Emergency situations monitoring

using OLAP tehnology

T.G. Penkova, A.V. Korobko, V.V. Nicheporchuk

Institute of Computational Modeling of the Siberian Branch of the Russian Academy of Science, Krasnoyarsk, Russia

penkova_t@icm.krasn.ru, lynx@icm.krasn.ru, valera@icm.krasn.ru

An approach to efficiency improvement of monitoring of the

emergency situations for the Siberian region is suggested in

this paper. It is based on integration of data processing and

storage technologies: Data Warehouse, OLAP and Expert

systems. The data warehouse provides accumulation of data

from isolated monitoring services. The OLAP tools allow a

user to perform multiaspect monitoring of natural and

anthropogenic emergency situations. The expert systems

technology enables user to estimate of the emergencies risk

based on state indicators. Suggested original methods and

tools are embedded in emergency situations monitoring

system “Espla-M”. The system “Espla-M” is implemented in

Ministry of emergency of Siberian Region.

I. INTRODUCTION

Great amount of data should be processed everyday for natural and anthropogenic emergency prevention and forecast in time. Nowadays, an automatic monitoring system is developing in Siberian region for operative observing of technosphere objects and environmental conditions. There are many isolated monitoring services with local operative databases. But, there isn’t integrated monitoring system. Monitoring data consolidation, operative data downloading from heterogeneous sources, on-line analytical data processing and emergencies state control are required to improve efficiency of emergencies monitoring.

In the paper, an approach to efficiency improvement of monitoring of the emergency situations for the Siberian region is suggested. It is based on integration of data processing and storage technologies: Data Warehouse, OLAP and Expert systems. The data warehouse provides accumulation of data from isolated monitoring services. The OLAP tools allow a user to perform multiaspect monitoring of natural and anthropogenic emergency situations. The expert systems technology enables user to estimate of the emergencies risk based on state indicators. Suggested original methods and tools are embedded in emergency situations monitoring system “Espla-M”. The system “Espla-M” is implemented in Ministry of emergency of Siberian Region.

The paper is structured as follows. Section II describes monitoring of the emergency situations for the Siberian region and main problems. Section III presents storage schema and content of the original emergencies data warehouse. In section IV the OLAP tools and a set of

particular analytical models for monitoring fields are considered. Section V describes the approach to emergencies risk estimation based on a comparison of the actual monitoring data with their crucial values. Section VI represents the paper conclusions.

II. EMERGENCY SITUATIONS MONITORING

OF SIBERIAN REGION

The Siberian territory is characterized by high level of natural and anthropogenic emergency hazard. Because of geographic location, climatic conditions and social-economic reasons, the risk of mortality in an emergency in Siberia exceeds ten times acceptable international and average Russian levels [1]. Today, to improve safety of population and territories, a number of emergencies preventing and mitigating actions is implemented. One of the most important preventing actions is the developing of the emergency monitoring and prediction system.

Regional organizational system of emergency monitoring and prediction is the administrative form of territorial management in the field of civil defense, emergencies and elimination of consequences of natural and anthropogenic disasters [2]. The system includes subdivisions of federal authority, region government departments and other organizations that provide supervision of natural and anthropogenic emergency situations in Siberian region. The scheme of information interaction between participants of the emergency monitoring system is presented on Fig.1.

In accordance with established normative acts [2, 3] the Regional organizational system of emergency monitoring and prediction is coordinated by Center of emergency monitoring and prediction of Krasnoyarsk region (CMP).

The main tasks of the CMP are:

realization of regional policy safety of population and territories;

observation of sources natural and anthropogenic emergency situations;

risk factors monitoring;

emergency data accumulation;

analytical data processing and forecasting;

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estimation of emergency risk and consequences;

forming of preventing and mitigating recommendations.

Siberian Regional

Center of Emergency

Monitoring and

Prediction

Government of

Krasnoyarsk region

Krasnoyarsk Research

Institute of Geology

and Mineral Resources

Siberian Department of

Hygrometeorology

and Environment

monitoring

Regional

Fire-prevention

Groups

Automatic Monitoring

System (Sensors)

Center of Emergency

Monitoring and

Prediction of

Krasnoyarsk kr.

Emergency Management

Center of Krasnoyarsk kr.

Emergency Control Ministry

of Krasnoyarsk kr.

Figure 1. The scheme of information interaction

between participants of the emergency monitoring system

Nowadays, there are number of important fields of emergency situations monitoring: meteorology, hydrology, seismology, radiation, spring flood, housing and communal services. Characteristics of technosphere objects and environmental conditions are registered by several different monitoring services.

The system of meteorological and hydrological monitoring in Krasnoyarsk Region consists of 80 weather-stations and 300 hydrological posts. Observations of water bodies are conducted by 9 hydropower stations.

The system of seismic monitoring consists of 12 seismic stations that are located in Baikal-Altai Region of Siberia South and on important industrial objects of Krasnoyarsk Region. Seismic stations can record basic parameters of seismic events in the 300 km area quickly and precisely. The sensors register even low-energy earthquakes and allow observer to distinguish industrial explosions and to control “seismic weather”.

The radiation supervision system ASKRO – Automated system of radiation state monitoring – is presented by 34 controlling posts which are situated in 100 km area of Mining and Chemical Plant. The sensors register value of the expose dose of gamma-ray, meteorological parameters and value of gamma-emitting radionuclides activity in Yenisei River. [4].

The system of technosphere objects monitoring provides control of long-span constructions in crowded

(e.g. heat source, energy supply, fuel supply). The control of these objects is very important for population safety because the number of accidents in the systems of housing and municipal services and severity of their consequences have been increased in the last years.

In addition, the emergency monitoring system includes the number of sensors for automatic control of hydrological, seismic and flood situations. As a result, the CMP accumulates a numerical data (e.g. water and snow level) and nonparametric data, such as photography, video of flood areas and the situation on the highways.

Domain research of regional organizational system of emergency monitoring and prediction allows us to detect some specific problems. There are many isolated monitoring services with local operative databases. There isn’t permanent data flow from the different monitoring services to the CMP. So, accumulated data can’t be processed comprehensively by the CMP.

For informational analytical support of CMP activities, methods and tools of monitoring data integration and processing are required.

The original emergencies monitoring system “ESPLA-M” has been developed for on-line analytical decision making support of CMP at the Institute of Computational Modeling of the Siberian Branch of the Russian Academy of Science. The system is aimed to solve the problems of data integration, comprehensive analytical processing and visualization of analytical results. The goal is reached by simultaneous using of data processing and storage technologies: Data Warehouse, OLAP, GIS and Expert systems.

III. DATA WAREHOUSE OF EMERGENCIES MONITORING

A data warehouse technology is widely used in business analysis and decision making process. Data warehousing is an approach to integrate information from multiple, very large, distributed and heterogeneous operational databases and other sources.

A data warehouse is a subject oriented, nonvolatile, integrated, time variant collection of data in support of management's decisions [5]. The data in the data warehouse can be arranged in different ways. Data structure defines the opportunity for applying such modern technologies as OLAP and Data Mining.

The original data warehouse managing tool (data warehouse manager – DWM) and storage schema have been developed for emergencies monitoring support in CMP [6]. The domain specific requires special data warehouse structure and particular DWM functions.

In accordance with CMP tasks, organizing DW structure includes three basic layers: the layer of stationary storage, the layer of analytical objects and the layer of pre-loading processing (Fig.2).

Figure 2. The DWM objects tree

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The stationary storage layer contains main part of monitoring data and consists of fact tables and dimension tables. The layer of analytical objects includes analytical models, aggregates tables and reports. These analytical objects are used for on-line analytical processing. The layer of pre-loading processing provides loading and preliminary processing of monitoring data from heterogeneous operational databases. This layer includes data sources, loading scripts and temporary tables.

There are historical and actual data in the fact tables in the stationary storage. Historical data includes archive monitoring data. Referenced data as a dimension tables includes Russian, regional and specialized classifiers and glossaries. The stationary storage content is represented in Table 1.

TABLE I. THE STATIONARY STORAGE CONTENT

Monitoring

fields Fact tables Dimension tables

Emergency

situations Emergencies data;

Accidents data.

Territories;

Emergency types;

Emergency scale;

Emergency causes;

Fire-prevention groups.

Meteorological

situation Meteorological data from meteostations

(e.g. temperature,

speed and direction of

wind, atmospheric

precipitation, air

moisture, atmospheric pressure);

Weather forecast;

Meteorological data from sensors.

Territories;

Meteostations;

Dangerous weather events;

Sensors.

Hydrological situation

Water level of the rivers;

Water discharge of the hydro power plants;

Water level of hydro power plants;

Ice conditions.

Territories;

Water bodies;

Hydroposts;

Hydro power plants;

Crucial water levels of the rivers;

Crucial water levels of hydro power

plants;

Acceptable levels of

water discharge;

Freezing-over types.

Seismological

situation Seismic events

characteristic (e.g. depth, magnitude,

diameter of the

transient cavity, epicenter coordinates).

Territories;

Seismic stations;

Seismic event types.

Radiation

situation Expose dose of

gamma-ray;

Volumetric activity of

gamma-emitting radio nuclides in water.

Territories;

Sensors;

Crucial doses of gamma-ray.

Flood

situation Flood areas;

Condition of hydraulic facilities.

Territories;

Hydraulic facilities types;

Hydraulic facilities states;

Water bodies;

Dangerous objects.

Housing and

communal services

Data about the preparation for heating

season (e.g. fuel

Territories;

Housing and

Monitoring

fields Fact tables Dimension tables

reserves, fuel supply,

equipment reserve,

material and financial resources);

Condition of objects

(e.g. heat source, energy source, electric

power lines,

communication);

Housing and

communal accidents.

communal objects;

Housing and

communal objects types;

Housing and communal accidents

types;

Fuel types;

Financing sources;

Communal services.

The analytical models of the analytical objects layer are set of data marts for on-line analytical processing of monitoring data. Each data mart has a number of prepared data representations as the analytical models (OLAP-models). The aggregate tables store intermediate results for complex analysis. Important results of analytical processing can be represented as the analytical reports and can be saved in the analytical objects layer too.

The data sources of the pre-loading processing layer provide connections with heterogeneous operational databases. The loading scripts enable DWM to load and refresh monitoring data from different sources, according to timetable. The temporary tables of the pre-loading processing layer are used for storing intermediate loading data during their conversion.

In addition, DWM has special section “The analytic portfolios” that consists of analytical portfolios – a set of analytical models for different user roles.

So, the data warehouse of emergencies monitoring accumulates data from different monitoring services for the further multiaspect analytical processing.

IV. ON-LINE ANALYTICAL PROCESSING

OF MONITORING DATA

The On-line analytical processing (OLAP) is used for analytical decision making support [7]. The OLAP tools and methods developing and extension of OLAP implementing scope are of importance today.

The DWM is integrated with original on-line analytical processing tools for emergences monitoring. The “Espla-M” means allows user to create and modify analytical models and to use these for analytical portfolio forming. OLAP-models are used for situations multiaspect analysis, on-line estimating of the emergencies risk and forecasting of the emergencies consequences.

A set of particular OLAP-models have been developed for each of the monitoring field.

Field: Emergency situations

OLAP-model: Controlled emergencies Dimensions: Year, Emergency date, Emergency liquidation date, Place, Emergency type, Emergency scale and Emergency cause. Facts: Died people, Victims, Emergencies count and Material damage.

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Field: Meteorological situation

OLAP-model: Temperature Dimensions: Year, Meteostation Facts: Temperature

OLAP-model: Speed and direction of wind Dimensions: Year, Meteostation Facts: Speed of wind and Direction of wind

OLAP-model: Weather forecast Dimensions: Date, Territory Facts: Weather forecast description

OLAP-model: Meteorological data from sensors Dimensions: Year, Place, Date, Sensor Facts: Temperature, Speed and direction of wind, Air moisture, Atmospheric pressure

Field: Hydrological situation

OLAP-model: Water level of the rivers Dimensions: Year, Date, Month and day, Hydroposts, River Facts: Water level of the rivers, Crucial water level of the rivers, Exceeding of crucial water level

OLAP-model: Water discharge of the hydro power plants

Dimensions: Year, Date, Hydro power plant Facts: Minimal acceptable level of discharge, Maximal acceptable level of discharge, Water discharge level

OLAP-model: Ice conditions Dimensions: Year, Date, Hydroposts, River, Freezing-over type Facts: Count of ice events, Ice event description

Field: Seismological situation

OLAP-model: Seismic events Dimensions: Year, Date, Place, Seismic station, Seismic event type Facts: depth, magnitude, diameter of the transient cavity, epicenter coordinates

Field: Radiation situation

OLAP-model: Expose dose of gamma-ray Dimensions: Date, Place, Sensor Facts: Expose dose of gamma-ray

OLAP-model: Dimensions: Date, Place, Sensor Facts: Volumetric activity of gamma-emitting radio nuclides in water

Field: Flood situation

OLAP-model: Flood forecast Dimensions: Year, Territory, Hydroposts, River, Date of expected flood, Date of fact flood in the last year Facts: Minimal expected water level, Maximal expected water level, Fact water level in the last year

OLAP-model: Hydraulic facilities condition

Dimensions: Year, Place, Water body, Hydraulic facilities, Hydraulic facilities type, Hydraulic facilities state Facts: Volume of hydraulic facility, Square of hydraulic facility

Field: Housing and communal services

OLAP-model: Housing and communal accidences Dimensions: Year, Place, Accidence date, Liquidation date, Emergences types, Communal services Facts: Adult victims, Children victims, Count of accidence, Accidence causes description, Liquidation actions description

OLAP-model: Housing and communal accidences forecast

Dimensions: Year, Place, Report date, Housing and communal object Facts: Population, Probable victims, Probable accidence description, Description of the probable liquidation actions

OLAP-model: Fuel reserve Dimensions: Year, Territory, Fuel types, Report date Facts: Requirement of fuel, Fact reserve of fuel, Readiness

The OLAP-models are used for analytical portfolio forming in accordance with different user’s roles. The OLAP-models can be represented as statistical table, cross-table, diagram and map for geographical data. All these facilities of the data visualization give user opportunity to change traditional view point and discover new analytical relations between monitoring data [8-10].

The statistical table represents analytical processing result as a relational table (Fig.3). Using statistical tables, user can filter data, change font color for fact value interval and sort data. Moreover, the statistical table enables user to process data with standard statistic functions: expectation, variance, quintiles, correlation and cluster analysis.

Figure 3. The statistical table

The cross-table provides interface for intuitive manipulation of analytical results. It allows user to change fields order in vertical and horizontal header of table, interchange facts and dimensions (table pivoting), choose needed level of aggregation and detailing by dimension hierarchy, “slice” and “dice” data set (Fig.4). OLAP

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operations enable users to navigate data flexibly, define relevant data sets, analyze data at different granularity and visualize results in different forms.

Figure 4. The cross-table

The “Espla-M” analytical tools support various types of diagrams: horizontal and vertical bar charts, pie charts, point charts, line charts, area charts, etc. The diagram tools provide the selection of user preferences. The user can choose a dimension and generate series of diagrams, according to dimension values. The diagram is the special OLAP tool. It allows user to do data visual comparison and forecast. For example, if user constructs hydrographs for several years, he can define “analog-year” and forecast water level of river (Fig.5).

Figure 5. The river hydrograph

If the OLAP-model has geographical dimension, it can be processed as map (Fig.6).

Figure 6. The map

Map layers and polygons relate to analytical model facts. Values or indication marks of facts are represented by color of map objects. It is the most demonstrative way of geographical data representation.

The OLAP tools allow a user to perform multiaspect monitoring of natural and anthropogenic emergency situations. Using the OLAP technology into new fields and for new tasks improves OLAP tools and methods.

V. ON-LINE STATE CONTROL

The original visualization tool “Semaphore” has been developed for on-line control of the monitoring parameters in system “Espla-M” (Fig.7). This tool allows user to estimate of the emergencies risk based on a comparison of actual monitoring data with their crucial values. There are three levels of emergencies risk:

“Green” – the situation is normal; the parameter value doesn’t exceed crucial value.

“Yellow” – the situation requires attention; the parameter value is approaching to crucial value.

“Red” – the situation is dangerous; the parameter value exceeds crucial value.

The absence of actual data is marked as grey indicator.

The criterions of the emergencies risk are represented in Table 2.

TABLE II. THE CRITERIONS OF EMERGENCIES RISK

№ Parameter Cause Level

1. Emergency situations

1.1 Emergencies

Accidents (EA)

EA count = 0 green

EA count > 0, 1 or

more days ago yellow

EA count > 0 today red

2. Meteorological situation

2.1 Temperature (T)

else green

30 ≤ T < 35

or -40 < T ≤ -35

yellow

T ≥ 35 or T ≤ -40 red

2.2 Speed (WS) and

direction of wind

0 < WS < 15 green

15 ≤ WS < 25 yellow

WS ≥ 25 red

3. Hydrological situation

3.1 Water level of the

rivers (RL)

0 < RL< critical-10% green

critical -10% ≤ RL <

critical level yellow

RL ≥ critical level red

3.2

Water level of hydro power plants (HL)

min+20<HL<max-

20 green

min<HL≤min+20 or

max+20≤HL<max yellow

HL≤min or HL≥max red

3.3 Ice conditions

else green

ice jam yellow

- red

4. Seismological situation

4.1 Earthquake,

magnitude (M)

M < 3 green

3 ≤ M < 5 yellow

M ≥ 5 red

5. Radiation situation

5.1 Expose dose of

gamma-ray (ED)

ED < 20 green

20 ≤ ED < 40 yellow

ED ≥ 40 red

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№ Parameter Cause Level

6. Flood situation

6.1 Flood areas

else green

residential buildings

are in flood area yellow

dangerous industrial

objects are in flood area

red

6.2 Condition of hydraulic facilities

else green

pre-crash state yellow

state of emergency red

7. Housing and communal services condition

7.1

Housing and

communal accidents (HA)

HA count = 0 green

HA count > 0, 1 or

more days ago yellow

HA count > 0 today red

As usual, the intervals of acceptable values are individual for each monitoring parameter. The crucial values for meteorological, hydrological, seismological, radiation situations are determined in accordance with natural characteristics of the Siberia territory.

Figure 7. The “Semaphore” tool

The original result of the research is OLAP-models and “Semaphore” tool integration. Each of analytical models is associated with significant monitoring parameter and indicator causes. So, it allows user to observe field’s and model parameter’s states and detail analytical information. On-line state control provides immediate emergency reaction of the CMP.

VI. CONLUSION

The integration of OLAP, data warehouse and expert systems technologies improves an efficient of emergency situation monitoring. The data warehouse technology provides accumulation of data from different monitoring services and storing of processing methods as the prepared OLAP-models and analytical portfolios. The OLAP technology allows a user to perform multiaspect monitoring of natural and anthropogenic emergency situations. The expert systems technology enables user to estimate of the emergencies risk based on state indicators. On-line state control provides immediate emergency reaction of the CMP.

Using the data processing and storage technologies into new domains and for new tasks solving improves tools and methods of decision making support.

ACKNOWLEDGMENT

This paper was supported by a development contract (No: 214, 25.08.2011), Emergency Control Ministry of Krasnoyarsk region.

REFERENCES

[1] Safety of Russia (2001). Legal, socio-economic, scientific and technical aspects. Regional safety problems. Krasnoyarsk Region. Moscow: Znaniye, 576 p.

[2] The Low of Emergency Control Ministry of Russia, “Functional monitoring system of the State system of emergencies prevention and elimination” № 94, 04.03.2011.

[3] The Regulation of Krasnoyarsk regoin government. “Regional organizational system of emergency monitoring and prediction” № 80-p, 09.02.2011.

[4] Encyclopedia “Natural Resources of Krasnoyarsk Region”. Krasnoyarsk: KNIIGiMS, 2007. pp. 441-443.

[5] W. Inmon “Building the Data Warehouse”, N.Y.: John Willey & Sons, 1992.

[6] K.V. Badmaeva, T.G. Penkova, V.V. Nicheporchuk “Designing a specialized Data Warehousefor emergency monitoring”, Journal of the Siberian State Aerospace University, Vol.5(38), pp.14-18.

[7] E.F. Codd “Providing OLAP to user-analysts: An IT mandate”, Codd and Associates, 1993.

[8] L.F. Nozhenkova, V.V. Shaidurov “OLAP tehnologies of on-line analitical support of administration”, Information tehnology and computing systems, 2010, Vol. 2, pp. 15-27.

[9] A. Korobko, T. Penkova “On-line analytical processing based on Formal concept analysis”, Procedia Computer Science, ELSEVIER, 2010, Vol. 1., Iss. 1, pp. 2305-2311.

[10] L.F. Nozhenkova, A.A. Evsukov, A.I. Nozhenkov “Governing and geo-informatic modelling methods in OLAP”, Journal of the Siberian Federal University. Engineering&Technologies 2009. Vol.1, pp.49-58.

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