a graph model for data and workﬂow provenance · 2019. 2. 25. · a graph model for data and...

A graph model for data and workflow provenance

Umut Acar, Peter Buneman, James Cheney, Natalia Kwasnikowska,

Jan van den Bussche, & Stijn Vansummeren

TaPP 2010

Provenance in ...• Databases

• Mainly for (nested) relational model

• Where-provenance ("source location")

• Lineage, why ("witnesses")

• How/semiring model

• Relatively formal

• Workflows

• Many different systems

• Many different models

• (converging on OPM?)

• Graphs/DAGs

• Relatively informal

Provenance in ...• Databases

• Mainly for (nested) relational model

• Where-provenance ("source location")

• Lineage, why ("witnesses")

• How/semiring model

• Relatively formal

• Workflows

• Many different systems

• Many different models

• (converging on OPM?)

• Graphs/DAGs

• Relatively informal

?????

This talk

• Relate database & workflow "styles"

• Develop a common graph formalism

• Need a common, expressive language that

• supports many database queries

• describes some (simple) workflows

Previous work

• Dataflow calculus (DFL), based on nested relational calculus (NRC)

• Provenance "run" model by Kwasnikowska & Van den Bussche (DILS 07, IPAW 08)

• "Provenance trace" model for NRC

• by (Acar, Ahmed & C. '08)

• Open Provenance Model (bipartite graphs)

• (Moreau et al. 2008-9), used in many WF systems

NRC/DFL background

• A very simple, functional language:

• basic functions +, *,... & constants 0,1,2,3...

• variables x,y,z

• pair/record types (A:e,...,B:e), πA (e)

• collection (set) types

• {e,...} e ∪ e {e | x in e'} ∪e

An example

An example

• Suppose R = {(1,2,3), (4,5,6), (9,8,7)}

An example

• Suppose R = {(1,2,3), (4,5,6), (9,8,7)}

sum { x * y | (x,y,z) in R, x < y}

An example

• Suppose R = {(1,2,3), (4,5,6), (9,8,7)}

sum { x * y | (x,y,z) in R, x < y}

= sum { x * y | (x,y,z) in {(1,2,3), (4,5,6)}}

An example

• Suppose R = {(1,2,3), (4,5,6), (9,8,7)}

sum { x * y | (x,y,z) in R, x < y}

= sum { x * y | (x,y,z) in {(1,2,3), (4,5,6)}}

= sum {1 * 2, 4 * 5}

An example

• Suppose R = {(1,2,3), (4,5,6), (9,8,7)}

sum { x * y | (x,y,z) in R, x < y}

= sum { x * y | (x,y,z) in {(1,2,3), (4,5,6)}}

= sum {1 * 2, 4 * 5}

= sum {2,20}

An example

• Suppose R = {(1,2,3), (4,5,6), (9,8,7)}

sum { x * y | (x,y,z) in R, x < y}

= sum { x * y | (x,y,z) in {(1,2,3), (4,5,6)}}

= sum {1 * 2, 4 * 5}

= sum {2,20}

= 22

Another example

• In DFL, built-in functions / constants can be whole programs & files,

• as in Provenance Challenge 1 workflow:

let WarpParams := {align_warp(img,hdr})

| (img,hdr) in Inputs} in

let Reslices := {reslice(wp)

| wp in WarpParams} in

softmean(Reslices)

Goal: Define "provenance graphs" for DFL


let WarpParams := {align_warp(img,hdr}) | (img,hdr) in Inputs} inlet Reslices := {reslice(wp) | wp in WarpParams} inin softmean(Reslices)


let WarpParams := {align_warp(img,hdr}) | (img,hdr) in Inputs} inlet Reslices := {reslice(wp) | wp in WarpParams} inin softmean(Reslices)

http://www.flickr.com/photos/schneertz/679692806/



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Provenance graphs

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Input values

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Building provenance graphs

• is complicated

• Here we'll use high-level "graph rewrite rule" formalism

• Mostly because it is nicer to look at than formal version

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Graphs can "lie" (inconsistency)

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•+ 5

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"Locally" but not "globally" consistent

Graph queries

• Many possible approaches

• In paper: some Datalog

• Maybe overkill, seems fragile

• In code: some "annotation propagation" traversals

• Seems to handle where, "explanations", "summaries"

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Explaining

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Explaining

Note: Smallest consistent subgraph (NOT

transitive closure!)

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Summarizing

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Summarizing

{}1

1

Graphs are partially "replayable"

• If we change a value node, can try to "readjust" to recover consistency

• Formalized in (Acar, Ahmed, Cheney 08)

+ 4

2

2




+ 4

2

17




+

2

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19




+

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+

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else




+

2

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if

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else

Stuck!

????

Implementation in Haskell

• Summarized in paper, full code on request

• roughly 250 LOC for basic evaluator

• another 300 for graphviz translation, basic queries, examples

• Point?

• No claim of efficiency/scalability but easy to understand, experiment

• Elucidates some tricky details that pictures hide

• Similar "lightweight modeling" might be valuable for understanding/relating other WF/DB models

Related work• This work synthesizes/rearranges ideas from

several previous works & "folklore"

• traces (Acar, Ahmed, Cheney 2008)

• runs (Kwasnikowska, van den Bussche, DILS 2007, IPAW 2008)

• OPM graphs (Moreau et al. IPAW 2008 etc.)

• and many workflow systems

• More can be done to relate DB & workflow models

Future work

• This is work in progress

• Next steps:

• Extending to understand/model other workflow features

• Better grasp of "real" queries and features needed

• Implementa(tion|ability)?

• Optimization?

Conclusions

• DB & WF provenance have much in common

• We develop common graph model

• with both intuitive & precise presentations

• Still much to do to relate and integrate DB & WF models

• let alone integrate models at scale in real systems

a graph model for data and workﬂow provenance · 2019. 2. 25. · a graph model for data and...

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