activity
20172026
most citedUnsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks

61 citations · 63 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2026

Hierarchical Specialised Ensembles for Classification of Zebrafish Phenotypes Using the Selected Image Recognition Methods

Piotr S. Maciąg, Monika Maciąg, Magdalena Majdan

We propose and evaluate three hierarchical ensemble setups for zebrafish phenotype classification from embryo images. In all setups, stage 1 uses a single four-class classifier to…

cs.DB2026

Discovering Frequent Closed Embedded Sub-DAGs in Spatio-Temporal Event Data

Piotr S. Maciąg

We propose a novel approach to mine patterns in spatio-temporal event data based on discovering frequent closed embedded sub-Directed Acyclic Graphs (DAGs). In our method, event in…

cs.IR2026

Combining Retrieval-Augmented Text Generation with LLMs for Reading Content Recommendations

Sooyeon Kim, Piotr S. Maciąg

This work presents the design, implementation, and evaluation of a system for generating personalized reading content using Large Language Models (LLMs) combined with Retrieval-Aug…

cs.LG2021

Discovery of Crime Event Sequences with Constricted Spatio-Temporal Sequential Patterns

Piotr S. Maciąg, Robert Bembenik, Artur Dubrawski

In this article, we introduce a novel type of spatio-temporal sequential patterns called Constricted Spatio-Temporal Sequential (CSTS) patterns and thoroughly analyze their propert…

cs.NE2019★ 61 cited

Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks

Piotr S. Maciąg, Marzena Kryszkiewicz, Robert Bembenik +2

Unsupervised anomaly discovery in stream data is a research topic with many practical applications. However, in many cases, it is not easy to collect enough training data with labe…

cs.DB2017

Discovering Sequential Patterns in Event-Based Spatio-Temporal Data by Means of Microclustering - Extended Report

Piotr S. Maciąg

In the paper, we consider the problem of discovering sequential patterns from event-based spatio-temporal data. The problem is defined as follows: for a set of event types and…

cs.DB2017★ 2 cited

Efficient Discovering of Top-K Sequential Patterns in Event-Based Spatio-Temporal Data

Piotr S. Maciąg

We consider the problem of discovering sequential patterns from event-based spatio-temporal data. The dataset is described by a set of event types and their instances. Based on the…