GraphX: Unifying Data-Parallel and Graph-Parallel Analytics

From social networks to language modeling, the growing scale and importance of graph data has driven the development of numerous new graph-parallel systems (e.g., Pregel, GraphLab). By restricting the computation that can be expressed and introducing new techniques to partition and distribute the graph, these systems can efficiently execute iterative graph algorithms orders of magnitude faster than more general data-parallel systems. However, the same restrictions that enable the performance gains also make it difficult to express many of the important stages in a typical graph-analytics pipeline: constructing the graph, modifying its structure, or expressing computation that spans multiple graphs. As a consequence, existing graph analytics pipelines compose graph-parallel and data-parallel systems using external storage systems, leading to extensive data movement and complicated programming model. To address these challenges we introduce GraphX, a distributed graph computation framework that unifies graph-parallel and data-parallel computation. GraphX provides a small, core set of graph-parallel operators expressive enough to implement the Pregel and PowerGraph abstractions, yet simple enough to be cast in relational algebra. GraphX uses a collection of query optimization techniques such as automatic join rewrites to efficiently implement these graph-parallel operators. We evaluate GraphX on real-world graphs and workloads and demonstrate that GraphX achieves comparable performance as specialized graph computation systems, while outperforming them in end-to-end graph pipelines. Moreover, GraphX achieves a balance between expressiveness, performance, and ease of use

MapReduce-based Solutions for Scalable SPARQL Querying

The use of RDF to expose semantic data on the Web has seen a dramatic increase over the last few years. Nowadays, RDF datasets are so big and rconnected that, in fact, classical mono-node solutions present significant scalability problems when trying to manage big semantic data. MapReduce, a standard framework for distributed processing of great quantities of data, is earning a place among the distributed solutions facing RDF scalability issues. In this article, we survey the most important works addressing RDF management and querying through diverse MapReduce approaches, with a focus on their main strategies, optimizations and results

A scale-out RDF molecule store for distributed processing of biomedical data

The computational analysis of protein-protein interaction and biomolecular pathway data paves the way to efficient in silico drug discovery and therapeutic target identification. However, relevant data sources are currently distributed across a wide range of disparate, large-scale, publicly-available databases and repositories and are described using a wide range of taxonomies and ontologies. Sophisticated integration, manipulation, processing and analysis of these datasets are required in order to reveal previously undiscovered interactions and pathways that will lead to the discovery of new drugs. The BioMANTA project focuses on utilizing Semantic Web technologies together with a scale-out architecture to tackle the above challenges and to provide efficient analysis, querying, and reasoning about protein-protein interaction data. This paper describes the initial results of the BioMANTA project. The fully-developed system will allow knowledge representation and processing that are not currently available in typical scale-out or Semantic Web databases. We present the design of the architecture, basic ontology and some implementation details that aim to provide efficient, scalable RDF storage and inferencing. The results of initial performance evaluation are also provided

Processing SPARQL Queries Over Distributed RDF Graphs

We propose techniques for processing SPARQL queries over a large RDF graph in a distributed environment. We adopt a "partial evaluation and assembly" framework. Answering a SPARQL query Q is equivalent to finding subgraph matches of the query graph Q over RDF graph G. Based on properties of subgraph matching over a distributed graph, we introduce local partial match as partial answers in each fragment of RDF graph G. For assembly, we propose two methods: centralized and distributed assembly. We analyze our algorithms from both theoretically and experimentally. Extensive experiments over both real and benchmark RDF repositories of billions of triples confirm that our method is superior to the state-of-the-art methods in both the system's performance and scalability.Comment: 30 page

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them