Analyzing User Query Needs Chapter 6

Types of Users zExecutives zManagers zBusiness analysts

User Access Types of Users zExecutives zCasual users or managers zBusiness analysts or power users Structured Unstructured

Gathering User Requirements zAreas to focus: zHow users do business and what the business drivers are zWhat attributes users need (required versus good to have) zWhat are the business hierarchies are zWhat data users use and what they like to have zWhat levels of detail or summary needed zWhat types of front-end data access tool used zHow users expect to see the query results

Gathering User Requirements: Possible Obstacles The following are some of the possible obstacles: zBusiness objective of the data warehouse has not been specifically defined zScope of the data warehouse is too broad zMisunderstanding about the purpose and function of a decision support systems and operational systems

User Query Progression zStarts simple zBecomes more analytical zRequires different techniques and flexible tools What? Why?

Training zMethods - Informal: one-to-one or small class - Formal: larger class - Self-study zBasic topics - Logging on - Accessing metadata - Creating and submitting a query - Interpreting results - Saving queries and storing results - Utilizing resources - Learning warehouse fundamentals

Query Efficiency User considerations zSuccessful completion zFaster query execution zLess CPU used zMore opportunity for further analysis

Query Efficiency Designer considerations zUse indexes zSelect minimum data zEmploy resource governmors zMinimize bottlenecks zDevelop metrics zUser prepared and tested queries zUse quiet periods

Charge Models zExamples of charge models: - Flat allocation model - Transaction-based model - Telephone service model - Cable TV model zDevelop your own unique model zAvoid a charge model that discourages users from using the warehouse

Query Scheduling and Monitoring zQuery scheduling - Manages information usage - Directs queries - Executes queries - Sets job queue priorities zQuery monitoring - Track resource-intensive queries - Detect unused queries - Catch queries that use summary data inefficiently - Catch queries that perform regular summary calculations at the time of query execution - Detect illegal access

Query Management and Monitoring Tools zUse tools, schedulers, Oracle Enterprise Manager zConsider - Automation levels - Technology interfaces - Cost

Security zDo not overlook zSubject area sponsors: - Review and authorize request for access rights - Identify enhancements zTransparent security zEasy to implement, maintain, and manage

Security Plan zDefine a strategy: - Allocate business area owners - Ensure invisibility zEnsure easy management zConsider auditing zManage passwords

Role-Based Security zSubject area access: - Summary data for new users - All data for experienced users zDepartmental access zLimited object access zAccess during load

Application Context and Fine- Grained Access Control in Oracle8i Who am I? Where am I? Application context Access policy Table

Comparing OLAP and DSS zOLAP is used for multidimensional analysis. zDSS provides a system enabling decision making. zOLAP tools provide a DSS capability zOLAP for the warehouse provides analytical power. zOther terms: - EIS - KBS

The Functionality of OLAP zRotate and drill down to successive levels of detail. zCreate and examine calculated data interactively on large volumes of data. zDetermine comparative or relative differences. zPerform exception and trend analysis. zPerform advanced analytical functions for example forecasting, modeling, and regression analysis

Original OLAP Rules zMultidimensional conceptual view zTransparency zAccessibility zConsistent reporting performance zClient-server architecture

Original OLAP Rules zGeneric dimensionality zDynamic sparse matrix handling zMultiuser support zUnrestricted cross-dimensional operations zIntuitive data manipulation zFlexible reporting zUnlimited dimensions and aggregation levels

Relational Database Model Attribute 1 NameAgeGenderEmp No. Row 1 Row 2 Row 3 Row 4 The table above illustrates the employee relation.

Multidimensional Database Model SALES Customer Store Time Product FINANCE Store Time GL-Line The data is found at the intersection of dimensions.

Relational Server zBenefits: - Well-known environment with many experts in most organizations able to support the product - Can be used with data warehousing and operational systems - Many tools available with advanced features including improvements made to performance with report servers zDisadvantages: - Does not have any complex functions or analysi s capabilities provided by OLAP tools - These products may also be restricted to the volumes of data they can access

Multidimensional Server zBenefits: - Quick access to very large volumes of data - Extensive and comprehensive libraries of complex functions specifically for analysis - Strong modeling and forecasting capabilities - Can access multidimensional and relational database structures zDisadvantages: - Difficulty of changing dimensions without reaggregating to time - Lack of support for very large volumes of data