EMF: Eclipse Modeling Framework 2.0
EMF: Eclipse Modeling Framework 2.0
A comparison of approaches to large-scale data analysis
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Nephele/PACTs: a programming model and execution framework for web-scale analytical processing
Proceedings of the 1st ACM symposium on Cloud computing
Pregel: a system for large-scale graph processing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
The Definitive Guide to MongoDB: The NoSQL Database for Cloud and Desktop Computing
The Definitive Guide to MongoDB: The NoSQL Database for Cloud and Desktop Computing
ASTERIX: towards a scalable, semistructured data platform for evolving-world models
Distributed and Parallel Databases
Morsa: a scalable approach for persisting and accessing large models
Proceedings of the 14th international conference on Model driven engineering languages and systems
A wireless mesh sensing network for early warning
Journal of Network and Computer Applications
Proceedings of the 15th International Conference on Extending Database Technology
15th International Conference on Extending Database Technology
Proceedings of the 15th International Conference on Extending Database Technology
15th International Conference on Extending Database Technology
Automated and transparent model fragmentation for persisting large models
MODELS'12 Proceedings of the 15th international conference on Model Driven Engineering Languages and Systems
Hawk: towards a scalable model indexing architecture
Proceedings of the Workshop on Scalability in Model Driven Engineering
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Map/Reduce is the programming model in cloud computing. It enables the processing of data sets of unprecedented size, but it also delegates the handling of complex data structures completely to its users. In this paper, we apply Map/Reduce to EMF-based models to cope with complex data structures in the familiar an easy-to-use and type-safe EMF fashion, combining the advantages of both technologies. We use our framework EMF-Fragments to store very large EMF models in distributed key-value stores (Hadoop's Hbase). This allows us to build Map/Reduce programs that use EMF's generated APIs to process those very large EMF-models. We present our framework and two example Map/Reduce jobs for querying software models and for analyzing sensor data represented as EMF-models.