Witold Andrzejewski

Witold Andrzejewski
Poznan University of Technology · Institute of Computing Science

dr eng.

About

40
Publications
5,285
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153
Citations
Additional affiliations
March 2005 - present
Poznan University of Technology
Position
  • Professor (Assistant)

Publications

Publications (40)
Chapter
In this paper, we investigate the efficiency of Co-location Pattern Mining (CPM). In popular methods for CPM, the most time-consuming step consists of identifying of pattern instances, which are required to calculate the potential interestingness of the pattern. We tackle this problem and provide an instance identification method that has lower com...
Chapter
Mining of Mixed-Drove Co-occurrence Patterns can be very costly. Widely used, Apriori-based methods consist in finding spatial co-location patterns in each considered timestamp and filtering out patterns that are not time prevalent. Such an approach can be inefficient, especially for datasets that contain co-locations with a high number of elements...
Article
Co-location pattern mining is an important area of spatial data mining. In many real word applications, new data is continuously arriving to the system and is stored in spatial databases. As co-location discovery is computationally demanding task, it is crucial to maintain co-location patterns for such dynamic databases without recalculating them f...
Chapter
Parallelizing of the supply chain simulator is considered in this paper. The simulator is a key element of the algorithm optimizing inventory levels and order sizes in a two-echelon logistic system. The mode of operation of the logistic system and the optimization problem are defined first. Then, the inventory optimization algorithm is introduced....
Article
In this paper, we investigate Co-location Pattern Mining (CPM) from big spatial datasets. CPM consists in searching for types of objects that are frequently located together in a spatial neighborhood. Knowledge about such patterns is very important in fields like biology, environmental sciences, epidemiology etc. However, CPM is computationally cha...
Conference Paper
Ubiquitous devices and applications generate data that are naturally ordered by time. Thus elementary data items can form sequences. The most popular way of analyzing sequences is searching for patterns. To this end, sequential pattern discovery techniques were proposed in some research contributions and implemented in a few database systems, e.g.,...
Article
Full-text available
The seventh issue of Complex Systems Informatics and Modeling Quarterly presents five papers devoted to two distinct research topics: systems modeling and natural language processing (NLP). Both of these subjects are very important in computer science. Through modeling we can simplify the studied problem by concentrating on only one aspect at a tim...
Article
This article tackles the problem of efficient construction of iCPI trees, frequently used in co-location pattern discovery in spatial databases. It discusses the methods for parallelization of iCPI-Tree construction and plane-sweep algorithms used in state-of-The-Art algorithms for co-location pattern mining. The main contribution of this paper is...
Article
Full-text available
Approximate query processing (AQP) is an interesting alternative for exact query processing. It is a tool for dealing with the huge data volumes where response time is more important than perfect accuracy (this is typically the case during initial phase of data exploration). There are many techniques for AQP, one of them is based on probability den...
Conference Paper
In spatial databases collocation pattern discovery is one of the most interesting fields of data mining. It consists in searching for types of spatial objects that are frequently located together in a spatial neighborhood. With the advent of data gathering techniques, huge volumes of spatial data are being collected. To cope with processing of such...
Chapter
Research on database and information system technologies has been rapidly evolving over the last few years. Advances concern either new data types, new management issues, and new kind of architectures and systems. The 17th East-European Conference on Advances in Databases and Information Systems (ADBIS 2013), held on September 1–4, 2013 in Genova,...
Article
The Probability Density Function (PDF) is a key concept in statistics. Constructing the most adequate PDF from the observed data is still an important and interesting scientific problem, especially for large datasets. PDFs are often estimated using nonparametric data-driven methods. One of the most popular nonparametric method is the Kernel Density...
Conference Paper
Collocation Pattern Discovery is a very interesting field of data mining in spatial databases. It consists in searching for types of spatial objects that are frequently located together in a spatial neighborhood. Application domains of such patterns include, but are not limited to, biology, geography, marketing and meteorology. To cope with process...
Article
Full-text available
Abstrakt. Artykuł pokazuje przykładowe zastosowanie architektury CUDA opracowanej przez firmę NVIDIA dla swoich kart graficznych. CUDA to uniwersalna architektura procesorów wielordzeniowych instalowanych we współczesnych, najbardziej wydajnych, kartach graficznych. Karta taka, oprócz oczywistych zastosowań w dziedzinie ogólnie pojętego przetwarzan...
Technical Report
Abstract Collocation Pattern Discovery is field of data mining performed in spatial databases. It consists in searching for types of spatial objects that are frequently located together in a spatial neighborhood. Such patterns are useful in many application domains including, but not limited to, biology, geography, marketing and meteorology. To cop...
Conference Paper
Recent appearance of the a type of OLAP analysis, the sequential OLAP (or SOLAP) has caused the need for new index structures which support new types of analytical queries. An integral part of processing SOLAP queries is finding sequences which match a user-specified pattern. We call such queries \emph{subsequence pattern queries}. The contribution...
Article
Bitmap indexes are data structures applied to indexing attributes in databases and data warehouses. A drawback of a bitmap index is that its size increases when the domain of an indexed attribute increases. As a consequence, for wide domains, the size of a bitmap index is too large to be efficiently processed. Hence, various techniques of compressi...
Conference Paper
Full-text available
Bitmap indexes are one of the basic data structures applied to query optimization in data warehouses. The size of a bitmap index strongly depends on the domain of an indexed attribute, and for wide domains it is too large to be efficiently processed. For this reason, various techniques of compressing bitmap indexes have been proposed. Typically, co...
Article
Full-text available
Method materialization is a promising data access optimization technique for multiple applications, including, in particular object programming languages with persistence, object databases, distributed computing systems, object-relational data warehouses, multimedia data warehouses, and spatial data warehouses. A drawback of this technique is that...
Article
Full-text available
Many of todays database applications, including market basket analysis, web log analysis, DNA and protein sequence analysis utilize databases to store and retrieve sequential data. Commercial database management systems allow to store sequential data, but they do not support efficient querying of such data. To increase the efficiency of analysis of...
Conference Paper
Full-text available
In order to extract useful knowledge from large databases of sales data, data mining algorithms (the so-called market basket analysis) are used. Unfortunately, these algorithms, depending on data and parameters, may generate a large number of patterns. Analysis of these results is performed by the user and involves executing a lot of queries on com...
Conference Paper
In many recent applications of database management systems data may be stored in user defined complex data types (such as sequences). However, efficient querying of such data is not supported by commercially available database management systems and therefore efficient indexing schemes for complex data types need to be developed. In this paper we f...
Conference Paper
Full-text available
Object-relational database management systems allow users to define complex data types, such as objects, collections, and nested tables. Unfortunately, most commercially available database systems do not support either efficient querying or indexing of complex attributes. Different indexing schemes for complex data types have been proposed in the l...
Article
Full-text available
In order to extract useful knowledge from large databases of sales data, data mining algorithms (the so-called market basket anal- ysis) are used. Unfortunately, these algorithms, depending on data and parameters, may generate a large number of patterns. Analysis of these results is performed by the user and involves executing a lot of queries on c...

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Project (1)
Project
FFT-Based Fast Bandwidth Selector for Multivariate Kernel Density Estimation FFT-Based Fast Computation of Multivariate Kernel Density Estimators with Unconstrained Bandwidth Matrices FPGA-Based Bandwidth Selection for Kernel Density Estimation Using High Level Synthesis Approach