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A speci c modeling technique has evolved in order to support the types of queries and analyses that businesses require This technique is called dimensional modeling This approach has been applied to data warehousing for nearly thirty years and is supported by a wide variety of database platforms and data access or business intelligence tools Dimensional models support the business perspective of the data, and today s technology ensures that they can be effectively implemented Dimensional modeling is a formal data modeling technique that is used to organize and present data for analytical and reporting use The focus is on the business perspective and representation of data The goal is to free the data that has been captured and stored by the operational systems and make it available o the business community Regardless of how data is structured, business people will ask questions based upon their frame of reference This perspective is driven by the basic characteristics of the industry and how the company is organized, so why not organize the data to re ect this business perspective The two primary goals for dimensional modeling are ease of use and query performance These are the principles that guide the entire dimensional modeling process There are other data modeling techniques that play an important part in overall systems development They help ensure that the data itself and the relationships between different data elements are clearly de ned For operational systems, it is important that the data be organized to facilitate transaction processing This includes ensuring transaction integrity and speed The type of modeling used for operational system design is called entity-relationship (E-R) modeling This may also be referred to as normalized modeling One speci c form of E-R modeling represents the data in third normal form (3NF) There is a complete discipline surrounding this approach to data modeling This is mentioned to acknowledge the value and purpose of E-R modeling for operational system design The following two sections examine the main objectives of dimensional modeling.

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With respect to the enterprise, selecting protocols and then broadcasting the selected protocols to system developers accomplishes grid engineering However, grid engineering also entails the development and enforcement of an enterprise information assurance and security strategy Future web service protocols that directly address this topic may simplify this task Grid engineering also includes de ning the service layers that connect enterprise systems to the grid The attributes of a smart grid will not be attained through protocols alone Every system that participates in the enterprise must provide functional capability as structured layers of services Grid engineering can be practiced at one of four levels 1 Unmanaged, Ad Hoc Legacy applications and corresponding business intelligence are integrated into the enterprise locally on the y 2 Encapsulation and Objecti cation Legacy applications are adapted to a process of encapsulation and objecti cation A service-oriented adaptor stack provides the face to the enterprise This stack includes translation and security engines Modeling of information and process steps objecti es data and processes Objecti ed data is maintained locally to the application Con guration management is exerted locally mostly by pplication developers 3 Capture of Business Intelligence Business intelligence is captured in local applications and systems in a comprehensive fashion A local content repository is used to store and manage business intelligence Common local functions, such as troubleshooting and recovery, are selectively exported to the enterprise Con guration management is exercised at the system level by system integrators 4 Externalization of Business Intelligence Business intelligence is captured out of local applications and systems in reference to enterprise level models and forms and exported to an enterprise repository This enables asset mining by enterprise users Through enterprise modeling, all business intelligence is derived from comprehensive uni ed models, resulting in a model-driven architecture Con guration management is exercised at the enterprise level Process Engineering Process engineering is the practice of designing and documenting enterprise processes Done properly, it enables process improvement while maintaining enterprise operations At its most basic level, it involves development of the rules of interaction for the processes that comprise the enterprise and their enforcement These rules of interaction, which apply to processes as well as data, are similar to the business process rules of today, but only apply to process characteristics that impact the execution of other processes within the enterprise Rules that constrain the internal operations of a business process application are excluded from the category of rules of interaction Rules of interaction deal with formality in describing the outcomes of processes, not constraining how they work internally Their purpose is to provide a human and machine interpretable characterization of what the process does so that other process designers can accurately anticipate the result as they design their process.

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