| Recursive query facilities in relational databases: a survey P Przymus, A Boniewicz, M Burzańska, K Stencel Database Theory and Application, Bio-Science and Bio-Technology, 89-99, 2010 | 35* | 2010 |
| Dynamic compression strategy for time series database using GPU P Przymus, K Kaczmarski New Trends in Databases and Information Systems, 235-244, 2014 | 25 | 2014 |
| Profile based recommendation of code reviewers M Fejzer, P Przymus, K Stencel Journal of Intelligent Information Systems 50 (3), 597-619, 2018 | 16 | 2018 |
| Time series queries processing with gpu support P Przymus, K Kaczmarski New trends in databases and information systems, 53-60, 2014 | 11 | 2014 |
| 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 | 10 | 2017 |
| 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 | 10 | 2012 |
| 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 | 9 | 2014 |
| Application of wavelets and kernel methods to detection and extraction of behaviours of freshwater mussels P Przymus, K Rykaczewski, R Wiśniewski International Conference on Future Generation Information Technology, 43-54, 2011 | 9 | 2011 |
| Improving high-performance GPU graph traversal with compression K Kaczmarski, P Przymus, P Rzążewski New Trends in Database and Information Systems II, 201-214, 2015 | 8 | 2015 |
| Compression planner for time series database with GPU support P Przymus, K Kaczmarski Transactions on Large-Scale Data-and Knowledge-Centered Systems XV, 36-63, 2014 | 8 | 2014 |
| 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, 313, 2021 | 6 | 2021 |
| A bi-objective optimization framework for heterogeneous CPU/GPU query plans P Przymus, K Kaczmarski, K Stencel Fundamenta Informaticae 135 (4), 483-501, 2014 | 6 | 2014 |
| 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, 277, 2021 | 4 | 2021 |
| 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 | 3 | 2019 |
| Query Optimization in Heterogeneous CPU/GPU Environment for Time Series Databases P Przymus University of Warsaw Faculty of Mathematics, Informatics and Mechanics, 2014 | 3 | 2014 |
| Fixed length lightweight compression for GPU revised K Kaczmarski, P Przymus Journal of Parallel and Distributed Computing 107, 19-36, 2017 | 2 | 2017 |
| 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 | 1 | 2021 |
| 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 | 1 | 2017 |
| MINING SOFTWARE REPOSITORIES FOR CODE QUALITY M Fejzer, P Przymus, K Stencel | | 2020 |
| Finding relevant multivariate models for multi-plant photovoltaic energy forecasting Y Hmamouche, P Przymus, L Lakhal, A Casali | | 2017 |
Krzysztof KaczmarskiWarsaw University of TechnologyZweryfikowany adres z mini.pw.edu.pl
