This is not a spectator event.  This is for people who want to co-invent the future. The Data-Centric systems that are being implemented today need more architectural support than currently exists.  Maybe you have part of the solution, maybe you need parts of the solution.  Either way, come to Fort Collins and contribute.

 This will be an intimate, interactive 2 ½ days.  We will have ten 90-minute sessions, each one focused on one of the twelve core problems to be solved.  Each 90-minute session will feature 2-3 practitioners (perhaps including you) describing how they have solved that problem, in a data-centric context, followed by Q&A.

Our theme for this event is “The Last Mile.” As attendees of the last three forums know, there are challenges in implementing a Data-Centric solution that sooner or later need to be addressed. We’ve identified twelve challenges that historically have been solved with application-specific code and will be covering the 10 that those attending are most interested in. The data-centric vision is to make applications thinner as more and more shared relationships and other common business logic ends up in the knowledge graph. The question is, how can we move more organizations up the maturity curve?

Many of these challenges have already been solved in graph environments. All could be improved and further integrated. The agenda is totally dedicated to upgrading our shared understanding of the state of the art on these twelve challenges in the Data-Centric / Knowledge Graph environment.  We will be selecting the top 10 of these 12 to focus on during the forum:

1. Authentication, Encryption and Personal Information (such as Solid)
2. Authorization – how to implement fine grained access to different parts of the graph
3. Entity Resolution – knowing whether two entities are the same and what to do about it
4. URI Resolution – how to implement smart “follow your nose” that is authorization aware and not its own application
5. Federation (especially to relational / big data) – consolidating polyglot data and integrating external data
6. Constraint Management – validation, back end, front end and sensible defaults
7. Model Driven UI – the data-centric no code / low code approach
8. Approaches to Graphical Visualization – for modelers, explorers, and end users
9. Integrating BI – getting analysts access to data in the graph, and approaches to aggregation and cubes
10. SemOps –speeding the time from ontology change to impact analysis and change
11. AI/ML and Data Science — How to make a Knowledge Graph that benefits Machine Learning and Data Science.
12. Unstructured Data– Integrating NLP, NLU, NLG and topic extraction

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