Информационные технологии управления рисками программных проектов
Implementation of IT and program projects seems to be very complicated and taught process, associated with many uncertainties and risks. Sure, this does not mean the rejection of such projects, supposed the more responsibility for the decision making process of new information technologies implementation. To manage various problems which face project managers, it makes sense to use special risk management software. The functionality of modern risk management systems allows identifying risk occurrence, conducting scenario modeling, take the more appropriate managing decisions based on scenario analysis and mathematical calculations. All these functionality will support project manager to optimize his business activities in accordance to risk management practices and ensure better coordination and balance inside the project team. Currently there available a wide range of project management software, but it is reasonable to conduct some analysis in terms of applicability to specific IT projects. The author will review the most appropriate software solutions for the risk management in IT area, conduct competitive analysis and provide some recommendations on software selection.
Ключевые слова: портфельный подход, концепция VaR, хеджирование рисков, хедж-премия, стоимость компании
The book presents the developments directions and researches within the framework of scientific schools of the Institute of Computer Technologies of MESI. It provides the concept of modern information systems architecture and the structure of information space; done the classification of information technologies. Considered the features of constructions of Business Process Management (BPM), Information Management Systems (ERP), Decision Support Systems (DSS). The book defines the theoretical foundations of algorithmization and programming with help of algorithmic languages, the theory of graphs and program synthesis. It presents safety factors and economic aspects of software development. It provides the methods and environments of information infrastructure management based on COBIT, ITIL, clouding computing. The methodology of economic efficiency of information systems and nets and information securities requirements are described. The book formulates conceptual foundations of information systems and data bases design, it presents the analysis of methodologies of information systems modeling.
Uncertainty is a concept associated with data acquisition and analysis, usually appearing in the form of noise or measure error, often due to some technological constraint. In supervised learning, uncertainty affects classification accuracy and yields low quality solutions. For this reason, it is essential to develop machine learning algorithms able to handle efficiently data with imprecision. In this paper we study this problem from a robust optimization perspective. We consider a supervised learning algorithm based on generalized eigenvalues and we provide a robust counterpart formulation and solution in case of ellipsoidal uncertainty sets. We demonstrate the performance of the proposed robust scheme on artificial and benchmark datasets from University of California Irvine (UCI) machine learning repository and we compare results against a robust implementation of Support Vector Machines.