Call for Contributions
Categories of submissions and reviewing
- regular papers reflecting original scientific results (from 12 to 15 pages)
- short papers (work in progress) (from 6 to 11 pages)
- tutorials (proposals should be submitted to the PC co-chairs)
For reviewing process papers of any category are submitted to the Program committee in digital form by the EasyChair system in PDF in strict conformance with the Springer Computer Science Proceedings Word or Latex format.
Two-round single-blind peer-reviewing will be organized. During the first round each paper (demo) is reviewed by at least three PC members. As a result of the first round a paper (demo) can be accepted as a full paper (demo), rejected or recommended to be revised w.r.t. remarks in reviews. All papers recommended to be revised are subjects for the second round of reviewing. During the second round a paper (demo) is reviewed again. As a result of the second round a paper (demo) can be accepted as a full paper (demo), short paper (demo) or rejected.
Soon after the conference the papers are distributed among different volumes of proceedings. Top-rated full papers are included into the CCIS volume and journal volumes (Lobachevskii Journal of Mathematics, Pattern Recognition and Image Analysis, Automation and Remote Control). Other full and short papers (demos) are included in the RCSI journal volume.
Conference topics
The open list of topics proposed for submission is organized in form of the tracks presented in the list given below.
Tracks for data analysis, problem solving, experiment organization
- Problem statement and solving: urgent problem or phenomena required study in a specific domain or in a generalized way, thorough insight based on the nature, characteristics of the phenomenon and data available, approaches for organization of problem solving and methods selection, problem classification in various domains, process of problem solving and tools applied.
- Organization of experiments: survey of approaches for the organization of experimental research, scientific theory justification, experiment simulation, research cycles, robotization, infrastructures for experiment organization, reproducing of results, workflow metadefinition and reuse, verification of results, comparison of new results with those obtained earlier.
- Hypotheses and models as constituents of research experiments: methods and facilities for hypotheses generation and testing, construction of computerized models, models as a mean for theory and hypothesis verification, cognitive modeling paradigm, experience of creation of predictive models in research.
- Advanced data intensive analysis methods and procedures: state of the art in methods of statistics, data mining, machine learning, multivariate analysis, evaluation of methods generality and specialization, orientation of methods on specific domains and kinds of data, classification of methods, systematization of experience of methods application for problem solving, cognitive analytics for data-driven decision making, information visualization and exploratory analysis, meta-analysis methods, Big Data analytics efficiency and scalability, new data analysis methods development.
- Conceptual modeling: formalization of semantics of the subject domains, conceptual specification of problems and evolution of ontologies in specific domains, experience of applying of various models and tools for ontology support, semantic annotation for concept formation, progress of ontological modeling, ontological models use for database schema specification, independence of conceptual specification of data, abstract specification of algorithms and workflows in the conceptual models, semantic interoperability of programs.
- Research support in data infrastructures, data intensive use cases: functions and architectures of facilities for research support (virtual laboratories/observatories, data centers), cross-infrastructure interoperability and data sharing between interdisciplinary researches, data intensive use cases for research data infrastructures, experience of use case implementation.
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