International Workshop on Analytics and Mining of Model Repositories (AMMoRe) @ MODELS ’18

14-16 October 2018
Copenhagen, Denmark

Model-based approaches promote the use of models and related artefacts (such as metamodels and model transformations) as central elements to tackle the complexity of building systems. Both in academia and in industry there is a growing need to efficiently i) store; ii) analyse; and ii) search & navigate, and iii) curate large collections of models. Such collections include for example large sets of software models such as the Lindholmen UML dataset, or of heterogeneous models in large MDE ecosystems and systems-of-systems, including e.g. software, hardware, and business models.

The workshop Analytics and Mining of Model Repositories (AMMoRe) aims to gather modelling researchers and practitioners to discuss the emerging problems and propose solutions. The scope ranges from industrial reports and empirical analyses in the problem domain to novel cross-disciplinary approaches for large-scale analytics and management, e.g. exploiting techniques from data analytics, repository mining and machine learning.

Important Dates:

Paper Submission: July 17, 2018
Authors Notification: August 17, 2018
Camera Ready and Registration: August 21, 2018
AMMoRe date: October 16, 2018


Big data approaches are causing large changes in the way we can perform science and business. Big Data is also entering the arena of software engineering and software modelling. We want to bring together the communities of Big Data/Machine Learning and Software Modelling. Various datasets of models have now become available and now our community must learn methods, techniques and tools for analysing these large datasets.

Many such methods, techniques and tools are known from the Big Data/Machine Learning and Information Retrieval/Natural Language Processing communities. How they can be adapted and applied to models and model repositories is an open question. Conversely, the insights that come out of this may lead to insights for these communities that are usable beyond software modelling.

Topics of interest (non-exclusive)

– Industrial reports and empirical studies on scalability in modelling;
– Identification of open research challenges and proposed solutions;
– Repository mining and management for modelling artefacts;
– Clone, pattern, aspect mining for modelling artefacts;
– Applications of exploratory or descriptive data analytics;
– Applications of predictive analytics, machine learning or deep-learning;
– Large-scale model management and consistency checking;
– Natural language processing for modelling;
– Model searching, indexing, retrieval, storage;
– Linking model-repositories;
– Visualization of (possibly heterogeneous) large sets of modelling artefacts;
– Techniques to analyse and automate (co-)evolution in modelling;
– Variability mining and management, model-driven software product lines;
– Distributed computing for modelling, with an eye towards Big Data;
– Intelligent techniques for automating modelling tasks;
– Building and composing model-analytics workflows (based on online services/repositories);
– Enriching/labelling in model-repositories.

We invite contributions from a wide range of technical spaces to promote cross-fertilization: Model-Driven Engineering, Systems Engineering, Business Process Modelling, Software Architecture, Data Mining, Machine Learning, Information Retrieval and so on.

Submission And Publication:


Program Chairs:

Önder Babur (Eindhoven University of Technology, NL)
Michel Chaudron (Chalmers | Gothenburg University, SE)
Loek Cleophas (Eindhoven University of Technology, NL; Stellenbosch University, ZA)
Davide Di Ruscio (University of L’Aquila, IT)
Dimitris Kolovos (University of York, UK)

Program Committee: