Abstract
Modern analytics environments are characterized by a data infrastructure that comprises a great variety of datasets, data formats, data management and processing systems. Such environments are dynamic and data analysis needs to be performed in a flexible and agile manner via data virtualization techniques. Towards this end, we have proposed the Data Virtual Machine (DVM), a graph-based conceptual model based on entities and attributes. The basic idea of the DVM is that the relations of entities and attributes are based and expressed as the output of data processing tasks. In this paper we discuss the notion of data virtualization and propose a set of goals for relevant techniques in terms of modeling capabilities, query formulation and schema flexibility. We also place DVMs with respect to these goals.
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Chatziantoniou, D., Kantere, V. (2021). Data Virtual Machines: Enabling Data Virtualization. In: Rezig, E.K., et al. Heterogeneous Data Management, Polystores, and Analytics for Healthcare. DMAH Poly 2021 2021. Lecture Notes in Computer Science(), vol 12921. Springer, Cham. https://doi.org/10.1007/978-3-030-93663-1_1
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DOI: https://doi.org/10.1007/978-3-030-93663-1_1
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