| IEEE BigData 2019 Cup: Suspicious Network Event Recognition A Janusz, D Kałuża, A Chądzyńska-Krasowska, B Konarski, J Holland, ... IEEE BigData 2019, 2019 | 35 | 2019 |
| Scalable cyber-security analytics with a new summary-based approximate query engine D Ślęzak, A Chądzyńska-Krasowska, J Holland, P Synak, R Glick, ... 2017 IEEE International Conference on Big Data (Big Data), 1840–1849, 2017 | 22 | 2017 |
| Scalable Machine Learning with Granulated Data Summaries: A Case of Feature Selection A Chądzyńska-Krasowska, P Betliński, D Ślęzak Foundations of Intelligent Systems: 23rd International Symposium, ISMIS 2017 …, 2017 | 11 | 2017 |
| Relacyjne bazy danych L Banachowski, A Chądzyńska, K Matejewski Wydawnictwo PJWSTK, 2004 | 11* | 2004 |
| A Metadata Diagnostic Framework for a New Approximate Query Engine Working with Granulated Data Summaries A Chądzyńska-Krasowska, S Stawicki, D Ślęzak Rough Sets, International Joint Conference, IJCRS 2017, 623-643, 2017 | 8 | 2017 |
| Ranking mutual information dependencies in a summary-based approximate analytics framework D Ślęzak, J Borkowski, A Chądzyńska-Krasowska 2018 International Conference on High Performance Computing & Simulation …, 2018 | 5 | 2018 |
| Quality of Histograms As Indicator Of Approximate Query Quality A Chądzyńska-Krasowska, M Kowalski Proceedings of the Federated Conference on Computer Science and Information …, 2016 | 5 | 2016 |
| Academic Models of Education Supported by Information Technology A Drabik, L Banachowski, A Chądzyńska-Krasowska, JP Nowacki EDULEARN17, https://library.iated.org/view/DRABIK201, 2017 | 4 | 2017 |
| Similarity-based accuracy measures for approximate query results A Chądzyńska-Krasowska Information Processing and Management of Uncertainty in Knowledge-Based …, 2018 | 3 | 2018 |
| Approximate Decision Tree Induction over Approximately Engineered Data Features D Ślęzak, A Chądzyńska-Krasowska Rough Sets 12179, 376, 2020 | 2 | 2020 |
| Systems and methods for intelligent capture and fast transformations of granulated data summaries in database engines D Slezak, R Glick, P Betlinski, P Synak, J Wroblewski, ... US Patent 11,301,467, 2022 | | 2022 |
| Od e-materiałów do e-tutorów L Banachowski, E Mrówka-Matejewska, A Chądzyńska-Krasowska, ... | | 2015 |
| System zbierania ocen i wstępna analiza ich relacji A Chądzyńska-Krasowska, W Kosiński, T Trung Nguyen EduAkcja. Magazyn Edukacji Elektronicznej 6, 44-53, 2014 | | 2014 |
| Budowa e-tutora wspomagającego uczenie się studentów L Banachowski, A Chądzyńska-Krasowska, E Mrówka-Matejewska, ... EduAkcja. Magazyn Edukacji Elektronicznej 12, 43-50, 2013 | | 2013 |
| Wspomaganie zarządzania uczelnią przy użyciu metod analitycznych hurtowni danych A Chądzyńska-Krasowska, E Mrówka-Matejewska Wydawnictwo PJWSTK, 2013 | | 2013 |
| Wybór ścieżki e-nauczania dostosowanej do profilu studenta A Chądzyńska-Krasowska | | 2011 |
| Deriving Volumetric Anomalies from Huge Log Event Data Sets using Approximate SQL Engine J Borkowski, A Chądzyńska-Krasowska, J Holland, M Kowalski, D Ślęzak, ... Book of Abstracts, 69, 0 | | |
Dominik SlezakUniversity of Warsaw & QED Software在 mimuw.edu.pl 的电子邮件经过验证
