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A VVSG-derived model of election data

A VVSG-derived model of election data. David Flater National Institute of Standards and Technology. Outline. Brief overview of data model On the importance of a data model In a Common Data Format specification For interoperability and "integratability" Conclusion. The data model.

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A VVSG-derived model of election data

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  1. A VVSG-derived model of election data David Flater National Institute of Standards and Technology

  2. Outline Brief overview of data model On the importance of a data model In a Common Data Format specification For interoperability and "integratability" Conclusion

  3. The data model Expressed as a UML class diagram Scope covers election definition and vote data reporting requirements of VVSG 2.0, with the exception of reporting for ranked order contests Did not do registration, security, accessibility, etc. Key features: Clean expression of classes and associations (or entities and relationships, choose your jargon) Consistent use of defined terms Specified scope Free of implementation noise

  4. Why model To build a coherent standard, standards workers first need to get their own thoughts in order Implementation noise camouflages inconsistencies and design blunders Inconsistencies become apparent only when the model is made explicit Other people need to understand the concepts To maintain or extend a standard without ruining its conceptual integrity To use a standard as intended: Interoperability is harder than most people think

  5. Interoperability Ability to cooperate meaningfully for some purpose Customer does not have to make it work—it already works Conforming to an IT standard may facilitate interoperability, but it does not yield interoperability Not comparable to (e.g.) an electrical standard The ways in which interoperability can fail are without bound Works-first-time is a fluke Critical ingredients: Interoperability testing Will to succeed—the manufacturers make it work

  6. "Integratability" A lower standard than interoperability Quality that makes it easier to adapt systems or their exchanged data so that they will cooperate meaningfully for some purpose; e.g.: Compatible data models Ability to import and export data Robustness (no undocumented assumptions or requirements) Possible to make it work, but customer needs to hire consultants

  7. Poor integratability Incompatible factorings of the same conceptual domain—translation destructive, expensive, or impossible Conceptual scope conflict—bogus data added, valid data lost, required data not available Reference conflicts (different keys)—all links break Overlapping roles—both parties think they control X Fragile interface No interface

  8. Relevance of data model Preventing conflicts that break integratability requires diligence Combat the development of semantic conflicts by deliberately focusing on the conceptualization No data have intrinsic meaning (not even "semantic" data) The purpose of modelling is to communicate interpretations between people Communicate the conceptualization ("signal") with a minimum of syntactic overhead ("noise") Maintain conceptual integrity

  9. Conclusion Interoperability ← conceptual integrity ← data model There is no free lunch, no magic, no panacea DO IT RIGHT

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