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ORCIDPawel Boinski
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- affiliation: Poznan University of Technology, Poland
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2020 – today
- 2024
[j7]Witold Andrzejewski
, Bartosz Bebel, Pawel Boinski, Robert Wrembel:
On tuning parameters guiding similarity computations in a data deduplication pipeline for customers records: Experience from a R&D project. Inf. Syst. 121: 102323 (2024)- [c14]Witold Andrzejewski, Bartosz Bebel, Pawel Boinski, Justyna Kowalewska, Agnieszka Marszalek, Robert Wrembel:
Statistical Modeling vs. Machine Learning for Deduplication of Customer Records. EDBT/ICDT Workshops 2024 - 2023
- [j6]Witold Andrzejewski, Pawel Boinski:
Bounding box representation of co-location instances for Chebyshev and Manhattan metrics. Data Knowl. Eng. 145: 102153 (2023) - [j5]Witold Andrzejewski, Pawel Boinski:
Maximal Mixed-Drove Co-occurrence Patterns. Inf. Syst. Frontiers 25(5): 2005-2028 (2023) - [c13]Pawel Boinski, Witold Andrzejewski, Bartosz Bebel, Robert Wrembel:
On Tuning the Sorted Neighborhood Method for Record Comparisons in a Data Deduplication Pipeline - Industrial Experience Report. DEXA (1) 2023: 164-178 - [c12]Witold Andrzejewski, Bartosz Bebel, Pawel Boinski, Mariusz Sienkiewicz, Robert Wrembel:
Text Similarity Measures in a Data Deduplication Pipeline for Customers Records. DOLAP 2023: 33-42 - [c11]Pawel Boinski, Mariusz Sienkiewicz, Robert Wrembel, Bartosz Bebel, Witold Andrzejewski:
On evaluating text similarity measures for customer data deduplication. SAC 2023: 297-300 - 2022
- [c10]Pawel Boinski, Mariusz Sienkiewicz, Bartosz Bebel, Robert Wrembel, Dariusz Galezowski, Waldemar Graniszewski:
On Customer Data Deduplication: Lessons Learned from a R&D Project in the Financial Sector. EDBT/ICDT Workshops 2022 - 2021
- [c9]Witold Andrzejewski, Pawel Boinski:
Maximal Mixed-Drove Co-Occurrence Patterns. ADBIS 2021: 15-29 - [c8]Witold Andrzejewski, Pawel Boinski:
Bounding Box Representation of Co-location Instances for L∞ Induced Distance Measure. DaWaK 2021: 3-14
2010 – 2019
- 2019
- [j4]Witold Andrzejewski, Pawel Boinski:
Parallel approach to incremental co-location pattern mining. Inf. Sci. 496: 485-505 (2019) - 2018
- [j3]Witold Andrzejewski, Pawel Boinski:
Efficient spatial co-location pattern mining on multiple GPUs. Expert Syst. Appl. 93: 465-483 (2018) - 2015
- [j2]Witold Andrzejewski, Pawel Boinski:
Parallel GPU-based Plane-Sweep Algorithm for Construction of iCPI-Trees. J. Database Manag. 26(3): 1-20 (2015) - 2014
- [j1]Pawel Boinski, Maciej Zakrzewicz:
Algorithms for spatial collocation pattern mining in a limited memory environment: a summary of results. J. Intell. Inf. Syst. 43(1): 147-182 (2014) - [c7]Witold Andrzejewski, Pawel Boinski:
A Parallel Algorithm for Building iCPI-trees. ADBIS 2014: 276-289 - 2013
- [c6]Witold Andrzejewski, Pawel Boinski:
GPU-Accelerated Collocation Pattern Discovery. ADBIS 2013: 302-315 - [c5]Pawel Boinski, Maciej Zakrzewicz:
Concurrent Execution of Data Mining Queries for Spatial Collocation Pattern Discovery. DaWaK 2013: 184-195 - 2012
- [c4]Pawel Boinski, Maciej Zakrzewicz:
Partitioning Approach to Collocation Pattern Mining in Limited Memory Environment Using Materialized iCPI-Trees. ADBIS (2) 2012: 19-30 - [c3]Pawel Boinski, Maciej Zakrzewicz:
Collocation Pattern Mining in a Limited Memory Environment Using Materialized iCPI-Tree. DaWaK 2012: 279-290
2000 – 2009
- 2006
- [c2]Pawel Boinski, Marek Wojciechowski, Maciej Zakrzewicz:
A Greedy Approach to Concurrent Processing of Frequent Itemset Queries. DaWaK 2006: 292-301 - [c1]Pawel Boinski, Konrad Jozwiak, Marek Wojciechowski, Maciej Zakrzewicz:
Improving Quality of Agglomerative Scheduling in Concurrent Processing of Frequent Itemset Queries. Intelligent Information Systems 2006: 233-242
Coauthor Index
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