Información de la conferencia
ICDT 2020: International Conference on Database Theory
https://diku-dk.github.io/edbticdt2020/
Día de Entrega:
2019-09-15
Fecha de Notificación:
2019-12-05
Fecha de Conferencia:
2020-03-30
Ubicación:
Copenhagen, Denmark
Años:
23
CCF: b   CORE: a   QUALIS: b1   Vistas: 18358   Seguidores: 53   Asistentes: 7

Ubicación
Solicitud de Artículos
ICDT is a series of international scientific conferences on research of data management theory (https://databasetheory.org/icdt-pages). Since 2009, it is annually and jointly held with EDBT (Extending DB Technology).

The 23rd edition of ICDT is planned to take place in Copenhagen, Denmark, from March 26th to March 29th 2020.

Broadening Scope

As ICDT strives to broaden its scope, ICDT 2020 will have a Reach Out Track that calls for

    novel formal frameworks or directions for database theory and/or
    onnections between principles of data management and other communities.

The aim of the Reach Out track is to present novel and important directions for database theory and to expand the scope and impact of ICDT towards neighboring communities such as Database Systems, Artificial Intelligence, Knowledge Representation, Machine Learning, Programming Languages, Distributed Computing, and Operating Systems. It is reasonable for articles submitted to this track to be be shorter than regular ICDT submissions. Submissions will be judged mainly in terms of their potential to lead to valuable and impactful theoretical developments. We encourage authors to illustrate the potential impact via convincing examples, preliminary results, and clearly specified open problems.

Topics of Interest

Every topic related to the principles of data management is relevant to ICDT. Particularly welcome are contributions that connect data management to theoretical computer science, and those that connect database theory and database practice.

Examples of relevant topics include:

    Data mining, information extraction, information retrieval, and database aspects of machine learning
    Data models, design, structures, semantics, query languages, and algorithms for data management
    Distributed and parallel databases, cloud computing
    Connections between databases and knowledge representation
    Graph databases, (semantic) Web data, and Web services
    Data streams and sketching
    Data-centric (business) process management and workflows
    Data and knowledge integration and exchange, data provenance, views, and data warehouses
    Domain-specific databases (multimedia, scientific, spatial, temporal, text)
    Data privacy and security, concurrency, and recovery
Última Actualización Por Dou Sun en 2019-09-01
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