research-article

Clustering provenance facilitating provenance exploration through data abstraction

Authors:

Linus Karsai,

Alan Fekete,

Judy Kay,

Paolo MissierAuthors Info & Claims

HILDA '16: Proceedings of the Workshop on Human-In-the-Loop Data Analytics

Article No.: 6, Pages 1 - 5

https://doi.org/10.1145/2939502.2939508

Published: 26 June 2016 Publication History

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Abstract

As digital objects become increasingly important in people's lives, people may need to understand the provenance, or lineage and history, of an important digital object, to understand how it was produced. This is particularly important for objects created from large, multi-source collections of personal data. As the metadata describing provenance, Provenance Data, is commonly represented as a labelled directed acyclic graph, the challenge is to create effective interfaces onto such graphs so that people can understand the provenance of key digital objects. This unsolved problem is especially challenging for the case of novice and intermittent users and complex provenance graphs. We tackle this by creating an interface based on a clustering approach. This was designed to enable users to view provenance graphs, and to simplify complex graphs by combining several nodes. Our core contribution is the design of a prototype interface that supports clustering and its analytic evaluation in terms of desirable properties of visualisation interfaces.

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Alencar VKohwalter TBraganholo Vda Silva JMurta L(2024)Prov-Dominoes: An approach for knowledge discovery from provenance dataExpert Systems with Applications10.1016/j.eswa.2023.123030245(123030)Online publication date: Jul-2024
https://doi.org/10.1016/j.eswa.2023.123030
Zhou ZWang WGuo MWang YGotz D(2023)A Design Space for Surfacing Content Recommendations in Visual Analytic PlatformsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.320944529:1(84-94)Online publication date: Jan-2023
https://doi.org/10.1109/TVCG.2022.3209445
de Oliveira WBraga RDavid JStroele VCampos FCastro G(2022)Visionary: a framework for analysis and visualization of provenance dataKnowledge and Information Systems10.1007/s10115-021-01645-664:2(381-413)Online publication date: 4-Jan-2022
https://doi.org/10.1007/s10115-021-01645-6
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HILDA '16: Proceedings of the Workshop on Human-In-the-Loop Data Analytics

June 2016

93 pages

ISBN:9781450342070

DOI:10.1145/2939502

Conference Chairs:
Carsten Binnig,
Alan Fekete,
Arnab Nandi

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

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Published: 26 June 2016

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SIGMOD/PODS'16: International Conference on Management of Data

June 26 - July 1, 2016

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Overall Acceptance Rate 28 of 56 submissions, 50%

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Alencar VKohwalter TBraganholo Vda Silva JMurta L(2024)Prov-Dominoes: An approach for knowledge discovery from provenance dataExpert Systems with Applications10.1016/j.eswa.2023.123030245(123030)Online publication date: Jul-2024
https://doi.org/10.1016/j.eswa.2023.123030
Zhou ZWang WGuo MWang YGotz D(2023)A Design Space for Surfacing Content Recommendations in Visual Analytic PlatformsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.320944529:1(84-94)Online publication date: Jan-2023
https://doi.org/10.1109/TVCG.2022.3209445
de Oliveira WBraga RDavid JStroele VCampos FCastro G(2022)Visionary: a framework for analysis and visualization of provenance dataKnowledge and Information Systems10.1007/s10115-021-01645-664:2(381-413)Online publication date: 4-Jan-2022
https://doi.org/10.1007/s10115-021-01645-6
Gupta HRajmohan CMehta SPulapa K(2020)On Efficiently Processing Business Lineage Queries2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9377973(513-522)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9377973
Spiekermann RJolly BHerzig ABurleigh TMedyckyj-Scott D(2019)Implementations of fine-grained automated data provenance to support transparent environmental modellingEnvironmental Modelling & Software10.1016/j.envsoft.2019.04.009Online publication date: Apr-2019
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Oliveira WOliveira DBraganholo V(2018)Provenance Analytics for Workflow-Based Computational ExperimentsACM Computing Surveys10.1145/318490051:3(1-25)Online publication date: 23-May-2018
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Trillo-Lado RDietze S(2018)KEYSTONE WG1: Activities and Results Overview on Representation of Structured Data SourcesSemantic Keyword-Based Search on Structured Data Sources10.1007/978-3-319-74497-1_20(196-214)Online publication date: 8-Feb-2018
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Herschel MDiestelkämper RBen Lahmar H(2017)A survey on provenanceThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-017-0486-126:6(881-906)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s00778-017-0486-1

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The perm provenance management system in action

Local clustering in provenance graphs

Provenance of publications: a PROV style for latex

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