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Organizations are often faced with the challenge of providing data management solutions for large, heterogenous datasets that may have different underlying data and programming models. For example, a medical dataset may have unstructured text, relational data, time series waveforms and imagery. Trying to fit such datasets in a single data management system can have adverse performance and efficiency...
Ever increasing data size and new requirements in data processing has fostered the development of many new database systems. The result is that many data-intensive applications are underpinned by different engines. To enable data mobility there is a need to transfer data between systems easily and efficiently. We analyze the state-of-the-art of data migration and outline research opportunities for...
A description and discussion of the SciDB database management system focuses on lessons learned, application areas, performance comparisons against other solutions, and additional approaches to managing data and complex analytics.
In this paper, we describe a versioned database storage manager we are developing for the SciDB scientific database. The system is designed to efficiently store and retrieve array-oriented data, exposing a ``no-overwrite'' storage model in which each update creates a new ``version'' of an array. This makes it possible to perform comparisons of versions produced at different times or by different algorithms,...
For many decades, users in scientific fields (domain scientists) have resorted to either home-grown tools or legacy software for the management of their data. Technological advancements nowadays necessitate many of the properties such as data independence, scalability, and functionality found in the roadmap of DBMS technology, DBMS products, however, are not yet ready to address scientific application...
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