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Knowledge Worker Framework Book
This material defines the knowledge worker and contrasts the knowledge worker characteristics to those of the process worker. The material provides nearly ten pages of reasons why large scale information systems fail. The material then introduces the Knowledge Worker Framework, defines its cells, outlines its meta-models, and the methodology required to implement a successful knowledge worker environment. The material concludes with a real example of a Knowledge Worker products that were developed in a court systems project.
The actual framework becomes a template for identifying and configuring knowledge worker projects such as information systems planning, database object modeling, functional/work-flow analysis and specification, and optimization of deployed human resources.
Enterprise Database Whitepaper
This material provides descriptions of the components required to achieve enterprise database. It serves as an in-depth management oriented primer and as an introductory set of materials for a number of other Whitemarsh materials.
Iterations of Database Design
This material presents step-wise refinement techniques for creating a quality database design. The material assumes that database designs are first hypothesized and then both iterated into higher quality forms and also bound to underlying DBMS and physical environments so they can be implemented quickly and easily with the highest return on investment possible.
Metabase Overview
This paper presents the rationale for and scope of the Whitemarsh Metadata database (metabase). The Whitemarsh Metabase has been implemented a number of times over the past 15 years. Not only has the Metabase project always been successful, it has always saved time, lowered risk, and increased quality. See here for this database design software tool.
WRAD Conference Talk
"Achieving Enterprise Wide Data Semantics Standardization in Support of Data Warehousing"
An essential component of enterprise-wide data warehousing is data standardization. Achieving data standardization requires an understanding of the various data architecture classes that exist within enterprise databases, the components of data semantics, data standardization work plans, and finally the required metadata repository meta-models that must be institutionalized.
DAMA Conference Talks
- DAMA 2000 - SQL 1999 Impact on Data and Database Administration
- DAMA 2001 - Data Standardization Talk
- DAMA 2001 - SQL 1999 Talk
- DAMA 2002 - Metadata Architecture for Enterprise Wide Data Sharing - Problem Specification
- DAMA 2003 - Metadata Architecture for Enterprise Wide Data Sharing - Problem Solution
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