The objective of the half-day workshop CoMoNoS is to explore opportunities for conceptual modeling, addressing real-world problems that arise with NoSQL data stores (like MongoDB, Couchbase, Cassandra, or Neo4j). In designing an application backed by a NoSQL data store, developers face specific challenges that match the strengths of the ER community.

We aim at bringing together researchers and practitioners with an interest in the empirical investigation of conceptual modeling systems and practices. The workshop invites both reports on specific finished, on-going, or proposed empirical studies, as well as theoretical, review, and experience papers about empirical research in conceptual modeling. Examples of contributions include but are not limited to: Complete, on-going or planned empirical studies in Conceptual Modeling, Literature Reviews on empirical research in Conceptual Modeling, Theoretical/philosophical positions on empirical Conceptual Modeling, Discussions/positions on statistical and methodological issues, Lessons learned from past studies.

The objective of the CMLS workshop is to be a meeting point for Information Systems (IS), Conceptual Modeling (CM), and Data Management(DM) researchers working on health care and life science problems, and an opportunity to share, discuss and find new approaches to improve promising fields, with a special focus on Genomic Data Management - how to use the information from the genome to better understand biological and clinical features - and Precision Medicine - giving to each patient an individualized treatment by understanding the peculiar aspects of the disease.

The approaches to conceptual modelling as well as earlier approaches to AI have mainly been focusing on the manual engineering of models, which requires a great deal of time and money. Thus, depending on the application domain, these approaches scale up poorly. This workshop aims at exploring the huge potential in combining manual model engineering with data-driven Artificial Intelligence techniques which focus on automatic model generation from large amounts of data. We are particularly interested in contributions that show how such a combination can contribute to reducing time and effort of conceptual modelling, improving model quality, supporting model maintenance, or be beneficial in any other ways.

"The objective of the iStar workshop series is to provide a platform for the conceptual modeling community to exchange the latest ideas and research on goal modeling. We encourage researchers in the area to exchange ideas, compare notes, promote interactions, and forge new collaborations. Expected outcomes include the communication of early results and new ideas to fellow researchers for feedback, the identification of the current problems and promising future research directions, and the fostering of awareness, collaboration and interoperability in the area of tool development. "

In an increasingly complex and heterogeneous environment, significant effort is required to efficiently work with data and other digital objects. The Findable, Accessible, Interoperable and Reusable (FAIR) principles were elaborated to tackle these problems, describing a minimal set of requirements for data stewardship towards higher data reusability. In order to improve findability, accessibility, interoperability and reusability of different types of digital objects at scale, the FAIR principles gives emphasis to machine actionability. Therefore, a critical aspect to achieve this machine actionability is semantics. Proper semantics, formal semantics and machine-actionable formal semantics should be available to make "intelligible" for computational agents the elements of a FAIR data ecosystem. The goal of the CMOMM4FAIR workshop is to discuss challenges, solutions and impact of, for one side, the use of conceptual modeling and metadata and data management and, for the other side, the adoption of the FAIR principles to guide improvements in conceptual modeling.