149 research outputs found
Vertexica: your relational friend for graph analytics!
In this paper, we present Vertexica, a graph analytics tools on top of a relational database, which is user friendly and yet highly efficient. Instead of constraining programmers to SQL, Vertexica offers a popular vertex-centric query interface, which is more natural for analysts to express many graph queries. The programmers simply provide their vertex-compute functions and Vertexica takes care of efficiently executing them in the standard SQL engine. The advantage of using Vertexica is its ability to leverage the relational features and enable much more sophisticated graph analysis. These include expressing graph algorithms which are difficult in vertex-centric but straightforward in SQL and the ability to compose end-to-end data processing pipelines, including pre- and post- processing of graphs as well as combining multiple algorithms for deeper insights. Vertexica has a graphical user interface and we outline several demonstration scenarios including, interactive graph analysis, complex graph analysis, and continuous and time series analysis
GRAPHiQL: A graph intuitive query language for relational databases
Graph analytics is becoming increasingly popular, driving many important business applications from social network analysis to machine learning. Since most graph data is collected in a relational database, it seems natural to attempt to perform graph analytics within the relational environment. However, SQL, the query language for relational databases, makes it difficult to express graph analytics operations. This is because SQL requires programmers to think in terms of tables and joins, rather than the more natural representation of graphs as collections of nodes and edges. As a result, even relatively simple graph operations can require very complex SQL queries. In this paper, we present GRAPHiQL, an intuitive query language for graph analytics, which allows developers to reason in terms of nodes and edges. GRAPHiQL provides key graph constructs such as looping, recursion, and neighborhood operations. At runtime, GRAPHiQL compiles graph programs into efficient SQL queries that can run on any relational database. We demonstrate the applicability of GRAPHiQL on several applications and compare the performance of GRAPHiQL queries with those of Apache Giraph (a popular `vertex centric' graph programming language)
Pregelix: Big(ger) Graph Analytics on A Dataflow Engine
There is a growing need for distributed graph processing systems that are
capable of gracefully scaling to very large graph datasets. Unfortunately, this
challenge has not been easily met due to the intense memory pressure imposed by
process-centric, message passing designs that many graph processing systems
follow. Pregelix is a new open source distributed graph processing system that
is based on an iterative dataflow design that is better tuned to handle both
in-memory and out-of-core workloads. As such, Pregelix offers improved
performance characteristics and scaling properties over current open source
systems (e.g., we have seen up to 15x speedup compared to Apache Giraph and up
to 35x speedup compared to distributed GraphLab), and makes more effective use
of available machine resources to support Big(ger) Graph Analytics
Enabling On-Demand Database Computing with MIT SuperCloud Database Management System
The MIT SuperCloud database management system allows for rapid creation and
flexible execution of a variety of the latest scientific databases, including
Apache Accumulo and SciDB. It is designed to permit these databases to run on a
High Performance Computing Cluster (HPCC) platform as seamlessly as any other
HPCC job. It ensures the seamless migration of the databases to the resources
assigned by the HPCC scheduler and centralized storage of the database files
when not running. It also permits snapshotting of databases to allow
researchers to experiment and push the limits of the technology without
concerns for data or productivity loss if the database becomes unstable.Comment: 6 pages; accepted to IEEE High Performance Extreme Computing (HPEC)
conference 2015. arXiv admin note: text overlap with arXiv:1406.492
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