Piotr Przymus
Cytowane przez
Cytowane przez
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
Dynamic compression strategy for time series database using GPU
P Przymus, K Kaczmarski
New Trends in Databases and Information Systems, 235-244, 2014
Profile based recommendation of code reviewers
M Fejzer, P Przymus, K Stencel
Journal of Intelligent Information Systems 50 (3), 597-619, 2018
Time series queries processing with GPU support
P Przymus, K Kaczmarski
New trends in databases and information systems, 53-60, 2014
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
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
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
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
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
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
A bi-objective optimization framework for heterogeneous CPU/GPU query plans
P Przymus, K Kaczmarski, K Stencel
Fundamenta Informaticae 135 (4), 483-501, 2014
Query Optimization in Heterogeneous CPU/GPU Environment for Time Series Databases
P Przymus
University of Warsaw Faculty of Mathematics, Informatics and Mechanics, 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
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
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
Fixed length lightweight compression for GPU revised
K Kaczmarski, P Przymus
Journal of Parallel and Distributed Computing 107, 19-36, 2017
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
Finding relevant multivariate models for multi-plant photovoltaic energy forecasting
Y Hmamouche, P Przymus, L Lakhal, A Casali
Mapping AOQL to SQL
M Meina, P Przymus
Capturing Behavior of Medical Staff: A Similarity-Oriented Temporal Data Mining Approach 1 Shusaku Tsumoto, Shoji Hirano, Haruko Iwata, and Yuko Tsumoto AF or DF, and How to …
HH Chen, C Ramos, Z Vale, L Faria, G Schaefer, T Dohi, PL Stanchev, ...
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