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Piotr Przymus
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Applications of machine learning in human microbiome studies: a review on feature selection, biomarker identification, disease prediction and treatment
LJ Marcos-Zambrano, K Karaduzovic-Hadziabdic, T Loncar Turukalo, ...
Frontiers in microbiology 12, 634511, 2021
2932021
Statistical and machine learning techniques in human microbiome studies: contemporary challenges and solutions
I Moreno-Indias, L Lahti, M Nedyalkova, I Elbere, G Roshchupkin, ...
Frontiers in microbiology 12, 635781, 2021
772021
Recursive query facilities in relational databases: a survey
P Przymus, A Boniewicz, M Burzańska, K Stencel
International Conference on Bio-Science and Bio-Technology, 89-99, 2010
46*2010
Profile based recommendation of code reviewers
M Fejzer, P Przymus, K Stencel
Journal of Intelligent Information Systems 50, 597-619, 2018
442018
Dynamic compression strategy for time series database using GPU
P Przymus, K Kaczmarski
New Trends in Databases and Information Systems: 17th East European …, 2014
272014
Improving high-performance GPU graph traversal with compression
K Kaczmarski, P Przymus, P Rzążewski
New Trends in Database and Information Systems II: Selected papers of the …, 2015
172015
Improving multivariate time series forecasting with random walks with restarts on causality graphs
P Przymus, Y Hmamouche, A Casali, L Lakhal
2017 IEEE International Conference on Data Mining Workshops (ICDMW), 2017
152017
A toolbox of machine learning software to support microbiome analysis
LJ Marcos-Zambrano, VM López-Molina, B Bakir-Gungor, M Frohme, ...
Frontiers in microbiology 14, 1250806, 2023
142023
Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action
D D’Elia, J Truu, L Lahti, M Berland, G Papoutsoglou, M Ceci, A Zomer, ...
Frontiers in Microbiology 14, 1257002, 2023
142023
Compression planner for time series database with GPU support
P Przymus, K Kaczmarski
Transactions on Large-Scale Data-and Knowledge-Centered Systems XV: Selected …, 2014
132014
Time series queries processing with GPU support
P Przymus, K Kaczmarski
New Trends in Databases and Information Systems: 17th East European …, 2014
132014
Zebra mussels’ behaviour detection, extraction and classification using wavelets and kernel methods
P Przymus, K Rykaczewski, R Wiśniewski
Future Generation Computer Systems 33, 81-89, 2014
112014
Improving Efficiency of Data Intensive Applications on GPU Using Lightweight Compression
P Przymus, K Kaczmarski
On the Move to Meaningful Internet Systems: OTM 2012 Workshops - Lecture …, 2012
102012
Tracking Buggy Files: New Efficient Adaptive Bug Localization Algorithm
M Fejzer, J Narebski, P Przymus, K Stencel
IEEE Transactions on Software Engineering, 1-1, 2021
92021
GFSM: a feature selection method for improving time series forecasting
Y Hmamouche, P Przymus, A Casali, L Lakhal
International Journal On Advances in Systems and Measurements, 2017
82017
Application of wavelets and kernel methods to detection and extraction of behaviours of freshwater mussels
P Przymus, K Rykaczewski, R Wiśniewski
Future Generation Information Technology: Third International Conference …, 2011
82011
Large multivariate time series forecasting: survey on methods and scalability
Y Hmamouche, PM Przymus, H Alouaoui, A Casali, L Lakhal
Utilizing big data paradigms for business intelligence, 170-197, 2019
72019
A bi-objective optimization framework for heterogeneous CPU/GPU query plans
P Przymus, K Kaczmarski, K Stencel
Fundamenta Informaticae 135 (4), 483-501, 2014
62014
Fixed length lightweight compression for GPU revised
K Kaczmarski, P Przymus
Journal of Parallel and Distributed Computing 107, 19-36, 2017
42017
Query Optimization in Heterogeneous CPU/GPU Environment for Time Series Databases
P Przymus
University of Warsaw Faculty of Mathematics, Informatics and Mechanics, 2014
22014
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Articles 1–20