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TEAM LEAD – ODS, Data Warehousing and Reporting
Consulting Research Scientist, OCLC Office of Research
Director, Dublin Core Metadata Initiative
This panel included an overview of the Dublin Core Metadata Initiative describing briefly what DC is, who uses it, and why. DC is an ISO standard (ISO 15836) for cross domain resource discovery that includes 15 basic elements and additional qualifiers. The DC architecture is founded on principles of modularity and extensibility, providing a basic set of descriptors that most information resources can share, and allowing extensions that can help tailor a metadata set to meet enterprise requirements.
DC is a formal part of the electronic information delivery strategies for 7 national governments (Australia, Denmark, Finland, Ireland, Canada, New Zealand, and the UK), and is used by museums, archives, libraries and a number of supra-governmental agencies as well ( eg. the EU, EEA, UNEP, FAO, and WHO).
Recently a working group has been formed to support the interests of commercial adopters as well. Further information can be found at:
Vision Accomplished: Metadata Repository to Single Source Directory
Data Adminstration Project Leader
Boston Federal Reserve Bank
This presentation described the Metadata Repository and Single Source Directory projects at the Federal Reserve Bank in Boston, which are combined under the Information Data Architecture initiative launched in October 2001. By April 2003 the Boston Metadata Repository has been in production for a year.
This presentation covered the story from selling the vision to management, to the design and implementation of the Repository and Single Source Directory. It included critical design elements, process steps, software and hardware choices, resource allocations, how training was managed, and how the deliverables were and continue to be user requirements driven.
Modeling Information Flow: Turning the Assembly Line into an InfoMatrix
Knowledge Integrity, Inc.
Most legacy information applications operate in an assembly-line fashion, creating “processed information” in isolated stages the same way that cars are manufactured. Factory-style processing creates artificial barriers to the effective sharing and use of information, leading to lost opportunities and decreased competitiveness.
The first step in turning this linear use of data into an information matrix is understanding how information objects flow through your processing. This presentation discussed an abstract representation of information flow, and then explored ways this model can be exploited when integrated with meta data, business rules, and business user expectations.
Attendee learned how the information flow model can be used to:
- Assess information compliance and data quality
- Determine critical informaiton processing stages
- Identify "Information Hubs"
- Build "virtual staging areas" for different database and business intelligence targets
The Associative Model of Data
Lazy Software, Ltd.
Today's standard database architecture, the Relational Model of data, is over thirty years old and suffers from some significant limitations, whilst object databases have failed to cross the chasm into mainstream use. The Associative Model of data is a viable and scalable alternative to both that overcomes two fundamental limitations of current programming practice: the need to write new programs for every new application, and the need to store identical types of information about each instance. It also offers a superior distributed data model, allowing one database to be readily distributed over many geographically dispersed servers. Moreover, associative databases may be readily tailored to serve different requirements simultaneously, and different databases may be instantly combined and correlated without extra programming.
Wednesday, April 30
2:45 pm – 3:45 pm
Data Management - Thinking Outside the Box
University of Melbourne
The speaker took a management perspective of "doing whatever it takes" to achieve data management goals - even if this means putting aside some of the key traditional techniques of data administration. Key ideas included progress through (short, measurable) projects, focusing on the "20% that delivers 80% of the value"; justifying data management in terms of results actually achieved rather than the "fear factor" of data mismanagement. In a wide-ranging talk, he also discussed the need to challenge business rules, and to collaborate with other areas of business and IT rather than working in isolation.
SPECIAL INTEREST GROUPS
Wednesday, April 30
5:30 pm – 6:30 pm
The Role of the Data Management Professional in Developing Business Process Strategy and Metrics
In this session, the data management professional learned that:
1) customer valuation is the key to a successful process strategy, and the key to the selection of the right metrics to measure performance against that strategy, and
2) we have confused the structure used for organizing people with the structure needed for organizing work.
The data management professional can play an equal role on the process improvement team and contribute to its success if:
1) he/she understands and overcomes the organizational barrier,
2) is willing to establish the data requirements for the process metrics, and develop a process-specific data mart and dashboard prototype to enable the process team to validate alternative process designs.
PANEL: Current Work of the Business Rules Group on Standards
Ronald G. Ross
|Compiled and edited by Tony Shaw, Program Chair, Wilshire Conferences, Inc||General Chair Yogendra K. Joshi Georgia Institute of Technology, usa program Chair Sandeep Tonapi Anveshak Technology and Knowledge Solutions, usa chair, Thermal Management|
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|Associated Press – 1/21 isu space food program closes Tony Pometto Faculty/research||Warren L g koontz (Professor and Program Chair)|
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