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High-Level Programming Abstractions for Distributed Graph Processing
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.ORCID iD: 0000-0002-6718-0144
2018 (English)In: IEEE Transactions on Knowledge and Data Engineering, ISSN 1041-4347, E-ISSN 1558-2191, Vol. 30, no 2, p. 305-324Article in journal (Refereed) Published
Abstract [en]

Efficient processing of large-scale graphs in distributed environments has been an increasingly popular topic of research in recent years. Inter-connected data that can be modeled as graphs appear in application domains such as machine learning, recommendation, web search, and social network analysis. Writing distributed graph applications is inherently hard and requires programming models that can cover a diverse set of problems, including iterative refinement algorithms, graph transformations, graph aggregations, pattern matching, ego-network analysis, and graph traversals. Several high-level programming abstractions have been proposed and adopted by distributed graph processing systems and big data platforms. Even though significant work has been done to experimentally compare distributed graph processing frameworks, no qualitative study and comparison of graph programming abstractions has been conducted yet. In this survey, we review and analyze the most prevalent high-level programming models for distributed graph processing, in terms of their semantics and applicability. We review 34 distributed graph processing systems with respect to the graph processing models they implement and we survey applications that appear in recent distributed graph systems papers. Finally, we discuss trends and open research questions in the area of distributed graph processing.

Place, publisher, year, edition, pages
IEEE COMPUTER SOC , 2018. Vol. 30, no 2, p. 305-324
Keywords [en]
Distributed graph processing, large-scale graph analysis, big data
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-221918DOI: 10.1109/TKDE.2017.2762294ISI: 000422711800008Scopus ID: 2-s2.0-85040652305OAI: oai:DiVA.org:kth-221918DiVA, id: diva2:1179114
Note

QC 20180131

Available from: 2018-01-31 Created: 2018-01-31 Last updated: 2018-02-02Bibliographically approved

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Vlassov, VladimirHaridi, Seif

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  • apa
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