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Proceedings ArticleDOI

GraphScript: implementing complex graph algorithms in SAP HANA

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TLDR
GraphScript is introduced, a domain-specific graph query language tailored to serve advanced graph analysis tasks and the specification of complex graph algorithms that allows algorithm customization to the user's needs.
Abstract
Real-world graph applications are typically domain-specific and model complex business processes in the property graph data model. To implement a domain-specific graph algorithm in the context of such a graph application, simply providing a set of built-in graph algorithms is usually not sufficient nor does it allow algorithm customization to the user's needs. To cope with these issues, graph database vendors provide---in addition to their declarative graph query languages---procedural interfaces to write user-defined graph algorithms.In this paper, we introduce GraphScript, a domain-specific graph query language tailored to serve advanced graph analysis tasks and the specification of complex graph algorithms. We describe the major language design of GraphScript, discuss graph-specific optimizations, and describe the integration into an enterprise data platform.

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Citations
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Understanding Trolls with Efficient Analytics of Large Graphs in Neo4j

TL;DR: The design and integration of Neo4j Graph Algorithms are described, its utility is demonstrated with a Twitter Troll analysis, and its performance is shown with a few experiments on large graphs.
Proceedings ArticleDOI

Incremental View Maintenance for Property Graph Queries

TL;DR: OpenCypher as mentioned in this paper is a subset of the Cypher language, for which it currently provides grammar specification and a set of acceptance tests to allow vendors to implement their opencypher compatible engine.
Journal ArticleDOI

Discovering and merging related analytic datasets

TL;DR: A model and algorithms that exploit automatically extracted and user-defined semantic relationships for extending analytic datasets with new atomic or aggregated attribute values are described.
Journal ArticleDOI

Compact and efficient representation of general graph databases

TL;DR: In this article, the authors propose a compact data structure to store attributed graphs based on the $$k^2$$ -tree, which is designed to represent a simple directed graph, and evaluate the performance of their proposal with existing graph database systems and prove that their compact attributed graph representation obtains also competitive time results.
Journal ArticleDOI

Compact and Efficient Representation of General Graph Databases

TL;DR: This paper provides an implementation of a basic set of operations, which can be combined to form complex queries over these graphs with attributes, and proves that the compact attributed graph representation obtains also competitive time results.
References
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Proceedings ArticleDOI

Ligra: a lightweight graph processing framework for shared memory

TL;DR: This paper presents a lightweight graph processing framework that is specific for shared-memory parallel/multicore machines, which makes graph traversal algorithms easy to write and significantly more efficient than previously reported results using graph frameworks on machines with many more cores.
Proceedings Article

GraphLab: a new framework for parallel machine learning

TL;DR: The expressiveness of the GraphLab framework is demonstrated by designing and implementing parallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and Compressed Sensing and it is shown that using GraphLab the authors can achieve excellent parallel performance on large scale real-world problems.
Journal ArticleDOI

Efficiently compiling efficient query plans for modern hardware

TL;DR: This work presents a novel compilation strategy that translates a query into compact and efficient machine code using the LLVM compiler framework and integrates these techniques into the HyPer main memory database system and shows that this results in excellent query performance while requiring only modest compilation time.
Proceedings ArticleDOI

Green-Marl: a DSL for easy and efficient graph analysis

TL;DR: This paper describes Green-Marl, a domain-specific language (DSL) whose high level language constructs allow developers to describe their graph analysis algorithms intuitively, but expose the data-level parallelism inherent in the algorithms.
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