1. FROM ROOTS TO FRUITS: EXPLORING
LINEAGE FOR DATASET
RECOMMENDATIONS
Ted Dunning, Fellow, HPE
18 June, 2023

2. the meaning of words lies in their use
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3. the meaning of words lies in their use
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the meaning of data lies in its use
(apologies to Dr. Wittgenstein)

4. 4
A meteorologist’s data
- rainfall
- windspeed
- temperature

5. 5
A meteorologist’s data
- rainfall
- windspeed
- temperature
A business uses the
data to predict umbrella
sales

6. 6
What does the data
actually mean?

7. 7
What does the data
actually mean?
the meaning of data lies in its use

8. TRAINING PROCESS
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README
URL
History
Datasets
+
Models
Metadata
We start with explicit metadata.
Examples: column and table
names, documentation, common
values, and others

9. TRAINING PROCESS
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README
URL
History
Datasets
+
Models
Metadata
This is encoded as a large
artifact x characters
incidence table
At this point, direct metadata
search is possible

10. TRAINING PROCESS
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README
URL
History
Datasets
+
Models
Metadata
We augment with
metadata from all
ancestors and
descendants in
the global data
lineage graph

11. TRAINING PROCESS
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README
URL
History
Datasets
+
Models
Metadata
Finally, we reduce the characteristic
cooccurrences using indicator-based
recommendation methods.

12. A NOTE ON IMPLICATIONS
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The characteristic indicator
matrix is what connects
“umbrella” with “rainfall” or
“mosquito” with
“temperature” + “windspeed”

13. QUERY PROCESS
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The original query is often
textual, possibly a README

14. QUERY PROCESS
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augmented by recent project
behavior (queries, references)

15. QUERY PROCESS
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The query is expanded based
on indicators (when they say
“umbrellas” they also mean
“rainfall”)
as well as semantic token
embedding using BERT

16. Recommendations Explanation
positives.csv
notpositives.csv
SARIMA_model
dengue_monthly.csv
climate_monthly.csv
“dengue” ancestor
“dengue” ancestor
“dengue” ancestor
“dengue"
“wind speed”
QUERY PROCESS
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The final results include an
explanation of why files or
programs are included.

17. Recommendations Explanation
positives.csv
notpositives.csv
SARIMA_model
dengue_monthly.csv
climate_monthly.csv
“dengue” ancestor
“dengue” ancestor
“dengue” ancestor
“dengue"
“wind speed”
QUERY PROCESS
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18. EVALUATION
• Evaluation is difficult due to a lack of public datasets
• Most machine learning examples are truncated to final steps
• Very few non-machine learning pipelines exist outside of toy examples
• Private datasets generally cannot be shared
• Still important to use when possible due to scale
• Evaluation of recommendation engines is a subtle art
• Their purpose is to change behaviors
• Todays recommendations select tomorrow’s training data
• We aren’t to this point yet, this would be a symptom of success
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19. EVALUATION
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20. EVALUATION
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21. THANK YOU
ted.dunning@hpe.com
@ted_dunning
@ted_dunning@mastodon.social
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