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4 MONDO Consortium Partnersworld MDE case studies. We have made these cases available to the communityfor evaluation via a public MDE benchmark repository [26].Reactive Model Query and Transformation We propose a shift of paradigmfor programming MDE applications towards reactive programming [27], wherea network of transformations defines persistent data-flows among models. A reactivetransformation engine takes care of activating only the strictly neededcomputation in response to updates or requests of model elements, by a combinationof incremental and lazy computation. A reactive engine also opens theway to scenarios based on infinite intermediate models generated on demand, orstreaming models propagating from inputs to outputs.Distributed Model Query and Transformation in the Cloud We propose enginesthat implicitly (i.e., without explicit distribution primitives) distribute theexecution of model queries and transformations on top of well-known distributedprogramming models for the Cloud (e.g., MapReduce). We show how the executionsemantics of model queries and transformations can be aligned with parallelcomputation models [28].Prototypes for several components are already available: IncQuery-D, ascalable engine for distributed incremental model queries [29]; MONDO-SAM,a benchmarking framework [30]; VIATRA-CEP, a streaming transformationengine [31].5 Scalable Collaborative ModellingModel repositories such as CDO [32] and MORSA [33] are storage systemsfor modeling artefacts that are mostly focused on concurrent access over aclient-server infrastructure. They provide extension mechanisms and core APIsthat auxiliary, function-specific tools may use to support conflict management,branching, model comparison etc.Online collaborative modelling systems such as CoolModes [34] rely on a shorttransaction approach, whereby a single, shared instance of the model is editedby multiple users concurrently. These systems lack conflict management, or onlyprovide very light weight mechanisms (such as voluntary locking).Model versioning systems such as EMFStore [35] are more closely alignedwith offline version control systems such as SVN. They follow the long transactionapproach whereby contributors are assumed to commit larger portionsof work with respect to a certain (past) version as the reference. Hence, sinceconflicts are common, their detection, resolution and merging are features oftop importance. To that end, such sytems are frequently augmented with offlinemodel comparison, differencing and merging tools such as EMF Compare [36] orEMF Diff/Merge [37] are also often used.The key weaknesses of the collaborative modelling state of the art can be summarizedas follows: (i) immature integration of online and offline collaborationpatterns; (ii) mostly ad-hoc architectures that prohibit or make the implementationof domain-specific collaboration/version management difficult; (iii) very47
MONDO: Scalable Modelling and Model Management on the Cloud 5simplistic locking and conflict management solutions that severely hinder developerproductivity; (iv) the lack of a flexible and scalable back-end platform thatcaters to both Eclipse-based and other (commercial) tools.5.1 Research DirectionsWe are working on a multi-device collaborative modelling framework which onthe front-end is fully compatible with existing and future Eclipse-based technologies(EMF and its auxiliaries and the Team API); on the back-end, andintegrates into the scalable model persistence framework. It supports both offlineand online collaboration in a multi-user and multi-device environment, providinga model access layer (transaction management, queries, views and manipulation)featuring basic collaboration primitives (push, pull, commit, merge), andan adaptation layer for the integration of access control and security services.It is built on an extensible architecture that allows the integration of domainspecific,customized plugins for conflict management (detection, resolution andmerging).As novel and innovative features, it includes query-driven dynamic lockingthat uses complex graph queries [38] for the specification of locking partitionsfor views and manipulative transactions. Such queries operate in a collaborationawaremanner that includes support for real-time updates and locked queries(where updates are propagated only from a pre-defined subset of collaborationpartners). Additionally, the framework features automated conflict resolutionbased on design-space exploration techniques [39] that are able to ensure domainconsistency and well-formedness by automatically applying model manipulationpolicies to find valid and conflict-free model states. Prototypes for both the collaborationframework and the model merger based on design space explorationare already available under http://github.com/FTSRG/mondo-collab-frameworkand http://github.com/FTSRG/mondo-collab-mergespaceexploration.6 Scalable Model PersistenceAn essential component of scalable MDE is infrastructure that facilitates persistenceand retrieval or large models in an efficient manner.Efficient Model Storage The current standard model storage format is the XMLMetadata Interchange. As XMI is an XML-based format, in order to accessany model elements using current state-of-the-art modelling frameworks such asEMF, the complete model file needs to be parsed and loaded in memory first.This implies that the larger the model file, the more time and memory is neededin order to load the model. Also, XMI inherits the verbosity of XML which meansthat XMI-encoded model files are much larger in size than needed in order tostore the information they do.To address these issues, we envision a new efficient model representationformat that will reduce the size of model files, enable modelling and model48
Page 1 and 2: Proceedings of the Projects Showcas
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Page 5 and 6: Table of ContentsARTIST: Model-Base
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Page 9 and 10: FeasibilityanalysisReverseengineeri
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Page 23 and 24: OCCIware - A Formal and Tooled Fram
Page 25 and 26: and Linked Open Data as a Service.
Page 27 and 28: The OCCIware engineering studio (se
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Page 31 and 32: 24. Nyrén, R., Edmonds, A., Papasp
Page 33 and 34: 2 Alfonso Pierantonio, Gianni Rosa,
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Page 45 and 46: in text mining. Peer users are the
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