Map/Reduce on EMF Models

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Abstract—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.

KeywordsEMF, big data, cloud computing, map/reduce, meta-modeling

 @inproceedings{Scheidgen:2012:MEM:2446224.2446231,
 author = {Scheidgen, Markus and Zubow, Anatolij},
 title = {Map/reduce on EMF models},
 booktitle = {Proceedings of the 1st International Workshop on Model-Driven Engineering for High Performance and CLoud computing},
 series = {MDHPCL '12},
 year = {2012},
 isbn = {978-1-4503-1810-5},
 location = {Innsbruck, Austria},
 pages = {7:1--7:5},
 articleno = {7},
 numpages = {5},
 url = {http://doi.acm.org/10.1145/2446224.2446231},
 doi = {10.1145/2446224.2446231},
 acmid = {2446231},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {EMF, big data, cloud computing, map/reduce, meta-modeling},
}

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