We introduce the idea that metadata, including project information, data labels, data characteristics and indications of valuable use, can be propagated through a data processing lineage graph. Further, finding examples of significant cooccurrence of propagated and original metadata gives us the basis of an interesting kind of search engine gives interesting recommendations of data given a problem statement even in a near cold-start situation.

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
3
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
8
README
URL
History
Datasets
+
Models
Metadata
We start with explicit metadata.
Examples: column and table
names, documentation, common
values, and others

9. TRAINING PROCESS
9
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
10
README
URL
History
Datasets
+
Models
Metadata
We augment with
metadata from all
ancestors and
descendants in
the global data
lineage graph

11. TRAINING PROCESS
11
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
13
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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