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A conceptual model for event-sourced graph computing

Authors:

Benjamin Erb,

Frank KarglAuthors Info & Claims

DEBS '15: Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems

Pages 352 - 355

https://doi.org/10.1145/2675743.2776773

Published: 24 June 2015 Publication History

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Abstract

Systems for highly interconnected application domains are increasingly taking advantage of graph-based computing platforms. Existing platforms employ a batch-oriented computing model and neglect near-realtime processing or temporal analysis. We suggest an extended conceptual model for event-driven computing on graphs. It takes into account the evolution of a graph and enables temporal analyses, processing on previous graph states, and retroactive modifications.

References

[1]

G. Agha. Actors: A Model of Concurrent Computation in Distributed Systems. MIT Press, Cambridge, MA, USA, 1986.

Digital Library

Google Scholar

[2]

R. Cheng, J. Hong, A. Kyrola, Y. Miao, X. Weng, M. Wu, F. Yang, L. Zhou, F. Zhao, and E. Chen. Kineograph: Taking the pulse of a fast-changing and connected world. In Proceedings of the 7th ACM European Conference on Computer Systems, EuroSys '12, pages 85--98, New York, NY, USA, 2012. ACM.

Digital Library

Google Scholar

[3]

G. Cugola and A. Margara. Processing flows of information: From data stream to complex event processing. ACM Comput. Surv., 44(3):15:1--15:62, June 2012.

Digital Library

Google Scholar

[4]

R. C. Fernandez, P. Pietzuch, J. Kreps, N. Narkhede, J. Rao, J. Koshy, D. Lin, C. Riccomini, and G. Wang. Liquid: Unifying nearline and offline big data integration. In CIDR 2015, Seventh Biennial Conference on Innovative Data Systems Research, Asilomar, CA, USA, January 4--7, 2015, Online Proceedings, 2015.

Google Scholar

[5]

M. Fowler. Event sourcing. http://martinfowler.com/eaaDev/EventSourcing.html, 2005.

Google Scholar

[6]

M. Fowler. CQRS. http://martinfowler.com/bliki/CQRS.html, 2011.

Google Scholar

[7]

J. E. Gonzalez, Y. Low, H. Gu, D. Bickson, and C. Guestrin. Powergraph: Distributed graph-parallel computation on natural graphs. In Proceedings of the 10th USENIX Conference on Operating Systems Design and Implementation, OSDI'12, pages 17--30, Berkeley, CA, USA, 2012. USENIX Association.

Digital Library

Google Scholar

[8]

W. Han, Y. Miao, K. Li, M. Wu, F. Yang, L. Zhou, V. Prabhakaran, W. Chen, and E. Chen. Chronos: A graph engine for temporal graph analysis. In Proceedings of the Ninth European Conference on Computer Systems, EuroSys '14, pages 1:1--1:14, New York, NY, USA, 2014. ACM.

Digital Library

Google Scholar

[9]

G. Hohpe. Programming without a call stack - event-driven architectures. Technical report, eaipatterns.com, 2006.

Google Scholar

[10]

G. Malewicz, M. H. Austern, A. J. Bik, J. C. Dehnert, I. Horn, N. Leiser, and G. Czajkowski. Pregel: A system for large-scale graph processing. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, SIGMOD '10, pages 135--146, New York, NY, USA, 2010. ACM.

Digital Library

Google Scholar

[11]

N. Marz and J. Warren. Big Data: Principles and best practices of scalable realtime data systems. Manning Publications, 2013.

Digital Library

Google Scholar

[12]

Y. Simmhan, A. Kumbhare, C. Wickramaarachchi, S. Nagarkar, S. Ravi, C. Raghavendra, and V. Prasanna. Goffish: A sub-graph centric framework for large-scale graph analytics. In F. Silva, I. Dutra, and V. Santos Costa, editors, Euro-Par 2014 Parallel Processing, volume 8632 of Lecture Notes in Computer Science, pages 451--462. Springer International Publishing, 2014.

Google Scholar

[13]

G. Young. CQRS documents. Technical report, cqrsinfo.com, 2010.

Google Scholar

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Merelo-Guervós JBlancas-Álvarez ICastillo PRomero GRivas VGarcía-Valdez MHernández-Águila ARomáin M(2021)A comparison of implementations of basic evolutionary algorithm operations in different languages2016 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2016.7743980(1602-1609)Online publication date: 11-Mar-2021
https://dl.acm.org/doi/10.1109/CEC.2016.7743980
Vasconcellos PBezerra VBianchini C(2018)Applying Event Sourcing in a ERP System: A Case Study2018 XLIV Latin American Computer Conference (CLEI)10.1109/CLEI.2018.00019(80-89)Online publication date: Oct-2018
https://doi.org/10.1109/CLEI.2018.00019
Erb BMeißner DPietron JKargl F(2017)ChronographProceedings of the 11th ACM International Conference on Distributed and Event-based Systems10.1145/3093742.3093913(78-87)Online publication date: 8-Jun-2017
https://dl.acm.org/doi/10.1145/3093742.3093913
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Index Terms

A conceptual model for event-sourced graph computing
1. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles
      2. Software architectures

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Published In

DEBS '15: Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems

June 2015

385 pages

ISBN:9781450332866

DOI:10.1145/2675743

General Chairs:
Frank Eliassen
University of Oslo, Norway
,
Roman Vitenberg
University of Oslo, Norway

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 ACM 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

New York, NY, United States

Publication History

Published: 24 June 2015

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DEBS '15

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DEBS '15: The 9th ACM International Conference on Distributed Event-Based Systems

June 29 - July 3, 2015

Oslo, Norway

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Overall Acceptance Rate 145 of 583 submissions, 25%

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Cited By

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Merelo-Guervós JBlancas-Álvarez ICastillo PRomero GRivas VGarcía-Valdez MHernández-Águila ARomáin M(2021)A comparison of implementations of basic evolutionary algorithm operations in different languages2016 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2016.7743980(1602-1609)Online publication date: 11-Mar-2021
https://dl.acm.org/doi/10.1109/CEC.2016.7743980
Vasconcellos PBezerra VBianchini C(2018)Applying Event Sourcing in a ERP System: A Case Study2018 XLIV Latin American Computer Conference (CLEI)10.1109/CLEI.2018.00019(80-89)Online publication date: Oct-2018
https://doi.org/10.1109/CLEI.2018.00019
Erb BMeißner DPietron JKargl F(2017)ChronographProceedings of the 11th ACM International Conference on Distributed and Event-based Systems10.1145/3093742.3093913(78-87)Online publication date: 8-Jun-2017
https://dl.acm.org/doi/10.1145/3093742.3093913
Merelo-Guervós JBlancas-Álvarez ICastillo PRomero GGarcía-Sánchez PRivas VGarcía-Valdez MHernández-Águila ARomán M(2017)Ranking Programming Languages for Evolutionary Algorithm OperationsApplications of Evolutionary Computation10.1007/978-3-319-55849-3_44(689-704)Online publication date: 25-Mar-2017
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Namiot DSneps-Sneppe M(2017)On Internet of Things Programming ModelsDistributed Computer and Communication Networks10.1007/978-3-319-51917-3_2(13-24)Online publication date: 14-Feb-2017
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Erb BKargl F(2016)ChronographProceedings of the Posters and Demos Session of the 17th International Middleware Conference10.1145/3007592.3007601(15-16)Online publication date: 12-Dec-2016
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Erb BHabiger GHauck F(2016)On the Potential of Event Sourcing for Retroactive Actor-based ProgrammingFirst Workshop on Programming Models and Languages for Distributed Computing10.1145/2957319.2957378(1-5)Online publication date: 17-Jul-2016
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Merelo JCastillo PBlancas IRomero GGarcía-Sanchez PFernández-Ares ARivas VGarcía-Valdez M(2016)Benchmarking Languages for Evolutionary AlgorithmsApplications of Evolutionary Computation10.1007/978-3-319-31153-1_3(27-41)Online publication date: 2-Apr-2016
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