Transparent Provenance Derivation for User Decisions

Ingrid Nunes; Yuhui Chen; Simon Miles; Michael Luck; Carlos J. P. de Lucena

doi:10.1007/978-3-642-34222-6_9

Transparent Provenance Derivation for User Decisions

Ingrid Nunes, Yuhui Chen, Simon Miles, Michael Luck, Carlos J. P. de Lucena

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

8 Citations (Scopus)

237 Downloads (Pure)

Abstract

It is rare for data’s history to include computational processes alone. Even when software generates data, users ultimately decide to execute software procedures, choose their configuration and inputs, reconfigure, halt and restart processes, and so on. Understanding the provenance of data thus involves understanding the reasoning of users behind these decisions, but demanding that users explicitly document decisions could be intrusive if implemented naively, and impractical in some cases. In this paper, therefore, we explore an approach to transparently deriving the provenance of user decisions at query time. The user reasoning is simulated, and if the result of the simulation matches the documented decision, the simulation is taken to approximate the actual reasoning. The plausibility of this approach requires that the simulation mirror human decision-making, so we adopt an automated process explicitly modelled on human psychology. The provenance of the decision is modelled in Open Provenance Model (OPM), allowing it to be queried as part of a larger provenance graph, and an OPM profile is provided to allow consistent querying of provenance across user decisions.

Original language	English
Title of host publication	Provenance and Annotation of Data and Processes
Subtitle of host publication	4th International Provenance and Annotation Workshop, IPAW 2012, Santa Barbara, CA, USA, June 19-21, 2012, Revised Selected Papers
Editors	Paul Groth, James Frew
Place of Publication	Berlin and New York
Publisher	Springer
Pages	111-125
Number of pages	15
Volume	N/A
Edition	N/A
ISBN (Print)	9783642342219
DOIs	https://doi.org/10.1007/978-3-642-34222-6_9
Publication status	Published - 2012
Event	4th International Provenance and Annotation Workshop, IPAW 2012 - Santa Barbara, CA, United States Duration: 19 Jun 2012 → 21 Jun 2012

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Berlin Heidelberg
Volume	7525
ISSN (Print)	0302-9743

Conference

Conference	4th International Provenance and Annotation Workshop, IPAW 2012
Country/Territory	United States
City	Santa Barbara, CA
Period	19/06/2012 → 21/06/2012

Access to Document

10.1007/978-3-642-34222-6_9

Transparent Provenance Derivation for User Decisions
The final publication is available at link.springer.com
Submitted manuscript, 592 KB

http://link.springer.com/chapter/10.1007%2F978-3-642-34222-6_9

Cite this

Nunes, I., Chen, Y., Miles, S., Luck, M., & de Lucena, C. J. P. (2012). Transparent Provenance Derivation for User Decisions. In P. Groth, & J. Frew (Eds.), Provenance and Annotation of Data and Processes: 4th International Provenance and Annotation Workshop, IPAW 2012, Santa Barbara, CA, USA, June 19-21, 2012, Revised Selected Papers (N/A ed., Vol. N/A, pp. 111-125). (Lecture Notes in Computer Science; Vol. 7525). Springer. https://doi.org/10.1007/978-3-642-34222-6_9

Nunes, Ingrid ; Chen, Yuhui ; Miles, Simon et al. / Transparent Provenance Derivation for User Decisions. Provenance and Annotation of Data and Processes: 4th International Provenance and Annotation Workshop, IPAW 2012, Santa Barbara, CA, USA, June 19-21, 2012, Revised Selected Papers. editor / Paul Groth ; James Frew. Vol. N/A N/A. ed. Berlin and New York : Springer, 2012. pp. 111-125 (Lecture Notes in Computer Science).

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abstract = "It is rare for data{\textquoteright}s history to include computational processes alone. Even when software generates data, users ultimately decide to execute software procedures, choose their configuration and inputs, reconfigure, halt and restart processes, and so on. Understanding the provenance of data thus involves understanding the reasoning of users behind these decisions, but demanding that users explicitly document decisions could be intrusive if implemented naively, and impractical in some cases. In this paper, therefore, we explore an approach to transparently deriving the provenance of user decisions at query time. The user reasoning is simulated, and if the result of the simulation matches the documented decision, the simulation is taken to approximate the actual reasoning. The plausibility of this approach requires that the simulation mirror human decision-making, so we adopt an automated process explicitly modelled on human psychology. The provenance of the decision is modelled in Open Provenance Model (OPM), allowing it to be queried as part of a larger provenance graph, and an OPM profile is provided to allow consistent querying of provenance across user decisions.",

author = "Ingrid Nunes and Yuhui Chen and Simon Miles and Michael Luck and {de Lucena}, {Carlos J. P.}",

year = "2012",

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language = "English",

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Nunes, I, Chen, Y , Miles, S , Luck, M & de Lucena, CJP 2012, Transparent Provenance Derivation for User Decisions. in P Groth & J Frew (eds), Provenance and Annotation of Data and Processes: 4th International Provenance and Annotation Workshop, IPAW 2012, Santa Barbara, CA, USA, June 19-21, 2012, Revised Selected Papers. N/A edn, vol. N/A, Lecture Notes in Computer Science, vol. 7525, Springer, Berlin and New York, pp. 111-125, 4th International Provenance and Annotation Workshop, IPAW 2012, Santa Barbara, CA, United States, 19/06/2012. https://doi.org/10.1007/978-3-642-34222-6_9

Transparent Provenance Derivation for User Decisions. / Nunes, Ingrid; Chen, Yuhui ; Miles, Simon et al.
Provenance and Annotation of Data and Processes: 4th International Provenance and Annotation Workshop, IPAW 2012, Santa Barbara, CA, USA, June 19-21, 2012, Revised Selected Papers. ed. / Paul Groth; James Frew. Vol. N/A N/A. ed. Berlin and New York: Springer, 2012. p. 111-125 (Lecture Notes in Computer Science; Vol. 7525).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper

TY - CHAP

T1 - Transparent Provenance Derivation for User Decisions

AU - Nunes, Ingrid

AU - Chen, Yuhui

AU - Miles, Simon

AU - Luck, Michael

AU - de Lucena, Carlos J. P.

PY - 2012

Y1 - 2012

N2 - It is rare for data’s history to include computational processes alone. Even when software generates data, users ultimately decide to execute software procedures, choose their configuration and inputs, reconfigure, halt and restart processes, and so on. Understanding the provenance of data thus involves understanding the reasoning of users behind these decisions, but demanding that users explicitly document decisions could be intrusive if implemented naively, and impractical in some cases. In this paper, therefore, we explore an approach to transparently deriving the provenance of user decisions at query time. The user reasoning is simulated, and if the result of the simulation matches the documented decision, the simulation is taken to approximate the actual reasoning. The plausibility of this approach requires that the simulation mirror human decision-making, so we adopt an automated process explicitly modelled on human psychology. The provenance of the decision is modelled in Open Provenance Model (OPM), allowing it to be queried as part of a larger provenance graph, and an OPM profile is provided to allow consistent querying of provenance across user decisions.

AB - It is rare for data’s history to include computational processes alone. Even when software generates data, users ultimately decide to execute software procedures, choose their configuration and inputs, reconfigure, halt and restart processes, and so on. Understanding the provenance of data thus involves understanding the reasoning of users behind these decisions, but demanding that users explicitly document decisions could be intrusive if implemented naively, and impractical in some cases. In this paper, therefore, we explore an approach to transparently deriving the provenance of user decisions at query time. The user reasoning is simulated, and if the result of the simulation matches the documented decision, the simulation is taken to approximate the actual reasoning. The plausibility of this approach requires that the simulation mirror human decision-making, so we adopt an automated process explicitly modelled on human psychology. The provenance of the decision is modelled in Open Provenance Model (OPM), allowing it to be queried as part of a larger provenance graph, and an OPM profile is provided to allow consistent querying of provenance across user decisions.

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DO - 10.1007/978-3-642-34222-6_9

M3 - Conference paper

SN - 9783642342219

VL - N/A

T3 - Lecture Notes in Computer Science

SP - 111

EP - 125

BT - Provenance and Annotation of Data and Processes

A2 - Groth, Paul

A2 - Frew, James

PB - Springer

CY - Berlin and New York

T2 - 4th International Provenance and Annotation Workshop, IPAW 2012

Y2 - 19 June 2012 through 21 June 2012

ER -

Nunes I, Chen Y , Miles S , Luck M, de Lucena CJP. Transparent Provenance Derivation for User Decisions. In Groth P, Frew J, editors, Provenance and Annotation of Data and Processes: 4th International Provenance and Annotation Workshop, IPAW 2012, Santa Barbara, CA, USA, June 19-21, 2012, Revised Selected Papers. N/A ed. Vol. N/A. Berlin and New York: Springer. 2012. p. 111-125. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-34222-6_9

Transparent Provenance Derivation for User Decisions

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