2024-07-15 - KDB Project Call Meeting Minutes

alvin-c-shih commented 1 month ago

Date

Monday, July 15, 2024 - 9:30am ET / 2:30pm UK

// Second Monday of every month

Attendees

Fullname	Affiliation	GitHub Username
Karl Moll	FINOS	@karlmoll
Peter Gyorok	KX Systems	@gyorokpeter
Ross Duffy	Morgan Stanley	@rdffy
Rian Ó Cuinneagáin	KX Systems	@rianoc
Gary Davies	Data Intellect	@drgdavies
Aaron Davies	Morgan Stanley	@adavies42
Jude Reilly	Data Intellect
Alvin Shih	Morgan Stanley	@alvin-c-shih

Meeting notices

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Agenda

(5 min) Convene, roll call, welcome new people.
[ ] Display FINOS Antitrust Policy summary slide
[ ] Review Meeting Notices (see above)
(5 min) Approve previous meeting minutes.
(35 min) Preview of Pythonic query API for PyKX and feedback.
- https://github.com/finos/kdb/issues/99 has QF fragment for comparison.
- Current PyKX throws them straight into q.
- Hard to pass in variables.
- New PyKX uses & as alternative to list syntax.
- Builds a QueryPhrase object.
- What about & operator in q?
  - kx.q._and - Can't use a Python keyword as a method name.
  - kx.q('&') also works.
- Users will be able to register functions.
- Also a kx.ParseTree class.
- Questions around syntax for common string operations.
- ssr each entry of a column?
  - iterator=kx.q.each
- String match also commonly needs an iterator:
  - c3~\:"foo"
- b-strings already taken to mean q strings in PyKX
  - can't be used as a shorthand for kcol wrapper
- Had to pause the conversation to leave time for next presentation.
(10 min) Demo of KDB.AI for lung images.
- https://dataintellect.com/blog/kdb-ai-a-breath-of-fresh-air/
- KDB.AI allows inserting chunks up to 10 MB.
- Classifying lung images using k Nearest Neighbors with k=9.
- Embeddings from RESNET 50.
- Nearest neighbors shown to user for transparency / explainability.
- PoC accuracy of 79.9%.
- Might be nice to show a confusion matrix to see if the false positives / negatives skew a certain way.
- Flat index is exhaustive search.
- Higher accuracy.
- HNSW (Hierarchical Navigable Small Worlds) would be better for larger data set.
(5 min) AOB, Q&A, Adjourn.