The increasing variability, dynamic and heterogenous nature of big data, as well as the flexibility required by data producers and consumers lead to the necessity of organizing access to data without requiring information about its structure or belonging to any particular information system, i.e. data virtualization. Data virtualization complements the concept of a virtual supercomputer, allowing us to consider the computing environment as a single information continuum, where computing tools are part of the general data model and data is considered in the context of three basic components: integration, analysis, and data presentation. In this paper, we present the concept of unified, generalized and encapsulated representation of data coming from a heterogenous set of data sources, based on the extension of the Logical Data Warehouse (LDW) and the Distributed Data Network in the form of a distributed ledger – the virtual DLT (vDLT). The main difference between the vDLT and LDW approaches is the decentralized management of data using a consensus mechanism. We discuss data virtualization practices, the methodology of constructing a virtualized data environment, and compare core steps and approaches for each of the two directions.
Original languageEnglish
Pages (from-to)11-21
JournalCEUR Workshop Proceedings
Publication statusPublished - 2020
EventIV International Workshop "Data life cycle in physics", DLC-2020 - Москва
Duration: 8 Jun 202010 Jun 2020

Fingerprint Dive into the research topics of 'Big Data Virtualization: Why and How?'. Together they form a unique fingerprint.

Cite this