The document discusses managing metadata aggregation through linked open data (LOD). It outlines that current LOD projects only expose select data for experimentation and questions whether LOD can succeed on that basis alone without actual use. The document then proposes a workflow for managing metadata that involves obtaining data, storing it as statements in a cache rather than a database, evaluating and improving the data using specialized services, and publishing improved data in an ongoing and iterative process. It emphasizes developing automated and specialized services to continuously improve data quality and the use of a cache to manage metadata from multiple sources over time with detailed provenance.

1. The Other Side of Linked Data:
Managing Metadata Aggregation
ALCTS Metadata Interest Group
ALA Midwinter 2014

2. Where Are We Now?
• Major projects so far focused on exposing
selected portions of their data for
‘experimentation’
– Who’s using this data?
– Can LOD for libraries succeed on that basis?
• LOD is not just outputs, needs actual use to
inform practice
– A more complete view of the environment and
workflow should help

3. Outline
• Limitations of the traditional database strategy
– Including records, normalization, de-duplication, etc.
• Components of a fuller view
– Workflow
– Inputs, outputs
– Data cache and services
– Need for automated orchestration
– The maintenance conundrum

4. Substituting a Cache for a Database
• Supports multiple streams of data
• Allows detailed provenance to be carried over
time
• Separates services from data storage
• Allows more extensive automation (and
orchestration of services)
• Focuses valuable human effort where it’s
needed: analysis, design and implementation
of improvement services

5. Workflow
• Obtain data (possibly as ‘records’)
• Store data as statements in cache
• Evaluate data by source or collection
• Improve data using specific services, as
determined by evaluation
• Publish improved data
• [Rinse, repeat]

7. Yellow=Data we use now
Green=Data we’re adding

9. Yellow=Data we share now
Orange=Data we propose to share
Green=Data categories we can share

10. Developing and Defining Services
• Small single purpose services are easier to
develop and maintain
– What services you need are determined by goals,
evaluation results, etc.
– ‘Orchestration’ of services applies them to specific
kinds of data, in order
– Services can be described, and linked, to expose
who, what, when and how to downstream users

11. Developing Automated Interaction
• Rule: Use humans for things requiring human
understanding and decision making
– Use machines for everything else
– A manual process for something a machine can do as
well or better is a failure
• Improvement services can be granular, invoked in
prescribed order, and report results for later use
– Continuous improvement necessary to respond to
continuous change

13. Data Maintenance
• Improved data returns as statements to the data
cache, with provenance attached
• Statement strategy avoids overwriting of new data
over ‘improved’ data
• Each new statement adds to what is known about a
described resource
• Statements can be cherry picked and exposed to others in
statements or records, in ‘flavors’ or as a ‘everything we
have’

14. Contact
Information
Diane Hillmann
metadata.maven@gmail.com
Gordon Dunsire
gordon@gordondunsire.com
Jon Phipps
jonphipps@gmail.com
The First MetadataMobile