A talk given at the DATAPLAT workshop, co-located with the IEEE ICDE conference (May 2024, Utrecht, NL). Data Provenance for Data Science is our attempt to provide a foundation to add explainability to data-centric AI. It is a prototype, with lots of work still to do.

1. Luca Gregori∗, Paolo Missier†, Matthew Stidolph‡, Riccardo Torlone∗ and Alessandro Wood∗
∗Department of Engineering, Roma Tre University, Italy
†School of Computer Science, University of Birmingham, UK
‡Newcastle University, School of Computing
DATAPLAT@ICDE
May 2024
Utrecht, NL
Design and Development of a Provenance
Capture Platform for Data Science

2. 2
Setting and questions
Model
outputs
Training
datasets
Source
datasets
Data processing Training M Inference/generation
Data explanation questions:
• Which data transformations were applied to raw input dataset(s) to generate the final
training set used for modelling?
• Which of the individual data items were affected by each of the transformations
• What was the effect?
DATAPLAT@ICDE
2024

3. 3
Provenance basics
Abstract data transformation operator: 𝐷 → (OP) → 𝐷ʹ
D D’
A
wasGeneratedBy
wasDerivedFrom
used
Provenance expression:
DATAPLAT@ICDE
2024

4. 4
Extension to DAG topologies
Example: inputs 𝐷0
𝑎, 𝐷0
𝑏 Dc
0 are processed independently and eventually merged into 𝐷𝑛:
Da
0 OP1 Da
1
Db
0 OP2 Db
1
Dc
0
OP3 Dbc
0
OP4 Dabc
3
Da
0 OP1 Da
1
Db
0 OP2 Db
1
Dc
0
OP3 Dbc
0
OP4 Dabc
3
used
used
used
used
wgby
wgby
DATAPLAT@ICDE
2024

5. 5
The Big Provenance Dogma
Data provenance is an enabler for:
• Transparency
• Explainability
• Reproducibility
…for a variety of underlying process and source / target data combinations
Model
outputs
Training
datasets
Data processing Training M Inference/generation
Source Target
Process
DATAPLAT@ICDE
2024

6. 6
DATAPLAT@ICDE
2024
Contributions
ü Analysis of over 500 Data Science pipeline
§ “in the wild” --> Kaggle
§ “controlled” --> ML Bazaar
ü Formal provenance semantics for a catalogue of commonly used Data Science operators
ü Data Provenance for Data Science (DPDS)
§ automatically track granular provenance from Pandas
§ maximally transparent and minimally intrusive to the programmer
ü Empirical evaluation against a grid of 3 benchmark datasets x 3 synthetic pipelines

7. 7
Data processing pipelines analysis: ML Bazaar
ü Facilitates developing ML and AutoML systems
ü Workflow style: Pipelines composed out of pre-defined primitives
ü Data + task pairs with benchmark results over multiple data types
✗ Only 5 types of operators
✗ Single location, controlled ecosystem
DFS = Deep Feature Synthesis
DATAPLAT@ICDE
2024

8. 8
Data processing pipelines analysis: Kaggle
Scope: top 200 most upvoted python notebooks related to machine learning on Kaggle
Ø 29 unique pre-processing operations
Ø 12 appear in less than 10 pipelines
§ Transposing
§ changing index values
Ø feature augmentation (58)
Ø scaling operations (38)
DATAPLAT@ICDE
2024

9. 9
Data processing operators
DATAPLAT@ICDE
2024

10. 10
Data reduction
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D0
= ⇡C(D), D0
= C(D) - Projection, Selection
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⇡{Cid,Gender,Age}( Age<30(D))
DATAPLAT@ICDE
2024

11. 11
Data augmentation
Vertical augmentation
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↵!
f(X):Y
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↵!
f1(Age):ageRange(D)
group by gender
avg(age)
Horizontal augmentation
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E2 = ↵#
Gender:f2(Age)(D)
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↵#
X:f(Y )(D)
DATAPLAT@ICDE
2024

12. 12
Data transformation
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⌧f(X)
<latexit sha1_base64="Q7sjzw3r7FpZN6MWGMj9azYMGFk=">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</latexit>
the transformation of a set of features X of D using a function f
is obtained by substituting each value dia with f(d⇤a),
for each feature a occurring in X.
Example: data imputation. Here f replaces nulls with the most frequent value, for
column Zip
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⌧f(Zip)(D)
DATAPLAT@ICDE
2024

13. 13
Data fusion: join and append
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DL
./t
C DR
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DL
] DR
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DL
] DR
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DL
./inner
DL.CId=DR.CId DR
DATAPLAT@ICDE
2024

14. 14
Conceptual provenance capture model: templates
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↵!
f1(Age):ageRange(D)
A different provenance template pt𝜏 is associated with each type 𝜏 of operator

15. 15
Capturing provenance: bindings
At runtime, when operator o of type 𝜏 is executed, the appropriate template pt𝜏 for 𝜏 is selected
Data items from the inputs and outputs of the operator are used to bind the variables in the template
14/03/2021 03_ b _c .
:///U / 65/D a /03_ b _c . 1/1
14/03/2021 03_ b _c .
:///U / 65/D a /03_ b _c . 1/1
op
{old values: F, I, V} à {new values: F’, J, V’}
+
Binding rules
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For i : 1 . . . n :
used ent.:[hF = Xm, I = i, V = Di,Xm
i|Xm 2 X]
generated ent.:[hF0
= Yh, J = i, v = f(Di,X )i|Yh 2 Y ]

16. 16
Implementation by shape and value diff
Shape changes:
Rows
Added?
Rows
Removed?
Columns
Added?
Columns
Removed?
Columns
Removed?
Horizontal
Augmentation
Reduction
by selection
Reduction
by projection
data
transformation
(composite)
Y
Y
Y
Y
data
transformation
Y
N
N
N
Templates:
N
Value changes for each column:
Nulls reduced?
Values changed?
Y
Y
N
Templates:
data
transformation
(imputation)
data
transformation
1-1 derivations
For each input/output pair Din, Dout of dataframes:
1. Diff both shapes and values of Din, Dout
2. Use the diff to:
• Select the appropriate template
• Bind the template variables using the
relevant values in the two dataframes
• Generate an instantiated provlet
DATAPLAT@ICDE
2024

17. 17
Running Example
D1 D2 D3
Add
‘E4,’ ‘Ex’, ‘E1’
Remove ‘E’
D4 D6
Da
Db
Left join
(K1,K2)
Impute
all missing
Dc
Left join
(K1,K2)
Impute E,F
D5
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D1 = Da ./left
K1,K2
Db
D2 = ⌧f1(⇤)(D1)
D3 = D2 ./left
K1,K2
Dc
D4 = ⌧f2(E,F )(D3)
D5 = ↵!
h(E):{E4,Ex,E1}(D4)
D6 = ⇡{Ax,B,Ay,D,C,F,E4,Ex,E1,}(D5)
DATAPLAT@ICDE
2024

18. 18
Running Example
D1 D2 D3
Add
‘E4,’ ‘Ex’, ‘E1’
Remove ‘E’
D4 D6
Da
Db
Left join
(K1,K2)
Impute
all missing
Dc
Left join
(K1,K2)
Impute E,F
D5
df = pd.merge(df_A, df_B, on=['key1', 'key2'], how='left’) # join
df = df.fillna('imputed’) # Imputation
df = pd.merge(df, df_C, on=['key1', 'key2'], how='left’) #join
df = df.fillna(value={'E':'Ex', 'F':'Fx’}) # Imputation
# one-hot encoding
c = 'E'
dummies = []
dummies.append(pd.get_dummies(df[c]))
df_dummies = pd.concat(dummies, axis=1)
df = pd.concat((df, df_dummies), axis=1)
df = df_A.drop([c], axis=1)
DATAPLAT@ICDE
2024

19. 19
Running Example
D1 D2 D3
Add
‘E4,’ ‘Ex’, ‘E1’
Remove ‘E’
D4 D6
Da
Db
Left join
(K1,K2)
Impute
all missing
Dc
Left join
(K1,K2)
Impute E,F
D5
Dataframes Diff template
D1 ß {Da, Db} Explicit join provenance pattern
D2 ß D1 value change, reduced nulls à imputation Data transformation
D3 ß {D2, Dc} Explicit join provenance pattern
D4 ß D3 value change, reduced nulls à imputation Data transformation
D45 ß D4 Shape change, column(s) added <wait!>
D6 ß D5 Shape change, column(s) removed Data transformation, composite
DATAPLAT@ICDE
2024

20. 20
Program level transparency with control
Approach:
- add an observer to monitor dataframe changes
- mostly transparent to application
- some control over Tracker surfaced
DATAPLAT@ICDE
2024

21. 21
Provenance traversals – example
Capture, store and query element-level provenance
- Derivation of each element of each intermediate dataframe (when possible)
- Efficiently, at scale
fillna
Join
df_1
df_B (df_0)
df_A (df_-1)
DATAPLAT@ICDE
2024

22. 22
Benchmarking: data x pipelines
Datasets:
Pipelines:
Provenance graphs are stored
in a single Neo4J database
DATAPLAT@ICDE
2024

23. 23
Results
The PT/PO ratio provides a rough indication of scalability:
- The graphs for the complete pipelines are close in size to the sum of the sizes of the components’
graphs
1,2,3: pipeline number
DATAPLAT@ICDE
2024

24. 24
Conclusions
ü DPDS generates granular provenance graphs that accurately represent the
underlying data processing
ü A potentially useful building block towards explanations in a Data Centric AI
setting
Limitations:
v No granularity control --> limited scalability
v Operates only on Pandas dataframes
DATAPLAT@ICDE
2024