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

Distributed Visualization Systems in Remote Sensing Data Processing Grid

Implementation of GEOSS/GMES initiative requires creation and integration of service providers, most of which provide geospatial data output from Grid system to interactive user. In this paper approaches of DOS- centers (service providers) integration used in Ukrainian segment of GEOSS/GMES will be considered and template solutions for geospatial data visualization subsystems will be suggested. Developed patterns are implemented in DOS center of Space Research Institute of National Academy of Science of Ukraine and National Space Agency of Ukraine (NASU-NSAU)

WORKFLOW MODELLING IN GRID SYSTEM FOR SATELLITE DATA PROCESSING

Abstract: This paper focuses on a problem of Grid system decomposition by developing its object model. Unified Modelling Language (UML) is used as a formalization tool. This approach is motivated by the complexity of the system being analysed and the need for simulation model design

Grid Approach to Satellite Monitoring Systems Integration

This paper highlights the challenges of satellite monitoring systems integration, in particular based on Grid platform, and reviews possible solutions for these problems. We describe integration issues on different levels: data integration level and task management level (job submission in terms of Grid). We show example of described technologies for integration of monitoring systems of Ukraine (National Space Agency of Ukraine, NASU) and Russia (Space Research Institute RAS, IKI RAN). Another example refers to the development of InterGrid infrastructure that integrates several regional and national Grid systems: Ukrainian Academician Grid (with Satellite data processing Grid segment) and RSGS Grid (Chinese Academy of Sciences)

Intelligent Computations for Flood Monitoring

Floods represent the most devastating natural hazards in the world, affecting more people and causing more property damage than any other natural phenomena. One of the important problems associated with flood monitoring is flood extent extraction from satellite imagery, since it is impractical to acquire the flood area through field observations. This paper presents a method to flood extent extraction from synthetic-aperture radar (SAR) images that is based on intelligent computations. In particular, we apply artificial neural networks, self-organizing Kohonen’s maps (SOMs), for SAR image segmentation and classification. We tested our approach to process data from three different satellite sensors: ERS-2/SAR (during flooding on Tisza river, Ukraine and Hungary, 2001), ENVISAT/ASAR WSM (Wide Swath Mode) and RADARSAT-1 (during flooding on Huaihe river, China, 2007). Obtained results showed the efficiency of our approach

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

Metadata and Geospatial Data Processing on the Base of XML and Grid

The software architecture and development consideration for open metadata extraction and processing framework are outlined. Special attention is paid to the aspects of reliability and fault tolerance. Grid infrastructure is shown as useful backend for general-purpose task

Earth observation data science programs in National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"

Nowadays, satellite monitoring and geospatial intelligence are the drivers of digital transformation and economic development all over the world. At the same time, in Ukraine there is no higher education programs dealing with Earth observation data science or machine and deep learning on remote sensing data. In 2019, Space Research Institute in cooperation with the Department of Mathematical Modeling and Data Analysis (MMDA department) of National Technical University of Ukraine “Kyiv Polytechnic Institute” (NTUU “KPI”) joined the Copernicus Academy network for deeper involvement into educational activities related to the Copernicus program. As Copernicus Academy laboratory, we contribute into international scientific and innovative international programs, provide trainings and master classes for students, regional administrations and teachers. Most of our projects deal with machine learning on satellite and auxiliary data and satellite monitoring applications and require deep knowledge of mathematics, machine learning and data analysis. To facilitate involvement of students into our projects, in 2021 MMDA department established a certificate program “Models and methods of intellectual analysis of heterogeneous data” for master students of Applied Mathematics specialty (https://mmda.ipt.kpi.ua/en/certificate-program-models-and-methods-of-intellectual-analysis-of-heterogeneous-data/). It includes big geospatial data analysis, geospatial information technologies and deep learning for satellite and heterogeneous data. It allows students to dive into Earth observation domain and bridge the gap between applied mathematics and satellite monitoring. Students do their master’s research within international projects, in particular, Horizon-2020 e-shape or NASA project “High-Impact Hot Spots of Land Cover Land Use Change: Ukraine and Neighboring Countries”. They develop machine learning models for different applications based on Copernicus data and implement them on different cloud platforms, such as GEE, CREODIAS and AWS. Some of them develop their startup projects based on this research. For further development of our program and better motivation of our students we are interested in collaboration with similar programs for academic mobility of students and professors and looking for innovative educational forms and resources

Data Assimilation Technique For Flood Monitoring and Prediction

This paper focuses on the development of methods and cascade of models for flood monitoring and forecasting and its implementation in Grid environment. The processing of satellite data for flood extent mapping is done using neural networks. For flood forecasting we use cascade of models: regional numerical weather prediction (NWP) model, hydrological model and hydraulic model. Implementation of developed methods and models in the Grid infrastructure and related projects are discussed

Geospatial Analysis of Leased Lands in Ukraine

With the opening of the land market, the analysis of leased land in Ukraine becomes more and more important. On the base of remote sensing data we can use additional capabilities and tools to analyze and predict the state of the land. Thanks to the public cadastral map, we can check our results on the base of up-to date official information. In this article we have analyzed the large leased fields in the Kyiv region. In particular, operations had performed on geospatial objects with georeference, as on sets of pixels. The main purpose of this work is to use a crop classification map and geographic information systems in order to find large areas, and check the information about the tenant using the service of state geocadastre