granularity data
February 2013
Decision Support Systems Course .. Dr. Aref Rashad

30. February 2013
Decision Support Systems Course .. Dr. Aref Rashad

31. OLAP Implementation
OLAP implementations using only relational database features are called relational OLAP (ROLAP) systems
OLAP systems used multidimensional arrays in memory to store data cubes are referred to as multidimensional OLAP (MOLAP) systems.
Hybrid systems, which store some summaries in memory and store the base data and other summaries in a relational database, are called hybrid OLAP (HOLAP) systems.
February 2013
Decision Support Systems Course .. Dr. Aref Rashad

32. Star Schema (in RDBMS)

33. Star Schema Example

34. Star Schema with Sample Data

35. Points to be noticed about ROLAP
Defines complex, multi-dimensional data with simple model
Reduces the number of joins a query has to process
Allows the data warehouse to evolve with relatively low maintenance
Can contain both detailed and summarized data.
ROLAP is based on familiar, proven, and already selected technologies.

36. MOLAP: Dimensional Modeling Using the Multi Dimensional Model
MDDB: a special-purpose data model
Facts stored in multi-dimensional arrays
Dimensions used to index array
Sometimes on top of relational DB
Products
Pilot, Arbor Essbase, Gentia

37. MOLAP
February 2013
Decision Support Systems Course .. Dr. Aref Rashad

38. Data Cube Dimensions: Time, Product, Store Attributes:
Can have n dimensions;
Tables can be used as views on a data cube
roll-up to region
Dimensions:
Time, Product, Store
Attributes:
Product (upc, price, …)
Store … …
Hierarchies:
Product  Brand  …
Day  Week  Quarter
Store  Region  Country NY
Store
roll-up to brand SF LA
Juice
Milk
Coke
Cream
Soap
Bread 10 34 56 32 12
Product
roll-up to week
M T W Th F S S
Time
56 units of bread sold in LA on M

39. Dicing & slicing February 2013
Decision Support Systems Course .. Dr. Aref Rashad

40. Points to be noticed about MOLAP
Pre-calculating or pre-consolidating transactional data improves speed.
BUT
Fully pre-consolidating incoming data, MDDs require an enormous amount of overhead both in processing time and in storage. An input file of 200MB can easily expand to 5GB
MDDs are great candidates for the <50GB department data marts.
Rolling up and Drilling down through aggregate data.

41. HOLAP : Hybrid OLAP HOLAP = Hybrid OLAP: Best of both worlds
Storing detailed data in RDBMS
Storing aggregated data in MDBMS
User access via MOLAP tools

42. Data Flow in HOLAP RDBMS Server MDBMS Server Client
User
data
Meta data
Derived
MDBMS Server
Multi- dimensionaldata
Client
Multi-dimensional access
SQL-Read
Multidimensional Viewer
SQL-Read
SQL-Reach Through
Relational Viewer

43. When deciding which technology to go for, consider:
1) Performance:
How fast will the system appear to the end-user?
MDD server vendors believe this is a key point in their favor.
2) Data volume and scalability:
While MDD servers can handle up to 50GB of storage, RDBMS servers can handle hundreds of gigabytes and terabytes.

44. What-if analysis IF A. You require write access
B. Your data is under 50 GB
C. Your timetable to implement is days
D. Lowest level already aggregated
E. Data access on aggregated level
F. You’re developing a general-purpose application for inventory movement or assets management
THEN
Consider an MDD /MOLAP solution for your data mart
A. Your data is over 100 GB
B. You have a "read-only" requirement
C. Historical data at the lowest level of granularity
D. Detailed access, long-running queries
E. Data assigned to lowest level elements
Consider an RDBMS/ROLAP solution for your data mart.
A. OLAP on aggregated and detailed data
B. Different user groups
C. Ease of use and detailed data
Consider an HOLAP for your data mart

45. Examples ROLAP MOLAP HOLAP
Telecommunication startup: call data records (CDRs)
E-Commerce Site
Credit Card Company
MOLAP
Analysis and budgeting in a financial department
Sales analysis
HOLAP
Sales department of a multi-national company
Banks and Financial Service Providers

46. Tools available ROLAP: MOLAP: HOLAP: ORACLE 8i
ORACLE Reports; ORACLE Discoverer
ORACLE Warehouse Builder
Arbors Software’s Essbase
MOLAP:
ORACLE Express Server
ORACLE Express Clients (C/S and Web)
MicroStrategy’s DSS server
Platinum Technologies’ Plantinum InfoBeacon
HOLAP:
ORACLE Express Serve
ORACLE Relational Access Manager

47. Conclusion ROLAP: RDBMS -> star/snowflake schema
MOLAP: MDD -> Cube structures
ROLAP or MOLAP: Data models used play major role in performance differences
MOLAP: for summarized and relatively lesser volumes of data (10-50GB)
ROLAP: for detailed and larger volumes of data
Both storage methods have strengths and weaknesses
The choice is requirement specific, though currently data warehouses are predominantly built using RDBMSs/ROLAP.

48. Data Mining vs OLAP February 2013
Decision Support Systems Course .. Dr. Aref Rashad

49. Data Mining
Data mining is the process of semi-automatically analyzing large databases to find useful patterns
Prediction based on past history
Predict if a credit card applicant poses a good credit risk, based on some attributes (income, job type, age, ..) and past history
Predict if a pattern of phone calling card usage is likely to be fraudulent
Some examples of prediction mechanisms:
Classification
Given a new item whose class is unknown, predict to which class it belongs
Regression formulae
Given a set of mappings for an unknown function, predict the function result for a new parameter value

50. Data Mining Associations
Find books that are often bought by “similar” customers. If a new such customer buys one such book, suggest the others too.
Associations may be used as a first step in detecting causation
e.g. association between exposure to chemical X and cancer,
Clusters
e.g. typhoid cases were clustered in an area surrounding a contaminated well
Detection of clusters remains important in detecting epidemics

51. Other Types of Mining
Text mining: application of data mining to textual documents
cluster Web pages to find related pages
cluster pages a user has visited to organize their visit history
classify Web pages automatically into a Web directory
Data visualization systems help users examine large volumes of data and detect patterns visually
Can visually encode large amounts of information on a single screen
Humans are very good a detecting visual patterns

52. Data Mining Applications
Data mining application classes of problems
Classification
Clustering
Association
Sequencing
Regression
Forecasting
Hypothesis or discovery driven
……..

53. Tools and Techniques Data mining Text Mining Statistical methods
Decision trees
Case based reasoning
Neural computing
Intelligent agents
Genetic algorithms
Text Mining
Hidden content
Group by themes
Determine relationships