Skip to main content

Login for students

Login for employees

default alt fei

The research team is engaged in the application of new approaches and methodologies in the processing and analysis of large-scale data describing the operation of selected complex (e.g. transport) systems. Methods and technologies from the fields of Big Data, parallel systems, artificial intelligence and computer simulation are applied to investigate the characteristics of the operation of these systems:

  • Design of computer cluster structure for HPC, Big data, data analytic,
  • Implementation of parallel algorithms for OpenMP, MPI, CUDA, Chapel, Spark and Scala environments,
  • Detection of object state changes in space and time, temporal data processing, distributed and NoSQL databases,
  • Transportation information systems, data analysis. 

Key words

Clusters - Databases - Parallel HW and SW - Artificial Intelligence - Data analytics - Big data - Real time

Research Team members

  • doc. Dr. Ing. Tomas Brandejsky 
  • prof. Ing. Antonin Kavicka, Ph.D. 
  • doc. Ing. Michael Bazant, Ph.D.
  • Ing. Monika Borkovcova, Ph.D.
  • Ing. Roman Divis, Ph.D.
  • Ing. Jan Merta, Ph.D.

Projects being solved 

2022 – 2026Cooperation of the University of Pardubice and the application sphere in application-oriented research of location, detection and simulation systems for transport and transportation processes (PosiTrans). Ministry of Education and Science of the Czech Republic, OPVVV programme - sustainability
2023 – 2024

EverGreen. Creation of innovative courses and teaching materials in the field of data analysis with cross-sectoral transnational collaboration (UNIZA-SK, UPCE-CZ, Univerza v Mariboru-SLO,  Veleučilište u Šibeniku – HR, Oracle, Trokut Šibenik - Inkubator za nove tehnologije– HR) MŠMT ČR, Erasmus

2023 – 2025

SmartRail. Automated analysis of railway freight transport operational data (UPCE, ČD Cargo, a.s.), TAČR-Doprava 2020+

Selected publications

HRBEK, Vaclav and BRANDEJSKY, Tomas.  Memetic Algorithm with GPU Optimization. In: Data Science and Algorithms in Systems, Cham: Springer International Publishing, 2023, s. 174-185. Lecture notes in Networks and Systems, Vol. 579. DOI: 10.1007/978-3-031-09070-7_29. ISBN 978-3-031-21438-7_15 .
BRANDEJSKY, Tomas. The Train Delay Model Developed by the Genetic Programming Algorithm. Online. Journal of Advanced Transportation. 2022, roč. 2022, s. 1-7. ISSN 2042-3195. Dostupné z: https://doi.org/10.1155/2022/8858756.
BRANDEJSKY, Tomas and HRBEK, Vaclav.  The Survey of Object-Oriented Software Programming Language from a Heterogeneous Cluster Programming Viewpoint. In: Software Perspectives in Systems, Vol. 1. Cham: Springer International Publishing, 2022, s. 344-352. Lecture notes in Networks and Systems. DOI: 10.1007/978-3-031-09070-7_29. ISBN 978-3-031-09070-7.
MERTA, Jan and BRANDEJSKY, Tomas. Two-layer Genetic Programming. Neural Network World. Praha: CTU in Prague, 2022, 2022(4), 215-231. Dostupné z: doi:DOI: http://dx.doi.org/10.14311/NNW.2022.27.013
BRANDEJSKY, Tomas. Preconditions of GPA-ES Algorithm Application to Big Data. In Advances in Intelligent Systems and Computing : Artificial Intelligence and Evolutionary Computations in Engineering Systems. Vol.1056. Singapur: Springer, 2020, s. 485-492. ISBN 978-981-15-0198-2. ISSN 2194-5357.
ROZINEK, O., BORKOVCOVÁ, M. Theorems for Boyd-Wong Contraction Mappings on Similarity Spaces. Mathematics, 2023, roč. 11, č. 20, s.4359.
ROZINEK, O., BORKOVCOVÁ, M., MAREŠ, J. Scalable Similarity Joins for Fast and Accurate Record Deduplication in Big Data. Good Practices and New Perspectives in Information Systems and Technologies : WorldCIST 2024, Volume 6. Cham : Springer Nature Switzerland AG, 2024, s. 181 - 191. ISBN 978-3-031-60327-3. ISSN 2367-3370.
MAJERÍK, F., BORKOVCOVÁ, M. Common problems in application development. Proceedings of the 41st IBIMA Conference. IBIMA, 2023. Norristown : International Business Information Management Association-IBIMA, 2023, s. 1-7. ISBN 979-8-9867719-2-2. ISSN 2767-9640.

ROZINEK, O., BORKOVCOVÁ, M. A Novel Regression Approach: Analyzing Textual Data in Similarity Space. Proceedings of the 35th Conference of Open Innovations Association FRUCT. New York : IEEE (Institute of Electrical and Electronics Engineers), 2024, s. 596-603. ISBN 979-8-3503-4947-4. ISSN 2305-7254.

ROZINEK, O., BORKOVCOVÁ, M. A Novel Approach to Regression: Exploring the Similarity Space with Ordinary Least Squares on Database Records. Conference of Open Innovation Association, FRUCT. New York : IEEE (Institute of Electrical and Electronics Engineers), 2024, s. 270-277. ISBN 978-952-65-2460-3. ISSN 2305-7254.

MAJERÍK, F., BORKOVCOVÁ, M. Design of Data Access Architecture Using ORM Framework. Conference of Open Innovation Association,FRUCT. New York : IEEE (Institute of Electrical and Electronics Engineers), 2023, s. 93-99. ISBN 978-952-65-2460-3. ISSN 2305-7254.
ROZINEK, O., BORKOVCOVÁ, M., MAREŠ, J. BipartiteJoin: Optimal Similarity Join for Fuzzy Bipartite Matching. Good Practices and New Perspectives in Information Systems and Technologies : WorldCIST 2024, Volume 6. Cham : Springer Nature Switzerland AG, 2024, s. 171-180. ISBN 978-3-031-60327-3. ISSN 2367-3370.
BAŽANT, M., BULÍČEK, J. Impact Assessment of Interlocking Systems on Single-Track Railway Lines as a Measure Leading to Resilient Railway System. Journal of Advanced Transportation, 2022, roč. 2022, č. August, s. nestránkováno.
BAŽANT, M., BULÍČEK, J. Relationship between delay and the need for stations on single-track lines for train passing. Transport problems 2022 : proceedings. Katowice : Silesian University of Technology, 2022, s. 87-98. ISBN 978-83-959742-3-6.
LETAVAY, M., BAŽANT, M., TUČEK, P. Object Detection Algorithms - A Review. 2023 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO). New York : IEEE (Institute of Electrical and Electronics Engineers), 2023, s. 31-44. ISBN 979-8-3503-0092-5.
BAŽANT, M., SMOCZYŃSKI, P., GILL, A., BULÍČEK, J. Missing tracks and switches – on the way to assess modernised stations in terms of their usefulness for railway traffic. Transport Means 2022 : proceedings of the 26th Internationa Scientific Conference. Kaunas : Kaunas University of Technology, 2022, s. 180-185. ISSN 1822-296X.
KAVIČKA, A., DIVIŠ, R. Dynamic Search of Train Shortest Routes Within Microscopic Traffic Simulators. IEEE ACCESS, 2022, roč. 10, č. Neuveden, s. 90163-90199.
VESELÝ, P., KAVIČKA, A., KRÝŽE, P. Automated Construction of Mesoscopic Railway Infrastructure Models Supporting Station Throat Capacity Assessment. IEEE ACCESS, 2023, roč. 11, č. 20.04.2023, s. 37869-37899.
DIVIŠ, R., KAVIČKA, A. Reflective Nested Simulations Supporting Optimizations within Sequential Railway Traffic Simulators. ACM Transactions on Modeling and Computer Simulation, 2022, roč. 32, č. 1, s. nestránkováno.
FIKEJZ, J., KAVIČKA, A. RegioRail-GNSS Train-Positioning System for Automatic Indications of Crisis Traffic Situations on Regional Rail Lines. Applied Science - Basel, 2022, roč. 12, č. 12, s. nestránkováno.
DIVIŠ, R., NOVOTNÝ, Z. Efficient handling of lots of simulation data files. Proceedings of the 34th European Modeling & Simulation Symposium (EMSS 2022). Rende : CAL-TEK SRL, 2022, s. 1-4. ISBN 978-88-85741-73-7. ISSN 2724-0029.
KYSELA, J., ŠTORKOVÁ, P., BAYER, K., PANUŠ, J., DIVIŠ, R., KOPECKÝ, Z., CHUN-WEI LIN, J., AHMED, U. Activating Participants Through Social Networks and Gamification in Undertourism Areas. Pardubice : Univerzita Pardubice, 2023.180 s. ISBN 978-80-7560-474-3.

Partner organisations:

  • Cargo
  • ČD IS
  • Universita Žilina
  • Univerzita Ljubljana