Monika Chuchro

A Model of a System for Stream Data Storage and Analysis Dedicated to Sensor Networks of Embankment Monitoring

Contemporary monitoring systems are a source of data streams. Processing of this data is an interesting issue from both a performance and data storage perspective. It is worth paying attention to the concept of stream database management systems, which are a hybrid that allows for efficient analysis of the data stream and provide a set of implemented statistical methods.

A Concept of Time Windows Length Selection in Stream Databases in the Context of Sensor Networks Monitoring

Monitoring systems are a source of large amounts of data. These streams of data flow down as information which, in the case of sensor networks is often associated with the measurement of the selected physical signals. Processing of these data is a non-trivial issue, because accurate calculations often require dedicated solutions and large computing power.

A decision support system for emergency flood embankment stability

Abstract This article presents a concept of decision support system for emergency flood embankment stability. The proposed methodology is based on the analysis of data from both a flood em-bankment measurement network and generated through numerical modeling. Decisions about the risk of embankment failure are made based on this analysis. The authors present both the general concept of the system as well as a preliminary detailed description of the system components.

Reducing Flood Risk using Computer System for Monitoring River Embankments

Abstract In order to learn about the phenomena occurring in flood embankment under the influence of external factors, including the increasing water level in the river during floods, a Computer System for Monitoring River Embankment (ISMOP) was developed using an experimental flood embankment. The project was carried out by a consortium consisting of AGH University of Science and Technology departments (Computer Science, Hydrogeology and Engineering Geology, Geoinformatics and Applied Computer Science and two companies (NEOSENTIO and SWECO Hydroprojekt Kraków) in co-operation with the Czernichów Community Council. An experimental flood embankment was built with two parallel sections with a length of 150 m and a height of 4.5 m, connected by a meandering, creating a reservoir that can be filled with water. For the construction of the embankment, different types of soils were used in all the five sections. Inside the flood embankment 1300 sensors are placed, including sensors for temperature, pore pressure, vertical displacements, as well as inclinometers. Also fiber optic strands, capable of measuring the temperature of the flood embankment on the upstream side, are located inside the experimental embankment [ismop.pl]. Together with the real experiments, numerical modelling using the Itasca Flac 2D 7.0 was performed in order to describe the impact of water pressing on the flood embankment and the impact of increasing and decreasing reservoir water level on the phenomena that occur within the embankment. The results of modelling compared with the real sensor data allowed the evaluation of the current and future state of the embankment. Based on the data measured by the sensors and data received during the numerical modelling, a group of algorithms that allowed detection of anomaly phenomena was developed.

Limits of Colour Perception in the Context of Minimum Dimensions in Digital Cartography

The commonly used methods in digital cartography are based on the minimum dimensions of black and white objects. This article presents a solution in which both the colour of the symbols and the background on which they are presented are relevant in the context of setting the minimum dimensions of the objects on a map. To achieve this, the authors have developed a perception coefficient that is an extension of the formal definitions of minimum object dimensions. In support of the presented solutions, the authors offer several cartographic examples. The article also contains experimental research that examines the impact of colour on the recognition of objects by means of specially prepared surveys. These results are compared against the theoretical values of the perception coefficient. The research objective was achieved by developing new solutions that could be used in the cartographic production processes of any national map agency.

Optimal selection of numerical models for flood embankment pore pressure and temperature data

The aim of the ISMOP project is to study processes in earthen flood embankments: water filtration, pore pressure changes, and temperature changes due to varying water levels in the riverbed. Developing a system for continuous monitoring of flood embankment stability is the main goal of this project. A full-size earthen flood embankment with built-in sensors was built in Czernichow and used to conduct experiments involving the simulation of different flood waves, with parameters mostly measured at time intervals of 15 minutes. Numerical modelling—in addition to providing information about phenomena occurring in the embankment due to external factors and changes in water level—could be used to assess the state of the embankment. Modelling was performed using Itasca Flac 2D 7.0 with an assumed grid cell size of 10x10 cm. The water level in the embankment simulated the water flow in the Wisla River and the temperature of the air and water. Data about the state of the flood embankment was exported every hour. Using numerical models and real experiment data, a model-driven module was used to perform comparisons. Analyses of each half-section of the flood embankment were carried out separately using similarity measures and an aggregate window. For the tests, the North-West (NW) half cross-section of the embankment was chosen, which contains pore pressure and temperature sensors UT6 to UT10. The water level in the embankment was raised to a height of 3m; the best numerical model was considered the one that best matched the actual data recorded by the sensors during the experiment. The experiment period was from 9pm on 29/08/2016 to 9am on 03/09/2016. Seventeen numerical models of the water level rising to 2, 3, and 4 meters were compared against real experimental data from the NW half cross-section. The first step was to verify the similarity between the incoming data from the sensors. If the correlation value exceeded 0.8, the data from the sensors was averaged. The experimental data was then compared against the numerical models using least absolute deviations L1-Norm. The L1-Norm varied from 26 to 32, depending on window length and the numerical model used.

A Proposal of Hybrid Spatial Indexing for Addressing the Measurement Points in Monitoring Sensor Networks

One of the important features of data analysis methods in the area of continuous surveillance systems is a computation time. This article contains a research that is focused on improving the performance of processing by the most efficient possible indexation of spatial data. The authors proposed a structure of indexes implementation based on layered grouping of sensors, so as to reduce the amount of data in time windows. This allows to compare data at the layer-layer level, thereby reducing the problem of comparisons between all sensors.

Using Data Mining Techniques for Diagnostic of Virtual Systems Under Control of KVM

Analysis of logs of remote network services is one of the most difficult and time consuming task—its amount and variety of types are still growing. With the increasing number of services increases the amount of logs generated by computer programs and their analysis becomes impossible for the common user. However, the same analysis is essential because it provides a large amount of information necessary for the maintenance of the system in good shape thus ensuring the safety of their users. All ways of relevant information filtering, which reduce the log for further analysis, require human expertise and too much work. Nowadays, researches take the advantage of data mining with techniques such as genetic and clustering algorithms, neural networks etc., to analyze system’s security logs in order to detect intrusions or suspicious activity. Some of these techniques make it possible to achieve satisfactory results, yet requiring a very large number of attributes gathered by network traffic to detect useful information. To solve this problem we use and evaluate some data mining techniques (Decision Trees, Correspondence Analysis and Hierarchical Clustering) in a reduced number of attributes on some log data sets acquired from a real network, in order to classify traffic logs as normal or suspicious. The results obtained allow an independent interpretation and to determine which attributes were used to make a decision. This approach reduces the number of logs the administrator is forced to view, also contributes to improve efficiency and help identify new types and sources of attacks.