TemporalEMF: A Temporal Metamodeling Framework
- 1. TemporalEMF: A Temporal Metamodeling Framework Abel Gómez, Jordi Cabot, and Manuel Wimmer Internet Interdisciplinary Institute – Universitat Oberta de Catalunya, Spain CDL-MINT, TU Wien, Austria 1
- 2. Introduction • Modeling tools have improved drastically in the last decade… • … but temporal aspect have not received enough attention yet. • Typical solutions are built on top of (or rely concepts brought from) generic Version Control Systems: e.g. model versions/snapshots taken at user-defined times. • This approximate answer is not enough in scenarios that require a more precise and immediate response to temporal queries like complex collaborative co-engineering processes or runtime models. 2
- 3. TemporalEMF contributions • Light-weight extension of current metamodeling standards: • Models are automatically and transparently treated as temporal… • … but if the only state is of interest, models are accessed as usual. • Infrastructure to manage temporal models: • TemporalEMF is built on top the Eclipse Modeling Framework. • Models history is stored in a NoSQL database. • We outline a temporal query language • Allows retrieving historical information from models at any point in time. 3
- 5. A Profile for Temporal Metamodeling 5
- 6. A Profile for Temporal Metamodeling 6
- 7. A Profile for Temporal Metamodeling Now, we can compute execution states of interest (e.g. for provenance) and KPIs (such as utilization): • Q1 — Find all items which have been processed by machine m. • Q2 — Find the components which had an item assigned at a particular point in time. • Q3 —Find the components which had an item assigned within a particular time frame. • Q4 — Compute the utilization of machine e for the whole system execution lifecycle. 7
- 8. A Profile for Temporal Metamodeling Now, we can compute execution states of interest (e.g. for provenance) and KPIs (such as utilization): • Q1 — Find all items which have been processed by machine m. • Q2 — Find the components which had an item assigned at a particular point in time. • Q3 —Find the components which had an item assigned within a particular time frame. • Q4 — Compute the utilization of machine e for the whole system execution lifecycle. 8 Component.allInstances()->select(c | not c.hostsAt(instant).oclIsUndefined())
- 9. Approach 9
- 10. Bigtable • Map-based (i.e. key-value) stores are especially well-suited to persist models managed by (meta-)modeling frameworks. • BigTable is a distributed, scalable, versioned, non-relational and column- based big data store; where data is stored in tables, which are sparse, distributed, persistent, and multi-dimensional sorted maps. • These maps are indexed by the tuple row key, column key, and a timestamp. 10
- 11. Example 11
- 12. Example 12
- 13. Example 13
- 14. Example 14
- 15. Example 15
- 16. Example 16
- 17. Architecture 17
- 18. Architecture 18
- 20. Questions • RQ1: Production Cost — Is there a significant difference of the time required for producing and manipulating temporal model elements? • RQ2: Storage Cost — Is there a significant difference of the storage size of temporal models? • RQ3: Reproduction Cost — Is there a significant difference of restoring previous versions of temporal model elements? TemporalEMF vs Saving Historical Data Explicitly 20
- 21. RQ1 21
- 22. RQ2 22
- 23. RQ2 23
- 24. RQ3 24
- 25. RQ3 25
- 26. Related work and conclusions 26
- 27. Related work • Model versioning tools: • Typically, XMI serialization saved on CVS, SVN or Git. • Each version is a self-contained model instance. • Versions are generated by the user. • Lack of precision. • Fine grained historical data may require model comparison operations. • Models@run.time: • Provide a modeling infrastructure to instantiate models (as we do), they do not store the history of those changes. 27
- 28. Conclusions • Conceptual schemas are automatically and transparently treated as temporal models. • Can be subject to temporal queries to retrieve and compare the model contents at different points in time. • An extension to the standard EMF APIs allows modelers to easily express such temporal queries. • TemporalEMF relies on HBase to provide an scalable persistence layer to store all past versions. 28
- 29. TemporalEMF: A Temporal Metamodeling Framework Abel Gómez, Jordi Cabot, and Manuel Wimmer Internet Interdisciplinary Institute – Universitat Oberta de Catalunya, Spain CDL-MINT, TU Wien, Austria 29