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1

Thespis: Causally-consistent OLTP

Camilleri Carl, Vella Joseph G., Nezval Vitezslav

Annals of Computer Science and Information Systems

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2021

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Vol. 25

261--269

EN

Data Consistency defines the validity of a data set according to some set of rules, and different levels of data consistency have been proposed. Causal consistency is the strongest type of consistency possible when data is stored in multiple locations, and fault tolerance is desired. Thespis is a middleware that leverages the Actor model to implement causal consistency over a DBMS, whilst abstracting complexities for application developers behind a REST interface. ThespisTRX is an extension that provides read-only transaction capabilities, whilst ThespisDIIP is another extension that handles distributed integrity invariant preservation. Here, we analyse standard transactional workloads on the relational data model, which is richer than the key-value data model supported by the Thespis interface. We show the applicability of the Thespis approach for this data model by designing new operations for the Thespis interface, which ensure correct execution of such workloads in a convergent, causally consistent distributed environment.

2

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

Acker Alexander, Wittkopp Thorsten, Nedelkoski Sasho, Bogatinovski Jasmin, Kao Odej

Annals of Computer Science and Information Systems

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2020

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Vol. 21

7--10

EN

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.

3

Data Mining-Based Phishing Detection

Bohacik Jan, Skula Ivan, Zabovsky Michal

Annals of Computer Science and Information Systems

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2020

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Vol. 21

27--30

EN

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.

4

The Impedance Mismatch in Light of the Unified State Model

Wiśniewski P., Burzańska M., Stencel K.

Fundamenta Informaticae

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2012

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Vol. 120, nr 3/4

359-374

EN

In this paper we discuss the misunderstanding that have arisen over the years around the broadly defined term of the object-relational impedance mismatch. It occurs in various aspects of database application programming. There are three concerns judged the most important: mismatching data models, mismatching binding times and mismatching object lifecycle. This paper focuses on the data model mismatch. We introduce the common state theory, i.e. a unified model of objects in popular programming languages and databases. The proposed model exploits and emphasizes common properties of all these objects. Using our model we demonstrate that there are notably more similarities than differences. We conclude that the impact of the mismatch of data models can be significantly reduced.

5

Ewolucja realizacji zapytań w systemach akwizycji wiedzy

Jagielski J., Wnęk P.

Zeszyty Naukowe Akademii Marynarki Wojennej

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2009

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R. 50 nr 177B

109-116

PL

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.

EN

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.

6

Modelling and optimisation of cogeneration plant thermal cycle

Panowski M., Klajny R.

Archives of Thermodynamics

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2006

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Vol. 27, no. 4

179-188

EN

The optimal control of the process of energy production is the cheapest way of increasing its thermal efficiency. However, the possible benefits could be achieved both by setting optimal values of thermodynamic parameters as well as by decreasing the time span of change of these values, dependent upon change of cogeneration plant energy load. Computer-aided modelling and optimisation applied to these two aspects of steering can be done with use of accessible computer software and should lead to economical profits through the thermal cycle efficiency increase. A new methodology of optimal thermal cycle steering is presented in this paper. This methodology is based on local data models formulated owing to data obtained from the simulation software. Optimisation procedures are applied to found the best values of thermodynamic parameters of the thermal cycle which realise one of the important steering aspects. Moreover, because of using simulation software the calculations are fast enough to realise also the second one. This paper also presents application of methodology and evaluated numerical procedures to optimisation of thermal cycle of cogeneration power plant for which the simulation model was also elaborated. Optimisation process was made for different thermodynamic parameters with respect to total egzergetic and energetic efficiency of the cycle.