2017, Tom 25 Nr 2
Stały URI dla kolekcji
Przeglądaj
Ostatnie zgłoszenia
Pozycja Resolving Classical Concurrency Problems Using Outlier Detection(Wydawnictwo Politechniki Łódzkiej, 2017) Smoliński, MateuszIn this paper outlier detection is used to determine anomaly between tasks to prevent occurrence of resource conflicts in prepared schedule. Determined conflictless schedule bases on controlling access of tasks to groups of shared resources. Proposed approach allows to prepare conflictless schedule of efficient parallel task processing without resource conflicts and is dedicated to environments of task processing with high contention of shared resources. In this paper the outlier detection is used to resolve two classical concurrency problems: readers and writers and dining philosophers. In opposition to other known solutions of concurrency problems, proposed approach can be applied to solve different problems and do not require to use additional mechanisms of task synchronization. The universality of proposed approach allows to prepare conflictless schedule even in environments, where classical concurrency problems will be significantly expanded and complicated.Pozycja Outlier Mining Using the DBSCAN Algorithm(Wydawnictwo Politechniki Łódzkiej, 2017) Nowak-Brzezińska, Agnieszka; Xięski, TomaszThis paper introduces an approach to outlier mining in the context of a real-world dataset containing information about the mobile transceivers operation. The goal of the paper is to analyze the influence of using different similarity measures and multiple values of input parameters for the densitybased clustering algorithm on the number of outliers discovered during the mining process. The results of the experiments are presented in section 4 in order to discuss the significance of the analyzed parameters.Pozycja Outlier Phenomenon in Data Interpretation for One Waves Scattering Problem(Wydawnictwo Politechniki Łódzkiej, 2017) Emets, Volodymyr F.; Rogowski, JanAn outlier is an observation (or measurement) that is different with respect to the other values contained in a given data set. Outliers can occur due to several causes. The measurement can be incorrectly observed, recorded or processed or otherwise is correctly measured but represents a rare event. In this paper it is shown that observed data can contain values that differ from expected ones and can be interpreted as an outlier, but in fact are caused by a specific physical phenomenon.Pozycja Outlier Detection Using the Multiobjective Genetic Algorithm(Wydawnictwo Politechniki Łódzkiej, 2017) Duraj, Agnieszka; Chomątek, ŁukaszSince almost all datasets may be affected by the presence of anomalies which may skew the interpretation of data, outlier detection has become a crucial element of many datamining applications. Despite the fact that several methods of outlier detection have been proposed in the literature, there is still a need to look for new, more effective ones. This paper presents a new approach to outlier identification based on genetic algorithms. The study evaluates the performance and examines the features of several multiobjective genetic algorithms.Pozycja Outlier Mining in Rule-Based Knowledge Bases(Wydawnictwo Politechniki Łódzkiej, 2017) Nowak-Brzezińska, AgnieszkaThis paper introduces an approach to outlier mining in the context of rule-based knowledge bases. Rules in knowledge bases are a very specific type of data representation and it is necessary to analyze them carefully, especially when they differ from each other. The goal of the paper is to analyze the influence of using different similarity measures and clustering methods on the number of outliers discovered during the mining process. The results of the experiments are presented in Section 6 in order to discuss the significance of the analyzed parameters.