dc.contributor.advisor | Stoimenov, Leonid | |
dc.contributor.other | Stojanović, Dragan | |
dc.contributor.other | Rančić, Dejan | |
dc.contributor.other | Stanimirović, Aleksandar | |
dc.contributor.other | Milosavljević, Branko | |
dc.creator | Štufi, Martin T. | |
dc.date.accessioned | 2023-10-20T20:56:39Z | |
dc.date.available | 2023-10-20T20:56:39Z | |
dc.date.issued | 2022 | |
dc.identifier.uri | http://eteze.ni.ac.rs/application/showtheses?thesesId=8623 | |
dc.identifier.uri | https://fedorani.ni.ac.rs/fedora/get/o:1915/bdef:Content/download | |
dc.identifier.uri | https://plus.cobiss.net/cobiss/sr/sr/bib/122999817 | |
dc.identifier.uri | https://nardus.mpn.gov.rs/handle/123456789/21793 | |
dc.description.abstract | In recent years, the application and widespread adoption
of Big Data, Internet of Things (IoT), Cloud technologies
have increased the use of large-scale data processing systems.
These technologies increased significantly and exponentially
with the heterogeneous data generated (structured,
unstructured, and semi-structured). The processing and
analysis of a tremendous amount of data is cumbersome and
is gradually moving from the classic "batch" processing -
extraction, transformation, loading (ETL) techniques to realtime
processing. For example, in the domain of the
automobile industry, healthcare, but also in other disciplines.
Tracking, data processing, environmental management, timeseries
data, and historical data set are crucial to forecasting
models not only in these domains.
This doctoral dissertation is about the design of a
general architecture for processing a large amount of data.
The architecture as such enables efficient acquisition of data,
their optimal placement, processing of large amounts of data,
use of various algorithms for drawing conclusions as well as
for displaying data. The doctoral dissertation shows the
complete process of modeling and designing architecture, the
selection of appropriate software components for its
realization. The presented platform met very demanding
parameters for meeting the system's performance, including
the standard for decision support of the Transaction
Processing Council (TPC-H) by following the European
Union (EU) legislation and the Czech Republic. Currently, the
presented proof of concept (PoC) that has been upgraded to
the production environment has united isolated parts of the
Czech Republic's healthcare. The reported PoC Big Data
Analytics platform, artefacts and concepts can be transferred
to health systems in other countries interested in developing
or upgrading their national health infrastructure in a costeffective,
secure, scalable, and high-performance way. | en |
dc.format | application/pdf | |
dc.language | sr | |
dc.publisher | Универзитет у Нишу, Електронски факултет | sr |
dc.rights | openAccess | en |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.source | Универзитет у Нишу | sr |
dc.subject | Klaster, Big Data, Stream, Vertica, NoSQL, obrada
podataka u realnom vremenu, stream podaci | sr |
dc.subject | Big Data, Big Data Analytics, TPC-H, NoSQL Database
cluster, Real time BDA | en |
dc.title | Predlog arhitekture sistema visokih performansi za generalnu obradu podataka na klasterima za podatke velikog obima | sr |
dc.type | doctoralThesis | |
dc.rights.license | BY-NC-ND | |
dc.identifier.fulltext | http://nardus.mpn.gov.rs/bitstream/id/155732/Doctoral_thesis_14149.pdf | |
dc.identifier.fulltext | http://nardus.mpn.gov.rs/bitstream/id/155733/Stufi_Martin.pdf | |
dc.identifier.rcub | https://hdl.handle.net/21.15107/rcub_nardus_21793 | |