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Aggregation by Provenance Types: A Technique for Summarising Provenance Graphs

Aggregation by Provenance Types: A Technique for Summarising Provenance Graphs
Aggregation by Provenance Types: A Technique for Summarising Provenance Graphs
As users become confronted with a deluge of provenance data, dedicated techniques are required to make sense of this kind of information. We present Aggregation by Provenance Types, a provenance graph analysis that is capable of generating provenance graph summaries. It proceeds by converting provenance paths up to some length k to attributes, referred to as provenance types, and by grouping nodes that have the same provenance types. The summary also includes numeric values representing the frequency of nodes and edges in the original graph.Quantitative and qualitative evaluations and a complexity analysis show that this technique is tractable; with small values of k, it can produce useful summaries and can help detect outliers. We illustrate how the generated summaries can further be used for conformance checking and
visualization.
129-144
Moreau, Luc
033c63dd-3fe9-4040-849f-dfccbe0406f8
Moreau, Luc
033c63dd-3fe9-4040-849f-dfccbe0406f8

Moreau, Luc (2015) Aggregation by Provenance Types: A Technique for Summarising Provenance Graphs. GRAPHS AS MODELS 2015. pp. 129-144 . (doi:10.4204/EPTCS.181.9).

Record type: Conference or Workshop Item (Paper)

Abstract

As users become confronted with a deluge of provenance data, dedicated techniques are required to make sense of this kind of information. We present Aggregation by Provenance Types, a provenance graph analysis that is capable of generating provenance graph summaries. It proceeds by converting provenance paths up to some length k to attributes, referred to as provenance types, and by grouping nodes that have the same provenance types. The summary also includes numeric values representing the frequency of nodes and edges in the original graph.Quantitative and qualitative evaluations and a complexity analysis show that this technique is tractable; with small values of k, it can produce useful summaries and can help detect outliers. We illustrate how the generated summaries can further be used for conformance checking and
visualization.

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Submitted date: 7 May 2014
Accepted/In Press date: 12 February 2015
e-pub ahead of print date: 19 March 2015
Published date: 10 April 2015
Venue - Dates: GRAPHS AS MODELS 2015, 2015-02-12
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 364726
URI: http://eprints.soton.ac.uk/id/eprint/364726
PURE UUID: cbac18ae-0346-4c93-bf37-3a25e929a667
ORCID for Luc Moreau: ORCID iD orcid.org/0000-0002-3494-120X

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Date deposited: 07 May 2014 12:20
Last modified: 14 Mar 2024 16:40

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Contributors

Author: Luc Moreau ORCID iD

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