Analyzing extended property graphs with Apache Flink

Junghanns, Martin; Petermann, André; Teichmann, Niklas; Gómez, Kevin; Rahm, Erhard

doi:10.1145/2980523.2980527

Cited by 27 publications

(27 citation statements)

References 14 publications

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“…Figure 2 shows the Gradoop architecture. Analytical programs are defined within our Graph Analytical Language (GrALa), which is a domain specific language for the Extended Property Graph Model (EPGM) [5]. GrALa contains operators for accessing graphs in the underlying storage as well as for applying graph operations and analytical graph algorithms to them.…”

Section: The Gradoop Systemmentioning

confidence: 99%

“…For our current proof of concept, we chose Apache Flink, a distributed batch and stream processing framework, that allows executing arbi- Extended Property Graph Model. The EPGM [5] describes how graphs and their elements (vertices and edges) are structured within Gradoop. It is an extension of the property graph model [13], which is used in various graph database systems.…”

Section: The Gradoop Systemmentioning

confidence: 99%

“…The latter are exposed to the user and can be accessed within graph operators. For enhanced analytical expressiveness, the EPGM supports handling of multiple, possibly overlapping graphs within a single analytical program [5]. Graphs, as well as vertices and edges, are first-class citizens of the data model and can have their own properties.…”

Section: The Gradoop Systemmentioning

confidence: 99%

“…• The Extended Property Graph Model [5] to support heterogeneous graphs as well as collections of those. • Composable graph operators, including analytical operators such as Cypher-based graph pattern matching [6] and graph grouping [8] as well as general operators for data transformation and aggregation.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Declarative and distributed graph analytics with GRADOOP

Junghanns¹,

Kiessling²,

Teichmann³

et al. 2018

Proc. VLDB Endow.

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We demonstrate Gradoop, an open source framework that combines and extends features of graph database systems with the benefits of distributed graph processing. Using a rich graph data model and powerful graph operators, users can declaratively express graph analytical programs for distributed execution without needing advanced programming experience or a deeper understanding of the underlying system. Visitors of the demo can declare graph analytical programs using the Gradoop operators and also visually experience two of our advanced operators: graph pattern matching and graph grouping. We provide real world and artificial social network data with up to 10 billion edges and allow running the programs either locally or on a remote research cluster to demonstrate scalability.

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Section: The Gradoop Systemmentioning

confidence: 99%

Section: The Gradoop Systemmentioning

confidence: 99%

Section: The Gradoop Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Declarative and distributed graph analytics with GRADOOP

Junghanns¹,

Kiessling²,

Teichmann³

et al. 2018

Proc. VLDB Endow.

Self Cite

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“…A similar approach was presented in [11], [12]. The authors propose graph collections that are logical partitions of a graph called logical graphs.…”

Section: Introductionmentioning

confidence: 99%

Named Property Graphs

Tomaszuk

Szeremeta

2018

Annals of Computer Science and Information Systems

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The amount of information that is stored and processed by computer systems is constantly increasing. The relational model is still popular. Unfortunately, despite its simplicity, it has many disadvantages, which more often exclude it from large-scale applications. The property graph model seems to be a good alternative for describing real world data with its relationships. Therefore, property graph based databases become more and more popular every day. In this paper we introduce Named Property Graph model that allows to group graphs into separate units and describe information about them. We also present Cyphern query language that supports our proposal, mapping algorithms, use cases with the chemical data, and SDFEater that is our tool for processing data. Presented solutions are fully backward compatible with existing databases.

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MAGMA: Proposing a Massive Historical Graph Management System

Spitalas

Tsichlas

2023

Lecture Notes in Computer Science

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Analyzing extended property graphs with Apache Flink

Cited by 27 publications

References 14 publications

Declarative and distributed graph analytics with GRADOOP

Declarative and distributed graph analytics with GRADOOP

Named Property Graphs

MAGMA: Proposing a Massive Historical Graph Management System

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