By Chuck Ballard; International Business Machines Corporation. International Technical Support Organization.; et al
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This textbook examines the constraint pride challenge (CSP), that is a normal challenge in AI functions. It goals to supply a finished creation to the CSP, masking theoretical, sensible and implementation concerns. The publication discusses formal definitions, CSP fixing algorithms and implementation of a few of the algorithms on PROLOG.
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You can begin to get a better appreciation for the impact of data modeling as you look closer at data mart consolidation. This initiative involves merging data (and the associated data models) from multiple data marts. And these data marts can exist on many heterogeneous platforms and reside in many different databases and data structures from many different vendors. As you look at the formats, data types, and data definitions from these heterogeneous environments, you quickly see the beginning of a complex task, and that is the integration of all these heterogeneous components and elements, along with all the associated applications and queries.
Planning: Key activities in the planning phase include: – – – – – Identifying business sponsor Identifying analytical structures to be consolidated Selecting the consolidation approach Defining the DMC project purpose and objectives Defining the scope Chapter 2. Business Intelligence: The destination 43 – Identifying risks, constraints, and concerns – In the planning phase above, based on the DMC Assessment Findings report, create the Implementation Recommendation report. Design: Key activities involved in this phase are: – – – – – – – Target EDW schema design Standardization of business rules and definitions Meta data standardization Identify dimensions and facts to be conformed Source to target mapping ETL design User reports Implementation: The implementation phase includes the following activities: – – – – – Target schema construction ETL process development Modifying or adding user reports Standardizing reporting environment Standardizing other BI tools Testing: This may include running in parallel with production.
Operational System Extract, Transform, and Load Operational Data Store ETL meta data meta data Enterprise Data Warehouse meta data Line of Business Data Marts Dependent Data Marts Independent Data Marts Figure 2-5 Data warehouse architecture with data marts As you can see, there are a number of options for architecting a data mart. For example: Data can come directly from one or more of the databases in the operational systems, with few or no changes to the data in format or structure. This limits the types and scope of analysis that can be performed.