This report compiled and edited by Tony Shaw, Program Chair, Wilshire Conferences




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How to Talk Like an Oracle DBA


Roseanne Baker

Decision Support Analyst

Duke Energy Gas Transmission


This presentation described the Oracle database architecture: the physical files (datafiles), the memory structures (the instance) and the processes that monitor the database and write data from the memory structures into the physical files. The physical files were also associated with their logical counterparts, tablespaces. New Oracle features such as DataGuard, Range/Hash/List Partitioning, Materialized Views and Oracle Streams were also covered. In addition to these hard-core techie concepts, a series of "Smart DBA Rules", such as "When computing, whatever happens, behave as though you meant it to happen.", covered the lighter side of the DBA role.


TUTORIALS

Monday, April 28

8:30 am – 4:45 pm


Transforming Information Resource Management

for Business and Systems Effectiveness


Larry English

President

Information Impact International Inc.


In this Tutorial, Mr. English outlined the:

- Root causes for why IRM has NOT been implemented defectively. Business has been managed down the functions, and systems have been built according to the “systems” approach of automating isolated projects.

- The paradigm shift required to understand what and how to address effective Information Management as a business management tool. The Information-Age paradigm is one of collaborative work and value-chain (NOT data flow) management

- The principles of IRM. These have their counterparts to the principles used to manage financial, human, material and facility resources of the enterprise. With electronic information being the ONLY non-consumable enterprise resource, it is ludicrous and wasteful to redundantly store it when it could be shared from a single or controlled, replicated databases.

- The transformations to Business and Information Systems areas. Deming’s Quality Point 2, clearly describes that management MUST TRANSFORM itself to the paradigm and principles of quality management of information in order to achieve the benefits of IRM and IQ management.

- How to effect the Transformation. Culture change happens when information professionals have a business-centric vision of IRM that speaks to executives’ needs, they have a plan for the next steps the enterprise must make, and they quantify the costs of the status quo so that executives cannot sidestep IRM. PS: this requires information professions to transform themselves and be change agents.


Enterprise Physics 101


John Zachman

President

Zachman International


Enterprise Physics 101: In the Industrial Age, it was the products (airplanes, buildings, automobiles, computers, etc.) that were increasing in complexity and changing. We had to learn how to describe them (architecture) in order to create them and maintain them (that is, change them) over time. In the Information Age, it is the Enterprise that is increasing in complexity and changing. By the same token, we will have to learn how to describe them (architecture) in order to create them to accommodate dramatic increases in complexity and keep them relevant as they change over time. The Framework for Enterprise Architecture defines the set of descriptive representations that constitutes Enterprise Architecture and establishes a semantic structure to understand the physics of Enterprise engineering.


Data Resource Integration


Michael Brackett

Consulting Data Architect

Data Resource Design & Remodeling


Data integration is a major objective of many organizations. Resolving existing data disparity and creating an integrated data resource is a key strategy for improving data resource quality. However, there are three major problems with current data integration strategies. First, they do not stop the ongoing creation of disparate data before they begin integrating the existing disparate data. Second, they do no integrate all components of the data resource, including data descriptions, structure, integrity documentation, and data management practices. Third, they concentrate only on current problem areas and do not integrate the entire data resource.


This tutorial provided the basic concepts, principles, and techniques for stopping the creation of disparate data, resolving the existing disparate data, and creating a high-quality enterprise-wide data resource that is readily shared, including:

* The concept of a common data architecture for understanding and integrating data

* The concept of overall data resource quality

* Techniques for inventorying and understanding disparate data

* Techniques for transforming data


The Logical to Physical Data Model Transformation


Bill Smith

Founder and Principal Consultant

William G. Smith & Associates


This presentation described a step-by-step process for transforming a detailed (data element level of detail), properly normalized Logical Data Model (LDM) into a Physical Data Model (PDM). Two major data modeling steps/phases must have preceeded this transform: Conceptual Data Modeling, in which the entities and relationships of concern are carefully modeled according to well-defined rules which are unvarying; and Logical Data Modeling, in which detailed data elements are formed and defined to meet the detailed information requirements of the enterprise, and then properly normalized (using the unvarying "normal forms" as the rules) into the entity/relationship structure, resulting in a strictly and properly normalized Logical Data Model, ready for transformation into physical form for implementation.


In transforming an LDM into a PDM, the "rules" which must be followed are in fact defined by the business, and are the physical constraints (ie., speed of performance, space, cost, physical placement/distribution, security and integrity protection) which the business desires to be met. Unfortunately, typically, many of these constraints are mutually exclusive, ie., if the PDM satisfies one of them, then it cannot satisfy another. The very methodical, organized step-by-step process described in the tutorial showed how each of these considerations is analyzed and incorporated as the LDM is transformed into the PDM to arrive at a "best balanced" PDM which will optimally meet the best balance of the physical constraints imposed by the business.


Building a Business Intelligence Environment on a Shoestring


Claudia Imhoff

President

Intelligent Solutions, Inc.


Some companies can afford to spend hundreds of thousand or millions of dollars to build a data warehouse, while others cannot. There is hope even for companies who fall into the latter group. This session began with a brief description of a full-scale data warehouse architecture and the methodology required to implement it. That description provided the foundation for the remainder of the seminar. For companies with limited funding, the importance of an early hit is magnified so this seminar focused on alternative approaches for providing quick and relatively inexpensive BI deliverables.


Specific topics covered:

- Data Warehouse Architecture and Methodology – a brief review of a sustainable data warehouse architecture and the methodology required to implement it.

- Leadership and Scope – emphasis on the importance of the project sponsor for providing leadership, and of the scope document for establishing expectations. Desired and detrimental traits for the team leader were also discussed.

- Planning – description of the critical program oriented activities that ensured that the warehouse built with the limited resources can eventually fit within a sustainable architecture.

- Component Analysis – Review of the approach and methodology for building each of the architecture components – the data warehouse and data marts – with the compromises that could be made without destroying the infrastructure.


Enterprise Metadata Implementation: Learning from "Best Practices"


Todd Stephens

Director of the Metadata Services Group

BellSouth


Enterprise metadata and EAI may seem like a strange relationship but the reality is that metadata is one of the most critical elements of a solid application integration effort. Todd discussed and provided a three year learning curve for the attendies of this workshop. The session reviewed seven perspectives of an enterprise metadata effort.


- Enterprise metadata environment

- The architecture of an enterprise metadata effort

- The project and implementation side of an enterprise effort

- The importance of usability in metadata

- Technical architecture of the repository collection

- The principle of success around the service side of delivery

- Key leasons learned


Metadata and the principles that define this technology must be expanded into the other areas of the enterprise environment. Interfaces, components, schemas, DTD, web services, systems, documents, web pages, metrics as well as the components of the traditional database metadata effort needs to be looked at from a different view. The organization that plans on implementing an enterprise architectures needs to take a long look at the data architecture and ensure that it includes a heavy dose of metadata.


Fundamentals of Meta Data and Repository Management


David Marco

President

Enterprise Warehousing Solutions


Creating a meta data repository that is accessible and relevant to its users is essential to the success of any corporation. This tutorial based on corporate meta data implementations, provided attendees with a full life cycle strategy and methodology for defining meta data requirements, capturing/integrating meta data, and accessing the meta data repository. The metadata repository provides the developers and users with a road map to the rich, strategic information contained within an organization’s data warehouse and operational systems.


Among the challenges of implementing a meta data repository are:

* Defining technical & business meta data

* Selling the concept of building a meta data repository to management (ROI)

* Creating the meta data project plan

* Generic meta model presentation

* Generic meta model use cases

* Defining meta data requirements

* Identifying & integrating sources of meta data

* Constructing the meta data scope document

* Analyzing meta data tool vendors


Implementing a Message-Based Data Integration Strategy


Dave McComb

President

Semantic Arts


Simon Hoare

Director

Semantic Arts


The team from Semantic Arts led a highly interactive and content-laden discussion covering mechanics, strategy, methodology and approach to shifting your enterprise to a Service Oriented Architecture, and the implications that would have on Data Integration. Some of the key take aways were that in the future data is going to be far more replicated than it is now, and attempts to implement centralized enterprise databases are futile. Instead, the presenters outlined a strategy for managing the replication through publish and subscribe mechanisms over a message based architecture. Another key point was a future role for Data Administrators and Enterprise Message Modelers, essentially as keepers of the corporate message base.


Design of Reusable XML Component Schemas


James Bean

President and CEO

Global Web Architecture Group


The ability to describe enterprise data using XML and XML Schemas for the purposes of movement, exchange, sharing and collaboration is a key feature. However, XML and Reusable Component XML Schemas also provide a tremendous opportunity to extend and proliferate enterprise metadata standards. To accomplish this task, the metadata practitioner must become proficient with XML and XML Schemas syntax, formal reuse concepts and a robust process for schema design and engineering. The design process goes beyond traditional data modeling to delve into visualization, prototyping, abstraction, decomposition, modularity, and rationalization. As emphasis is placed on the importance of metadata, the data architecture practitioner must take a proactive role.


Modern Database Administration


Craig Mullins

Director, Technology Planning

BMC Software


The job of database administration is getting increasingly more difficult as database technology rapidly advances adding new functionality, more options, and more complex and complicated capabilities. This is a bigger problem than it at first appears to be because many IT professionals have no idea what DBAs actually do in the first place. At a high level, the job of the DBA can be summarized as that of keeping databases running up to PAR. In this context, PAR has dual meaning. The basic meaning for par (the word) is like it is used in golf: an amount taken as an average or norm. But for DBAs PAR can also be an acronym that defines the three primary responsibilities they have for managing databases: Performance, Administration, and Recovery. But to this basic definition we must append additional duties to support modern IT database systems. These duties include managing web-enabled databases, dealing with code in the database, managing multimedia and unstructured data, and working with databases for purchased applications (such as ERP and CRM). All of these factors conspire to make the DBA's job more difficult because they bring additional requirements (e.g. higher availability), new functionality (e.g. stored procedures), and different usages (e.g. the non-standard implementation of most packaged apps).


NIGHT SCHOOL

Monday, April 28

5:00 pm – 6:00 pm


Using UML for Data Modeling


Paul Dorsey

President

Dulcian, Inc.


Many people question whether any part of the Unified Modeling Language (UML) can be used for data modeling. Some have suggested creating a new tool to explicitly support data modeling. However, with some extensions, the UML can be used very effectively to design databases. This session provided an overview of UML class diagram syntax as it pertains to data modeling and a discussion of how each drawing element can be implemented in a relational database.


One of the most challenging problems in mapping an object-oriented design into a relational database is how to implement generalizations. The traditional mapping of each class to a table generates logically correct but unusable systems. Redundant storage of inherited attributes along the inheritance path is a strategy that allows modelers to use generalization without hesitation. The speaker also covered how logical Primary Key specification is still useful in class diagram data models and how the rules of normalization can be adapted to support object-oriented database design.

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