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Goals


GoalAccomplished?
1Produce version of system for demonstration.
2

Define team GitHub process and educate the team.

Pushed to Sprint 3
3

Load data for the majority (80%) of nodes for one dog.

Changed to 50% on 2/12/19
4

Validate data loading by basic searching/visualizing of the data.


5

Integrate Pentaho into the transformation process (timeboxed as an experiment for this sprint).

Yes - Philip


Retrospective Results

AuthorCategoryCard Title
Philip MWhat WorkedThe idea of manually defined, static but real/realistic seed data being used to generate tsv files with which to pre-configure structure around studies, study arms, and corresponding cohorts. This has given us a robust, scalable mechanism via which to quickly create a realistic study landscape that we can then import subjects and their clinical data into. We should extend this approach in Sprint 3 and mechanize the associated generation of tsv files.
Philip MWhat WorkedYe being able to iterate the data model in real time during our initial data loads allowed us to immediately address gaps in properties associated with target nodes, and/or fully align field nomenclature before trying to load a given set of data. The pay off from Mark's extra effort on the Make Model tool was huge.
Matthew BWhat WorkedTeam continued great work while PM out on vacation.
Amit MWhat WorkedSeparation of file generation and load testing by different individuals.
Philip MWhat WorkedCollaboration and coordination around data loading was excellent.
Amit MWhat WorkedPlanning out the next set of events and identifying roles and responsibilities
Amit MWhat Didn't WorkMark being Mark!
Philip MWhat Didn't WorkUse of manually-contrived values for Submitter ID in all nodes downstream of Cycle, rather than IDs derived from actual data values using Pentaho-based transformations. They worked in terms of allowing data imports in the extreme short term, but simply don't represent a realistic, scalable solution.
Philip MSuggestions for ImprovementAs a team, we need to maintain better focus in our meetings.

Bugs


Future

  1. Take model and impose initial data constraints (add properties).
  2. Conduct dry-run of Demo.
  3. Conduct Demo.

Sprint 2

Key Summary T Created Updated Due Assignee Reporter P Status Resolution
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