Principles of transaction-oriented database recovery
ACM Computing Surveys (CSUR)
EMF: Eclipse Modeling Framework 2.0
EMF: Eclipse Modeling Framework 2.0
Bigtable: a distributed storage system for structured data
OSDI '06 Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation - Volume 7
Dynamo: amazon's highly available key-value store
Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Scalaris: reliable transactional p2p key/value store
Proceedings of the 7th ACM SIGPLAN workshop on ERLANG
Cassandra: structured storage system on a P2P network
Proceedings of the 28th ACM symposium on Principles of distributed computing
CouchDB: The Definitive Guide Time to Relax
CouchDB: The Definitive Guide Time to Relax
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
Model driven language engineering with kermeta
GTTSE'09 Proceedings of the 3rd international summer school conference on Generative and transformational techniques in software engineering III
Morsa: a scalable approach for persisting and accessing large models
Proceedings of the 14th international conference on Model driven engineering languages and systems
Proceedings of the 1st International Workshop on Model-Driven Engineering for High Performance and CLoud computing
SAM'12 Proceedings of the 7th international conference on System Analysis and Modeling: theory and practice
Reference representation techniques for large models
Proceedings of the Workshop on Scalability in Model Driven Engineering
EMF modeling in traffic surveillance experiments
Proceedings of the Modelling of the Physical World Workshop
Querying large models efficiently
Information and Software Technology
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Existing model persistence frameworks either store models as a whole or object by object. Since most modeling tasks work with larger aggregates of a model, existing persistence frameworks either load too many objects or access many objects individually. We propose to persist a model broken into larger fragments. First, we assess the size of large models and describe typical usage patterns to show that most applications work with aggregates of model objects. Secondly, we provide an analytical framework to assess execution time gains for partially loading models fragmented with different granularity. Thirdly, we propose meta-model-based fragmentation that we implemented in an EMF based framework. Fourthly, we analyze our approach in comparison to other persistence frameworks based on four common modeling tasks: create/modify, traverse, query, and partial loads. We show that there is no generally optimal fragmentation, that fragmentation can be achieved automatically and transparently, and that fragmentation provides considerable performance gains.