MoKMaSD 2015 - 4th International Symposium on Modelling and Knowledge Management applications: Systems and Domains

MoKMaSD 2015 aims at bringing together practitioners and researchers from academia, industry and research institutions to present research results and exchange experience, ideas, and solutions for modelling and analysing complex systems and using knowledge management and discovery methodologies in various domain areas such as social systems, ecology, biology, medicine, smart cities, governance, education and social software engineering.
Papers that present a synergistic approach that integrates modelling and knowledge management/discovery or exploit knowledge management and discovery to develop/syntesise system models are especially welcome.
Authors are invited to submit original research or tool papers on any relevant topic. These can either be normal or short papers. Short papers can discuss new ideas which are at an early stage of development and which have not yet been thoroughly evaluated.
TOPICS OF INTEREST
Methodologies and notations include:
Agent-based Methodologies
Automata-based Notations
Big Data Analytics
Cellular Automata
Conformance Analysis
Constraint Programming
Data Mining
Differential Equations
Empirical Modelling
Game Theory
Machine Learning
Membrane Systems
Network Theory and Analysis
Ontologies
Optimisation Modelling
Petri Nets
Process Calculi
Process Mining
Rewriting Systems
Spatio-temporal Data Analysis/Mining
System Dynamics
Text Mining
Application domains include:
Biology
Business Process Management
Climate Change
Ecology
Education
Environmental Risk Assessment and Management
Enterprise Architectures
Epidemiology
Genetics
Governance
Open Source Software Development and Communities
Pharmacology
Social Software Engineering
Social Systems
Sustainable Development
Urban Ecology and Smart Cities
Synergistic approaches include:
use of modelling methods and notations in a Knowledge Management context
development and use of common modelling and Knowledge Management frameworks to explore and understand complex systems from the application domains of interest