This document summarizes the TemporalEMF approach for managing temporal aspects of models. The key points are: 1. TemporalEMF provides a lightweight extension to modeling standards that treats models and their elements as temporal, allowing automatic management of model history without requiring changes to modeling tools or processes. 2. It implements a new infrastructure built on Eclipse Modeling Framework and a NoSQL database to store the temporal evolution of models. 3. The approach defines a temporal query language to retrieve historical information from models at different points in time, addressing current limitations of modeling tools around temporal aspects.

1. TemporalEMF
@softmodeling – jordicabot.com June 2019
Abel Gómez, Jordi Cabot, Manuel Wimmer. ER 2018:
365-381

2. Why?

3. Modeling tools have
improved a lot but temporal
aspects are still neglected

4. Right now, Simulated
by using VCS
1. Doesn’t scale (every time
we save the whole model)
2. Must be triggered on-
demand
3. No fine-grained
management of individual
elements (e.g. Queries)

5. Doesn’t work at all for current needs
(e.g. Big Data Apps)

6. Even if we have plenty of approaches
at the model level

7. A simple temporal model
<<temporal >>
WorksIn
{ Durability=durable,
Frequency=intermittent}
<<temporal >>
Employee
{ Durability=durable,
Frequency=intermittent}
<<temporal>>
Project
{ Durability=durable,
Frequency=single}* *

8. Contribution

9. TemporalEMF contributions
• Light-weight extension of current metamodeling standards:
– Models are automatically and transparently treated as temporal
(“normal” access performed as usual)
• Implemented in a new 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 OCL-like query language to retrieve historical
information from models at any point in time.
9

10. Applications
• Collaborative modeling where you want to
revise past design decisions and/or constraint
how the model can be evolved
• Model-based Simulation where you want to
model different scenarios, run them and study
which ones performed the best
10

11. Temporal
metamodeling

12. A Profile for Temporal Metamodeling
12
Combines our previous Temporal UML work with
the concept of EMF Profiles

13. A Transportation Line Metamodel
Simulator – Smart Production Systems
13
CDL-MINT is a multi-year research effort towards liquid models of cyber-physical production
systems. https://cdl-mint.big.tuwien.ac.at/

14. …enables a temporal analysis
Now, we can compute execution states of
interest (e.g. for provenance) and KPIs:
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.
14

15. …that can be expressed with Temporal OCL
15
Component.allInstances()->select(c | not
c.hostsAt(instant).oclIsUndefined())
Q2 - Find the components which had an item
assigned at a particular point in time.

16. Technical Approach

17. Based on Bigtable
• Map-based (i.e. key-value) stores are especially
well-suited to persist models (for direct accesses).
• Data is stored in tables, which are sparse,
distributed, and multi-dimensional sorted maps.
• These maps are indexed by the tuple row key,
column key, and a timestamp.
17
Alternative mappings best suited for other scenarios: NeoEMF: A multi-
database model persistence framework for very large models. Sci. Comput.
Program. 149: 9-14 (2017)

18. Models in Bigtable
• Our proposed data model flattens the typical graph structure
expressed by models into a set of key-value mappings that fit
Bigtable map-based data model
• Our proposed data model uses a single table with three column
families to store models’ information:
– a property column family, that keeps all objects’ data stored together;
– a type column family, that tracks how objects interact with the meta-
level (such as the instance of relationships); and
– a containment column family, that defines the models’ structure in
terms of containment
18

19. Example
19

20. Example
20

21. Example
21

22. Example
22

23. Example
23

24. Temporal OCL translation
24
a1.getComponent ().stream() .filter(
c -> c.eGetAt(instant ,
TllPackage.eINSTANCE.getComponent_Hosts
())!=null).collect(Collectors.toSet ());

25. Arquitecture

26. On top of Apache HBase
26
TemporalEMF, available as OSS, can be directly plugged into
any EMF-based tool to immediately provide enhanced temporal
support

27. Experiments

28. Questions (TemporalEMF vs Saving Historical
Data Explicitly)
• RQ1: Manipulation Cost — Is there a significant
difference of the time required for producing and
manipulating temporal model elements?
• RQ2: Storage Cost — Is there a significant time
difference when saving the temporal models?
• RQ3: Reproduction Cost — Is there a significant
difference of restoring/querying previous versions
of temporal elements?
28

29. RQ1 – Manipulation Cost
29
Initial small overhead.
Irrelevant in most cases

30. RQ2 – Storage Cost
30
Out of
memory
Very low memory
footprint

31. RQ2 – Storage Cost
31
Performance does
not deteriorates

32. RQ3 – Reproduction Cost
32

33. RQ3 – Reproduction Cost
33

34. Conclusions &
Further work

35. Summary
• Domain (meta)models are automatically and
transparently treated as temporal models.
• Allowing 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.
35

36. Further work
• Definition of temporal patterns that cover most
common types of temporal queries
• Add prediction capabilities based on the analysis of the
temporal data
– E.g. to adapt the tool interface based on the modeling
profile of a user
• Towards a full temporal modeling tool, e.g. adding
default temporal support to any model manipulation
operation
36