Ondrej Habala

Data mining and integration for predicting significant meteorological phenomena

Abstract This paper describes the planned contribution of the project Data Mining Meteo (DMM) to the research of parametrized models and methods for detection and prediction of significant meteorological phenomena, especially fog and low cloud cover. The project is expected to cover methods for integration of distributed meteorological data necessary for running the prediction models, training models and then mining the data in order to be able to efficiently and quickly predict even randomly occurring phenomena. We present the methods and technologies we will use for integration of the input data, distributed on different vendors’ servers. The meteorological detection and prediction methods are based on statistical and climatological methods combined with knowledge discovery — data mining of meteorological data (SYNOP, METAR messages, weather radar imagery, “raw” meteorological data from stations, satellite imagery and results of common meteorological prediction models).

Problem‐solving environment for flood forecasting

This paper presents a prototype of the collaborative problem solving environment (PSE) for flood forecasting. Flood forecasting is a complex problem that requires cooperation of many scientists in different areas. To enable this cooperation in a manner comfortable to hydrometeorological experts, a part of the CrossGrid project is aimed towards developing a PSE, whose prototype is described here. The PSE consists of a cascade of simulation models, a storage system for computed and measured data and other used datasets, a Web‐based portal with collaboration tools and a powerful computation facility. The whole system is tied together by grid technology and is used to support a virtual organization of experts, developers and users.

Flood Forecasting in CrossGrid Project

This paper presents a prototype of flood forecasting system based on Grid technologies. The system consists of workflow system for executing simulation cascade of meteorological, hydrological and hydraulic models, data management system for storing and accessing different computed and measured data, and web portals as user interfaces. The whole system is tied together by Grid technology and is used to support a virtual organization of experts, developers and users.

Hydro-meteorological Scenarios Using Advanced Data Mining and Integration

We present a hydro-meteorological scenario and its adaptation to a novel, advanced data integration and data mining platform developed within the project ADMIRE. The scenario uses data mining techniques instead of more common physical models in order to predict phenomena which are not being ordinarily solved in Slovakia – water temperature and discharge wave propagation downstream of a major water reservoir. The scenario is one of a set of use cases, which form the Flood Forecasting Simulation Cascade – a pilot application of ADMIRE. We describe the data integration methodology approach we have devised, and the variables used in data mining training of the scenario.

Semantically-Aided Data-Aware Service Workflow Composition

Composition of workflows of computational tasks, grid jobs, or even web services is not a new topic. Many papers and research projects have tackled this problem in the past, in recent years also using semantic description of resources. Most of the proposed or developed solutions deal only with the composition of the functional part of the workflow, the data necessary to actually execute it is considered secondary. The system, design of which we describe in this paper, tries to overcome this gap, and create workflows with the input data of the workflow actions already included. Our design is based on an existing solution from the project K-Wf Grid. The workflow is modeled as a Petri net, which allows for easy incorporation of data into the process. It utilizes semantic description of resources by means of ontologies, and operates on a SOA-based grid composed of web services. It is being developed in the context of a project called SEMCO-WS.

Application of ADMIRE data mining and integration technologies in environmental scenarios

In this paper we present our work on the engine for integration of environmental data. We present a suite of selected environmental scenarios, which are integrated into a novel data mining and integration environment, being developed in the project ADMIRE . The scenarios have been chosen for their suitability for data mining by environmental experts. They deal with meteorological and hydrological problems, and apply the chosen solutions to pilot areas within Slovakia. The main challenge is that the environmental data required by scenarios are maintained and provided by different organizations and are often in different formats. We present our approach to the specification and execution of data integration tasks, which deals with the distributed nature and heterogeneity of required data resources.

Knowledge-based Platform for Environmental Risk Management

We present a system for semantically-aided workflow management for SOA-based grid environment. The user can construct workflows of grid and web services just by stating the intended result, can see the workflow and its dynamic behavior in a graphical form, and can annotate the whole workflow, its parts, or its results. User experience, constructed workflows, and computed results can be shared between users. The system uses monitoring information to adapt to changes in the underlying infrastructure by extracting ontologically managed information about existing application services. One of its important features is also a transparency of the grid, and resulting ease of use even for application area experts not familiar with grid computing concepts. We also present the way this system is employed to predict flood hazards, using a series of simulation models for meteorology, hydrology, and hydraulics. The system is the result of the IST project K-Wf Grid.

Prediction of significant meteorological phenomena using advanced data mining and integration methods

We present the design, goals, and current state of the project Data Mining Meteo1which is aimed towards using new data integration and data mining techniques in prediction of several meteorological phenomena, whose prediction is so far difficult, yet very useful for planning and coordinated, for example in traffic management. The paper focuses on the description of the meteorological prediction scenarios, and how the data mining and integration technologies are used to make them feasible in a day-to-day production environment.

Mining environmental data in hydrological scenarios

We present data mining methods which are used in a hydro-meteorological scenarios within the FP7 project ADMIRE1. The scenarios uses data mining techniques instead of more common physical models in order to predict phenomena which are not being ordinarily solved in Slovakia - water temperature, discharge wave propagation downstream of a major water reservoir and short-term rainfall prediction by analyzing radar imagery. These scenarios are one of a set of use cases, which form the Flood Forecasting Simulation Cascade - a pilot application of ADMIRE project. We describe the variables used in data mining training of these scenarios and also an introduction to the data integration methodology approach we have devised.

Grid Based Flood Prediction Virtual Organization

The topic of natural disaster management -- prediction, prevention, or minimization of their impact is an important topic for scientific research. The advances in computer simulation and highperformance computing in recent years have highly extended the possibilities in this field, and have changed the ways in which natural disaster management systems operate. This paper describes evolution of one such system -- a flood prediction application. The application consists of a set of simulation models, visualization tools, and various support components. During past six years it has evolved from a simple hydraulic modeling scenario into a sophisticated cascade of simulations, using state-of-the art grid, workflow and knowledge management technologies, and is one of the first applications of the SOKU [1]concept in the field of computer simulations.