Multi-level modeling (MLM) represents a significant extension to the traditional two-level object-oriented paradigm with the potential to dramatically improve upon the utility, reliability and complexity of models. Different from conventional approaches, they allow for an arbitrary number of classification levels and introduce other concepts that foster expressiveness, reuse and adaptability. A key aspect of the MLM paradigm is the use of entities that are simultaneously types and instances, a feature which has consequences for conceptual modeling, language engineering and for the development of model-based software systems.

The objectives of the MULTI series is to provide a forum for the MLM community to address the foundations of MLM approaches and support future modelers through better DSMLs, tools, methods and guidelines. The workshop will encourage the presentation of case studies and tool demonstrations. Furthermore, we consider organizing a practical challenge in order to compare different approaches to MLM. To achieve this goal, a significant proportion of time will be devoted to discussions. This will facilitate the development of a community consensus while giving participants a platform to discuss their individual ideas.

Possible topics include, but are not limited to:

• the nature of elements in a multi-level hierarchy and how best to represent them,
• the importance and role of deep characterization mechanisms, including potency and its variants such a durability and mutability,
• the structure and labelling of a MLM framework,
• methods and techniques for discovering clabjects, specializations and classification relationships,
• formal approaches to MLM,
• fundamental aspects of MLM such as composition and decomposition,
• experiences and challenges in providing tool support for MLM,
• experiences and challenges in applying MLM techniques to large and/or real-world problems,
• model management languages (transformation, code generation etc.) in a multi-level setting,
• criteria and approaches for comparing MLM approaches,
• integration of modelling and programming languages,
• definition of behavioral semantics in a multilevel setting,
• multi-level modeling for big data.

Four kinds of papers are solicited (in ACM format): regular papers (max. 10 pages), tool-demo papers (max. 5 pages), position papers (max. 5 pages), and Challenge papers (max 10 pages + up to 5 pages to include important model fragments which demonstrate the solution; please see the challenge description and its requirements here). 

Papers should be submitted as PDF files via Easychair. For formatting instructions see the Submission Guidelines of the Technical Track of MODELS 2020. Accepted papers will be published in the MODELS 2020 joint workshop proceedings (see here) in the ACM Digital Library and indexed in DBLP.