Conference topics

The open list of topics proposed for submission is organized in form of the tracks presented in the list given below. It is important to note that such classification is valid only on the period of submission, reviewing and selection of papers. The structure of the conference program is defined by the Program committee relying on the selected papers and does not necessarily reflects the submission track structure. All tacks are open for submission of any categories of contribution including regular papers, short papers, posters, demos, tutorials.

Tracks of the sphere for data analysis, problem solving, experiment organization in DID

Peculiarities of data in DID (data, methods, tools and infrastructures for data acquisition and storage in various DID): advanced projects, experience of data acquisition and storage in long-living projects, comparative analysis of the projects, project surveys, facilities and approaches for data collecting and storage, specificity of semantics, structure and characteristics of data in DID (including streaming data), data representation, metadata organization, data quality, data provenance (including taking them from the literature), anomaly detection methods in data, procedures for data cleansing, problems of Big Data storage.

Problem statement and solving in DID: urgent problem or phenomena required study in specific DID 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 DID, process of problem solving and tools applied.

Organization of experiments in DID: provision of theory justification, hypothesis generation and testing, methods for hypothesis testing, simulation, research cycles, robotization, infrastructures for experiment organization, workflows, reproducing of results, workflow metadefinition and reuse, verification of results, comparison of new results with those obtained earlier, survey of approaches for the organization of experimental research.

Hypotheses and models (as constituents of research experiments in DID): methods and facilities for hypotheses formulation, for construction of computerized models during research in DID, models as a mean for theory and hypothesis verification, applying simulation over Big Data, 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, evaluation of methods generality and specialization, orientation of methods on specific DID and kinds of data, classification of methods, systematization of experience of methods application for problem solving, cognitive analytics for data-driven decision making, meta-analysis methods, multiscale analysis, Big Data analytics –efficiency and scalability, new data analysis methods development.

Conceptual modeling of a universe of discourse in DID: formalization of semantics of the subject domains, evolution of ontologies in specific DID, experience of applying of various models and tools for ontology support, semantic annotation for concept formation, progress of ontological modeling – e.g., based on Semantic Web stack, ontological models use for database schema specification, conceptual specification of problems in various DID based on ontologies ad declarative languages – e.g., such as used in W3C RIF, approaches for provisioning of independence of conceptual specification of data, experience and means for abstract specification of algorithms and workflows in the conceptual models, provisions for semantic interoperability of programs.

Application of data intensive analysis in DID research: functions and architectures of facilities for research support in data intensive cases (virtual laboratories/observatories, data centers, distributed infrastructures, applications of parallel database machines, supercomputer, grid and cluster based infrastructures), cross-infrastructure interoperability and data sharing between interdisciplinary researches.

Tracks of the sphere for data management in DID

Data integration in DID: methods and tools for entity resolution and fusion in the Big Data infrastructures, scalable methods for the map-reduce environments, unification of various data models (such as graph-based, NoSQL, RDF-based. array-based models), canonical data models and their synthesis, schema mapping, methods and tools for virtual data integration, application driven subject mediators, semantic integration of data, data warehouses, ETL process support, multidimensional data models, data integration in hybrid infrastructures supporting multistructured and structured data, infrastructures of data integration systems, application of data integration facilities in DID.

Data extraction from texts: linguistic methods, NLP, multilingual textual data, declarative languages and methods for identification of relevant data in texts, instruments for textual analysis, identification and extraction of structured data from the texts.

Research data infrastructures and their applications in DID: various data infrastructures, based on data and compute intensive platforms (such as clouds and grids, distributed clusters, supercomputers, etc.), data intensive use cases for such infrastructures, experience of their implementation, evaluation of performance of data infrastructures, new models for data intensive programming in such infrastructures and Big data platforms, scalability issues , metadata and modeling in data infrastructures, virtualization based technologies, DID applications as virtual machines.

Semantic Web role in DID: linked open data, matching of ontologies and linked data to support semantic interoperability and cross identification of the Semantic Web resources, scalability of matching, spatio-temporal linked data and ontologies, harvesting of linked data from diverse data collections, linked data quality, provenance of linked data, multidialect architectures for declarative conceptual specification and problem solving over heterogeneous collections of data, application of Semantic Web facilities and social nets for problem solving in DID.