Distributed temporal graph analytics with GRADOOP

Abstract Temporal property graphs are graphs whose structure and properties change over time. Temporal graph datasets tend to be large due to stored historical information, asking for scalable analysis capabilities. We give a complete overview of Gradoop, a graph dataflow system for scalable, distri...
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Autor*in:	Rost, Christopher [verfasserIn] Gomez, Kevin Täschner, Matthias Fritzsche, Philip Schons, Lucas Christ, Lukas Adameit, Timo Junghanns, Martin Rahm, Erhard

Format:	Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Graph processing Temporal graph Distributed processing Graph analytics Bitemporal graph model

Anmerkung:	© The Author(s) 2021

Übergeordnetes Werk:	Enthalten in: The VLDB journal - Springer Berlin Heidelberg, 1992, 31(2021), 2 vom: 19. Mai, Seite 375-401
Übergeordnetes Werk:	volume:31 ; year:2021 ; number:2 ; day:19 ; month:05 ; pages:375-401

Links:	Volltext

DOI / URN:	10.1007/s00778-021-00667-4

Katalog-ID:	OLC2078267686

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520			\|a Abstract Temporal property graphs are graphs whose structure and properties change over time. Temporal graph datasets tend to be large due to stored historical information, asking for scalable analysis capabilities. We give a complete overview of Gradoop, a graph dataflow system for scalable, distributed analytics of temporal property graphs which has been continuously developed since 2005. Its graph model TPGM allows bitemporal modeling not only of vertices and edges but also of graph collections. A declarative analytical language called GrALa allows analysts to flexibly define analytical graph workflows by composing different operators that support temporal graph analysis. Built on a distributed dataflow system, large temporal graphs can be processed on a shared-nothing cluster. We present the system architecture of Gradoop, its data model TPGM with composable temporal graph operators, like snapshot, difference, pattern matching, graph grouping and several implementation details. We evaluate the performance and scalability of selected operators and a composed workflow for synthetic and real-world temporal graphs with up to 283 M vertices and 1.8 B edges, and a graph lifetime of about 8 years with up to 20 M new edges per year. We also reflect on lessons learned from the Gradoop effort.
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allfields	10.1007/s00778-021-00667-4 doi (DE-627)OLC2078267686 (DE-He213)s00778-021-00667-4-p DE-627 ger DE-627 rakwb eng 004 VZ Rost, Christopher verfasserin (orcid)0000-0003-4217-9312 aut Distributed temporal graph analytics with GRADOOP 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Temporal property graphs are graphs whose structure and properties change over time. Temporal graph datasets tend to be large due to stored historical information, asking for scalable analysis capabilities. We give a complete overview of Gradoop, a graph dataflow system for scalable, distributed analytics of temporal property graphs which has been continuously developed since 2005. Its graph model TPGM allows bitemporal modeling not only of vertices and edges but also of graph collections. A declarative analytical language called GrALa allows analysts to flexibly define analytical graph workflows by composing different operators that support temporal graph analysis. Built on a distributed dataflow system, large temporal graphs can be processed on a shared-nothing cluster. We present the system architecture of Gradoop, its data model TPGM with composable temporal graph operators, like snapshot, difference, pattern matching, graph grouping and several implementation details. We evaluate the performance and scalability of selected operators and a composed workflow for synthetic and real-world temporal graphs with up to 283 M vertices and 1.8 B edges, and a graph lifetime of about 8 years with up to 20 M new edges per year. We also reflect on lessons learned from the Gradoop effort. Graph processing Temporal graph Distributed processing Graph analytics Bitemporal graph model Gomez, Kevin (orcid)0000-0001-6928-7335 aut Täschner, Matthias aut Fritzsche, Philip aut Schons, Lucas aut Christ, Lukas aut Adameit, Timo aut Junghanns, Martin aut Rahm, Erhard (orcid)0000-0002-2665-1114 aut Enthalten in The VLDB journal Springer Berlin Heidelberg, 1992 31(2021), 2 vom: 19. Mai, Seite 375-401 (DE-627)170933059 (DE-600)1129061-4 (DE-576)032856466 1066-8888 nnns volume:31 year:2021 number:2 day:19 month:05 pages:375-401 https://doi.org/10.1007/s00778-021-00667-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_30 GBV_ILN_2018 GBV_ILN_4277 AR 31 2021 2 19 05 375-401
spelling	10.1007/s00778-021-00667-4 doi (DE-627)OLC2078267686 (DE-He213)s00778-021-00667-4-p DE-627 ger DE-627 rakwb eng 004 VZ Rost, Christopher verfasserin (orcid)0000-0003-4217-9312 aut Distributed temporal graph analytics with GRADOOP 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Temporal property graphs are graphs whose structure and properties change over time. Temporal graph datasets tend to be large due to stored historical information, asking for scalable analysis capabilities. We give a complete overview of Gradoop, a graph dataflow system for scalable, distributed analytics of temporal property graphs which has been continuously developed since 2005. Its graph model TPGM allows bitemporal modeling not only of vertices and edges but also of graph collections. A declarative analytical language called GrALa allows analysts to flexibly define analytical graph workflows by composing different operators that support temporal graph analysis. Built on a distributed dataflow system, large temporal graphs can be processed on a shared-nothing cluster. We present the system architecture of Gradoop, its data model TPGM with composable temporal graph operators, like snapshot, difference, pattern matching, graph grouping and several implementation details. We evaluate the performance and scalability of selected operators and a composed workflow for synthetic and real-world temporal graphs with up to 283 M vertices and 1.8 B edges, and a graph lifetime of about 8 years with up to 20 M new edges per year. We also reflect on lessons learned from the Gradoop effort. Graph processing Temporal graph Distributed processing Graph analytics Bitemporal graph model Gomez, Kevin (orcid)0000-0001-6928-7335 aut Täschner, Matthias aut Fritzsche, Philip aut Schons, Lucas aut Christ, Lukas aut Adameit, Timo aut Junghanns, Martin aut Rahm, Erhard (orcid)0000-0002-2665-1114 aut Enthalten in The VLDB journal Springer Berlin Heidelberg, 1992 31(2021), 2 vom: 19. Mai, Seite 375-401 (DE-627)170933059 (DE-600)1129061-4 (DE-576)032856466 1066-8888 nnns volume:31 year:2021 number:2 day:19 month:05 pages:375-401 https://doi.org/10.1007/s00778-021-00667-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_30 GBV_ILN_2018 GBV_ILN_4277 AR 31 2021 2 19 05 375-401
allfields_unstemmed	10.1007/s00778-021-00667-4 doi (DE-627)OLC2078267686 (DE-He213)s00778-021-00667-4-p DE-627 ger DE-627 rakwb eng 004 VZ Rost, Christopher verfasserin (orcid)0000-0003-4217-9312 aut Distributed temporal graph analytics with GRADOOP 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Temporal property graphs are graphs whose structure and properties change over time. Temporal graph datasets tend to be large due to stored historical information, asking for scalable analysis capabilities. We give a complete overview of Gradoop, a graph dataflow system for scalable, distributed analytics of temporal property graphs which has been continuously developed since 2005. Its graph model TPGM allows bitemporal modeling not only of vertices and edges but also of graph collections. A declarative analytical language called GrALa allows analysts to flexibly define analytical graph workflows by composing different operators that support temporal graph analysis. Built on a distributed dataflow system, large temporal graphs can be processed on a shared-nothing cluster. We present the system architecture of Gradoop, its data model TPGM with composable temporal graph operators, like snapshot, difference, pattern matching, graph grouping and several implementation details. We evaluate the performance and scalability of selected operators and a composed workflow for synthetic and real-world temporal graphs with up to 283 M vertices and 1.8 B edges, and a graph lifetime of about 8 years with up to 20 M new edges per year. We also reflect on lessons learned from the Gradoop effort. Graph processing Temporal graph Distributed processing Graph analytics Bitemporal graph model Gomez, Kevin (orcid)0000-0001-6928-7335 aut Täschner, Matthias aut Fritzsche, Philip aut Schons, Lucas aut Christ, Lukas aut Adameit, Timo aut Junghanns, Martin aut Rahm, Erhard (orcid)0000-0002-2665-1114 aut Enthalten in The VLDB journal Springer Berlin Heidelberg, 1992 31(2021), 2 vom: 19. Mai, Seite 375-401 (DE-627)170933059 (DE-600)1129061-4 (DE-576)032856466 1066-8888 nnns volume:31 year:2021 number:2 day:19 month:05 pages:375-401 https://doi.org/10.1007/s00778-021-00667-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_30 GBV_ILN_2018 GBV_ILN_4277 AR 31 2021 2 19 05 375-401
allfieldsGer	10.1007/s00778-021-00667-4 doi (DE-627)OLC2078267686 (DE-He213)s00778-021-00667-4-p DE-627 ger DE-627 rakwb eng 004 VZ Rost, Christopher verfasserin (orcid)0000-0003-4217-9312 aut Distributed temporal graph analytics with GRADOOP 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Temporal property graphs are graphs whose structure and properties change over time. Temporal graph datasets tend to be large due to stored historical information, asking for scalable analysis capabilities. We give a complete overview of Gradoop, a graph dataflow system for scalable, distributed analytics of temporal property graphs which has been continuously developed since 2005. Its graph model TPGM allows bitemporal modeling not only of vertices and edges but also of graph collections. A declarative analytical language called GrALa allows analysts to flexibly define analytical graph workflows by composing different operators that support temporal graph analysis. Built on a distributed dataflow system, large temporal graphs can be processed on a shared-nothing cluster. We present the system architecture of Gradoop, its data model TPGM with composable temporal graph operators, like snapshot, difference, pattern matching, graph grouping and several implementation details. We evaluate the performance and scalability of selected operators and a composed workflow for synthetic and real-world temporal graphs with up to 283 M vertices and 1.8 B edges, and a graph lifetime of about 8 years with up to 20 M new edges per year. We also reflect on lessons learned from the Gradoop effort. Graph processing Temporal graph Distributed processing Graph analytics Bitemporal graph model Gomez, Kevin (orcid)0000-0001-6928-7335 aut Täschner, Matthias aut Fritzsche, Philip aut Schons, Lucas aut Christ, Lukas aut Adameit, Timo aut Junghanns, Martin aut Rahm, Erhard (orcid)0000-0002-2665-1114 aut Enthalten in The VLDB journal Springer Berlin Heidelberg, 1992 31(2021), 2 vom: 19. Mai, Seite 375-401 (DE-627)170933059 (DE-600)1129061-4 (DE-576)032856466 1066-8888 nnns volume:31 year:2021 number:2 day:19 month:05 pages:375-401 https://doi.org/10.1007/s00778-021-00667-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_30 GBV_ILN_2018 GBV_ILN_4277 AR 31 2021 2 19 05 375-401
allfieldsSound	10.1007/s00778-021-00667-4 doi (DE-627)OLC2078267686 (DE-He213)s00778-021-00667-4-p DE-627 ger DE-627 rakwb eng 004 VZ Rost, Christopher verfasserin (orcid)0000-0003-4217-9312 aut Distributed temporal graph analytics with GRADOOP 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Temporal property graphs are graphs whose structure and properties change over time. Temporal graph datasets tend to be large due to stored historical information, asking for scalable analysis capabilities. We give a complete overview of Gradoop, a graph dataflow system for scalable, distributed analytics of temporal property graphs which has been continuously developed since 2005. Its graph model TPGM allows bitemporal modeling not only of vertices and edges but also of graph collections. A declarative analytical language called GrALa allows analysts to flexibly define analytical graph workflows by composing different operators that support temporal graph analysis. Built on a distributed dataflow system, large temporal graphs can be processed on a shared-nothing cluster. We present the system architecture of Gradoop, its data model TPGM with composable temporal graph operators, like snapshot, difference, pattern matching, graph grouping and several implementation details. We evaluate the performance and scalability of selected operators and a composed workflow for synthetic and real-world temporal graphs with up to 283 M vertices and 1.8 B edges, and a graph lifetime of about 8 years with up to 20 M new edges per year. We also reflect on lessons learned from the Gradoop effort. Graph processing Temporal graph Distributed processing Graph analytics Bitemporal graph model Gomez, Kevin (orcid)0000-0001-6928-7335 aut Täschner, Matthias aut Fritzsche, Philip aut Schons, Lucas aut Christ, Lukas aut Adameit, Timo aut Junghanns, Martin aut Rahm, Erhard (orcid)0000-0002-2665-1114 aut Enthalten in The VLDB journal Springer Berlin Heidelberg, 1992 31(2021), 2 vom: 19. Mai, Seite 375-401 (DE-627)170933059 (DE-600)1129061-4 (DE-576)032856466 1066-8888 nnns volume:31 year:2021 number:2 day:19 month:05 pages:375-401 https://doi.org/10.1007/s00778-021-00667-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_30 GBV_ILN_2018 GBV_ILN_4277 AR 31 2021 2 19 05 375-401
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title_sort	distributed temporal graph analytics with gradoop
title_auth	Distributed temporal graph analytics with GRADOOP
abstract	Abstract Temporal property graphs are graphs whose structure and properties change over time. Temporal graph datasets tend to be large due to stored historical information, asking for scalable analysis capabilities. We give a complete overview of Gradoop, a graph dataflow system for scalable, distributed analytics of temporal property graphs which has been continuously developed since 2005. Its graph model TPGM allows bitemporal modeling not only of vertices and edges but also of graph collections. A declarative analytical language called GrALa allows analysts to flexibly define analytical graph workflows by composing different operators that support temporal graph analysis. Built on a distributed dataflow system, large temporal graphs can be processed on a shared-nothing cluster. We present the system architecture of Gradoop, its data model TPGM with composable temporal graph operators, like snapshot, difference, pattern matching, graph grouping and several implementation details. We evaluate the performance and scalability of selected operators and a composed workflow for synthetic and real-world temporal graphs with up to 283 M vertices and 1.8 B edges, and a graph lifetime of about 8 years with up to 20 M new edges per year. We also reflect on lessons learned from the Gradoop effort. © The Author(s) 2021
abstractGer	Abstract Temporal property graphs are graphs whose structure and properties change over time. Temporal graph datasets tend to be large due to stored historical information, asking for scalable analysis capabilities. We give a complete overview of Gradoop, a graph dataflow system for scalable, distributed analytics of temporal property graphs which has been continuously developed since 2005. Its graph model TPGM allows bitemporal modeling not only of vertices and edges but also of graph collections. A declarative analytical language called GrALa allows analysts to flexibly define analytical graph workflows by composing different operators that support temporal graph analysis. Built on a distributed dataflow system, large temporal graphs can be processed on a shared-nothing cluster. We present the system architecture of Gradoop, its data model TPGM with composable temporal graph operators, like snapshot, difference, pattern matching, graph grouping and several implementation details. We evaluate the performance and scalability of selected operators and a composed workflow for synthetic and real-world temporal graphs with up to 283 M vertices and 1.8 B edges, and a graph lifetime of about 8 years with up to 20 M new edges per year. We also reflect on lessons learned from the Gradoop effort. © The Author(s) 2021
abstract_unstemmed	Abstract Temporal property graphs are graphs whose structure and properties change over time. Temporal graph datasets tend to be large due to stored historical information, asking for scalable analysis capabilities. We give a complete overview of Gradoop, a graph dataflow system for scalable, distributed analytics of temporal property graphs which has been continuously developed since 2005. Its graph model TPGM allows bitemporal modeling not only of vertices and edges but also of graph collections. A declarative analytical language called GrALa allows analysts to flexibly define analytical graph workflows by composing different operators that support temporal graph analysis. Built on a distributed dataflow system, large temporal graphs can be processed on a shared-nothing cluster. We present the system architecture of Gradoop, its data model TPGM with composable temporal graph operators, like snapshot, difference, pattern matching, graph grouping and several implementation details. We evaluate the performance and scalability of selected operators and a composed workflow for synthetic and real-world temporal graphs with up to 283 M vertices and 1.8 B edges, and a graph lifetime of about 8 years with up to 20 M new edges per year. We also reflect on lessons learned from the Gradoop effort. © The Author(s) 2021
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container_issue	2
title_short	Distributed temporal graph analytics with GRADOOP
url	https://doi.org/10.1007/s00778-021-00667-4
remote_bool	false
author2	Gomez, Kevin Täschner, Matthias Fritzsche, Philip Schons, Lucas Christ, Lukas Adameit, Timo Junghanns, Martin Rahm, Erhard
author2Str	Gomez, Kevin Täschner, Matthias Fritzsche, Philip Schons, Lucas Christ, Lukas Adameit, Timo Junghanns, Martin Rahm, Erhard
ppnlink	170933059
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doi_str	10.1007/s00778-021-00667-4
up_date	2024-07-03T19:37:26.756Z
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