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w słowach kluczowych: modele danych

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Wdrożenie Polskiego Modelu Danych Podstawowych w strukturze katalogów produktowych GDSN

100%

Muszyński K.

Logistyka

2016

tom nr 4

65--65

Obiektowy model danych dla budzetu sprzedazy

75%

Nadolna B.

2007

tom 48

263-272

Ewolucja realizacji zapytań w systemach akwizycji wiedzy

75%

Jagielski J. , Wnęk P.

tom R. 50 nr 177B

109-116

W artykule przedstawiono rozwój modeli baz danych I ewolucję realizacji zapytań w systemach akwizycji wiedzy z baz danych. Omówiono modele: hierarchiczny, sieciowy i relacyjny. Ich rozwinięciem są modele rozmyte oraz obiektowe (semantyczne). Wskazano prawdopodobne kierunki rozwoju systemów baz danych i realizacji zapytań.

The paper presents the development of databases models and the evolution of questions realization In knowledge acquisition from databases systems. Hierarchical, network and relational models has been discussed. Their deployments are fuzzy models and object-oriented (semantic) models. Probable directions of the development of databases systems and queries realizations has been showed.

Wybrane aspekty metodyczne realizacji projektu badań przestrzennych

63%

Binczyk A.

tom 132

Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction

51%

Acker A. , Wittkopp T. , Nedelkoski S. , Bogatinovski J. , Kao O.

tom Vol. 21

7--10

The rapid growth and distribution of IT systems increases their complexity and aggravates operation and maintenance. To sustain control over large sets of hosts and the connecting networks, monitoring solutions are employed and constantly enhanced. They collect diverse key performance indicators (KPIs) (e.g. CPU utilization, allocated memory, etc.) and provide detailed information about the system state. Storing such metrics over a period of time naturally raises the motivation of predicting future KPI progress based on past observations. This allows different ahead of time optimizations like anomaly detection or predictive maintenance. Predicting the future progress of KPIs can be defined as a time series forecasting problem. Although, a variety of time series forecasting methods exist, forecasting the progress of IT system KPIs is very hard. First, KPI types like CPU utilization or allocated memory are very different and hard to be modelled by the same model. Second, system components are interconnected and constantly changing due to soft- or firmware updates and hardware modernization. Thus a frequent model retraining or fine-tuning must be expected. Therefore, we propose a lightweight solution for KPI series prediction based on historic observations. It consists of a weighted heterogeneous ensemble method composed of two models - a neural network and a mean predictor. As ensemble method a weighted summation is used, whereby a heuristic is employed to set the weights. The lightweight nature allows to train models individually on each KPI series and makes model retraining feasible when system changes occur. The modelling approach is evaluated on the available FedCSIS 2020 challenge dataset and achieves an overall R^2 score of 0.10 on the preliminary 10\% test data and 0.15 on the complete test data. We publish our code on the following github repository: https://github.com/citlab/fed\_challenge.

Data Mining-Based Phishing Detection

51%

Bohacik J. , Skula I. , Zabovsky M.

tom Vol. 21

27--30

Webpages can be faked easily nowadays and as there are many internet users, it is not hard to find some becoming victims of them. Simultaneously, it is not uncommon these days that more and more activities such as banking and shopping are being moved to the internet, which may lead to huge financial losses. In this paper, a developed Chrome plugin for data mining-based detection of phishing webpages is described. The plugin is written in JavaScript and it uses a C4.5 decision tree model created on the basis of collected data with eight describing attributes. The usability of the model is validated with 10-fold cross-validation and the computation of sensitivity, specificity and overall accuracy. The achieved results of experiments are promising.