Towards robust distributed systems (abstract)
Proceedings of the nineteenth annual ACM symposium on Principles of distributed computing
Data mining and statistical analysis using SQL
Data mining and statistical analysis using SQL
Simple and realistic data generation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Graphs-at-a-time: query language and access methods for graph databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
MDX Reporting and Analytics with SAP NetWeaver BW
MDX Reporting and Analytics with SAP NetWeaver BW
A comparison of a graph database and a relational database: a data provenance perspective
Proceedings of the 48th Annual Southeast Regional Conference
Survey of graph database performance on the HPC scalable graph analysis benchmark
WAIM'10 Proceedings of the 2010 international conference on Web-age information management
10 rules for scalable performance in 'simple operation' datastores
Communications of the ACM
Scalable SQL and NoSQL data stores
ACM SIGMOD Record
Semantics and complexity of SPARQL
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
A Comparison of Current Graph Database Models
ICDEW '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering Workshops
Benchmarking Traversal Operations over Graph Databases
ICDEW '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering Workshops
Converting relational to graph databases
First International Workshop on Graph Data Management Experiences and Systems
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NoSQL and especially graph databases are constantly gaining popularity among developers of Web 2.0 applications as they promise to deliver superior performance when handling highly interconnected data compared to traditional relational databases. Apache Shindig is the reference implementation for OpenSocial with its highly interconnected data model. However, the default back-end is based on a relational database. In this paper we describe our experiences with a different back-end based on the graph database Neo4j and compare the alternatives for querying data with each other and the JPA-based sample back-end running on MySQL. Moreover, we analyze why the different approaches often may yield such diverging results concerning throughput. The results show that the graph-based back-end can match and even outperform the traditional JPA implementation and that Cypher is a promising candidate for a standard graph query language, but still leaves room for improvements.