Agent-based Anomalies Monitoring in Distributed Systems

In this paper an agent-based approach for anomalies monitoring in distributed systems such as computer networks, or Grid systems is proposed. This approach envisages on-line and off-line monitoring in order to analyze users’ activity. On-line monitoring is carried in real time, and is used to predict user actions. Off-line monitoring is done after the user has ended his work, and is based on the analysis of statistical information obtained during user’s work. In both cases neural networks are used in order to predict user actions and to distinguish normal and anomalous user behavior.

E-learning

E-learning means learning via electronic means and is therefore an all-embracing term covering learning via an electronic device. The "expectations" and "realities" for each of the delivery mechanisms within the electronic arena vary greatly for not just the learners themselves, but also the site providers. Because of this, each of these learning systems has vastly different design principles, which is not always understood by those unfamiliar with technology. What is appropriate for a CD-ROM off-line system is generally inappropriate for an on- line internet system. So when designing an e-learning system it is important to understand how the information is to be accessed by the learner. This paper will identify and suggest some ways to avoid e-learning's pitfalls and reap its rewards.

Intelligent Model of User Behavior in Distributed Systems

We present a complex neural network model of user behavior in distributed systems. The model reflects both dynamical and statistical features of user behavior and consists of three components: on-line and off-line models and change detection module. On-line model reflects dynamical features by predicting user actions on the basis of previous ones. Off-line model is based on the analysis of statistical parameters of user behavior. In both cases neural networks are used to reveal uncharacteristic activity of users. Change detection module is intended for trends analysis in user behavior. The efficiency of complex model is verified on real data of users of Space Research Institute of NASU-NSAU.

Conceptual Information Compression and Efficient Pattern Search

This paper introduces an encoding of knowledge representation statements as regular languages and proposes a two-phase approach to processing of explicitly declared conceptual information. The idea is presented for the simple conceptual graphs where conceptual pattern search is implemented by the so called projection operation. Projection calculations are organised into off-line preprocessing and run-time computations. This enables fast run-time treatment of NP-complete problems, given that the intermediate results of the off-line phase are kept in suitable data structures. The experiments with randomly-generated, middle-size knowledge bases support the claim that the suggested approach radically improves the run-time conceptual pattern search.

О Некоторых Труднорешаемых Задачах Помехоустойчивого Анализа Структурированных Данных

* Работа поддержана грантами РФФИ 09-01-00032, 07-07-00022 и грантом АВЦП Рособразования 2.1.1/3235

Задачи Помехоустойчивого Анализа и Распознавания Последовательностей, Включающих Повторяющиеся Упорядоченные Наборы Вектор–Фрагментов

* Работа поддержана грантами РФФИ 09-01-00032, 07-07-00022 и грантом АВЦП Рособразования 2.1.1/3235

Об Одной Задаче Распознавания Последовательности, Включающей Повторяющийся Вектор

* Работа поддержана грантами РФФИ 09-01-00032, 07-07-00022 и грантом АВЦП Рособразования 2.1.1/3235

Model Mining and Efficient Verification of Software Product Lines

Software product line modeling aims at capturing a set of software products in an economic yet meaningful way. We introduce a class of variability models that capture the sharing between the software artifacts forming the products of a software product line (SPL) in a hierarchical fashion, in terms of commonalities and orthogonalities. Such models are useful when analyzing and verifying all products of an SPL, since they provide a scheme for divide-and-conquer-style decomposition of the analysis or verification problem at hand. We define an abstract class of SPLs for which variability models can be constructed that are optimal w.r.t. the chosen representation of sharing. We show how the constructed models can be fed into a previously developed algorithmic technique for compositional verification of control-flow temporal safety properties, so that the properties to be verified are iteratively decomposed into simpler ones over orthogonal parts of the SPL, and are not re-verified over the shared parts. We provide tool support for our technique, and evaluate our tool on a small but realistic SPL of cash desks.