Now showing 1 - 10 of 28
  • Publication
    Methodology for Similarity Assessment of Relational Data Models and Semantic Ontologies
    (2017-01-17)
    Zarembo, Imants
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    ;
    Barghorn, Knut
    ;
    Merkuryev, Yuri
    ;
    Berina, Gundega
    In the upcoming age of semantic web there is a large number of relational databases being widely used. When time comes for a legacy relational database to migrate to semantic web or to be integrated with it, an important issue of determining similarity (compatibility) between two data models expressed in different ways arises. The goal of this paper is to describe the methodology for similarity assessment of relational database models and semantic data models and to present an ontology matching tool research prototype. The methodology consists of a set of steps, including transformation rules for data models, whose compatibility must be assessed, to the same ontology representation and applying ontology matching techniques. The methodology enables domain experts to perform a matching task semi-automatically between a relational data model and data model expressed as an ontology. The results of the semi-automatic matching are manually verified by the domain experts. The methodology was approbated using a use case from land administration domain. In the use case compatibility of data model provided by an international standard and a relational database had to be assessed.
  • Publication
    Object-Oriented Modelling and Simulation of Large-Scale Systems
    (2017-05-29) ;
    Merkuryev, Yuri
    Abstract The paper discusses a methodology of object-oriented modelling and simulation. This methodology was developed for large-scale systems modelling and simulation. From modelling point of view, presented methodology can be classified as Object-Oriented methodology, and from simulation point of view as discrete event system (DEYS) methodology.
  • Publication
    Machine Learning Technology Overview In Terms Of Digital Marketing And Personalization
    (2021-04-29) ;
    Nikolajeva, Anna
    The research is dedicated to artificial intelligence technology usage in digital marketing personalization. The doctoral theses will aim to create a machine learning algorithm that will increase sales by personalized marketing in electronic commerce website. Machine learning algorithms can be used to find the unobservable probability density function in density estimation problems. Learning algorithms learn on their own based on previous experience and generate their sequences of learning experiences, to acquire new skills through self-guided exploration and social interaction with humans. An entirely personalized advertising experience can be a reality in the nearby future using learning algorithms with training data and new behaviour patterns appearance using unsupervised learning algorithms. Artificial intelligence technology will create website specific adverts in all sales funnels individually.
  • Publication
    E-LEARNING: DEVELOPING TOMORROW'S EDUCATION
    (2021)
    . Rohit
    ;
    Peter Grabusts
    ;
    E-learning refers to the term to deliver education or training using digital resources. Computer-based learning, which is considered the keystone of today's E-learning concept, was born in the 80s. Earlier E-learning provides education using only text as with the development in technology it allows adding various forms, i.e., Graphical Text, Images, Video Conferencing etc. In today's time, the concept of is E-learning growing at a rapid pace. Improved bandwidth and growing technology helped in pushing the expansion of E-learning. Along with the university, large corporate companies are also resorting to E-learning. E-learning provides many advantages as compare to Instructor-led training (ILT). E-learning saves the times of travel as physical presence is not required. Education can be provided from anywhere at any time. E-learning is cost-effective also as the course, once developed, can be modified easily. There can some concern which can be faced by the trainer and leaners in future. Adopting E-learning will be a step towards saving the environment. It will be environmentally friendly as tablets will replace books; paper notes will be replaced with digital messages. Digital tools will help to reduce the burden of a student. Artificial Intelligence is a prevalent concept in computer science. A branch of AI, known as a Neural Network, is based on the human brain. The research's main aim is to review existing methods and analyse further possibilities of E-learning systems with neural networks.
  • Publication
    APPLE AND PEAR SCAB ONTOLOGY
    (2021)
    Imants Zarembo
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    ;
    Toms Bartulsons
    ;
    Olga Sokolova
    ;
    Lienīte Litavniece
    ;
    Anna Nikolajeva
    An important issue in horticulture is ensuring plant disease, such as scab, prevention and treatment. Apple and pear are among the most widely grown (approximately 43% of all fruit tree area [1]) and economically important fruit crops specified worldwide and in Latvia. Scab diseases caused by ascomycetous fungi Venturia inaequalis and V.pyrina are economically the most important diseases worldwide. Research projects have produced research data covering various aspects of plant-pathogen interactions, but there is no internal linkage analysis, as well as implementation of other types of data (such as environmental and meteorological data, etc.). Establishing such a data integration system would allow the identification of new regularities in plant-pathogen interactions, and provide mechanisms for disease control decisions. Semantic analysis is one of information technology approaches to finding relationships in data. The product of analysis is ontology. There are plant disease ontologies which provide classification of diseases and describe their reasons. However, there is no ontology which describes a specific plant and relations among its farming parameters and disease probability. Such an ontology for apple and pear scab is presented in this paper. The constructed ontology can be applied to develop guidelines or digital expert systems.
  • Publication
    CORAS for Threat and Risk Modeling in Social Networks
    (2015)
    Aleksandrs Larionovs
    ;
    ;
    Peter Grabusts
  • Publication
    Mapping of Offshore Wind Climate and Site Conditions for the Baltic Sea within Latvian Territorial Waters
    (2013-10-31) ;
    Sergejs Rupainis
    ;
    Lita Lizuma
    The paper describes the assessment and mapping of wind climate and environmental conditions of the study region extending from 56.03N 20.2E to 57.22N 21.33E. Maps of wind resources and environmental conditions are the primary method used for presenting the offshore wind resources as well as site conditions data. A GIS database was chosen to house the offshore resources data because the datasets have a significant spatial component. A visualization of the geospatial data is created using the Google Maps platform. The maps datasets consist of gridded 1) climatological information on wind speed and direction, air temperature, air pressure, wind power potential at 10m, 80m, 90m and 100m height; 2) oceanographical information on water temperature, height and direction of sea waves, speed and direction of currents, ice conditions; 3) geological data on bathymetry and sea sediments. The horizontal resolution of the database grid cells is 5 km by 5 km. All the component datasets are spatially referenced to the same spatial base, allowing rapid indexing of the different datasets to each other. A database user may compare information from different datasets in the same geographic location. The GIS database also allows portions of a dataset to be quickly updated as new information becomes available.
  • Publication
    IPTV Statistic Data Collection, Processing and Preparation for use in a Modeling System
    (2016-01-14)
    Vjaceslavs Dubovskis
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    ;
    Nikolajs Visockis
    Abstract Today most people get information from a TV. Trust level for television is very high and this kind of media can strongly influence public opinion. To research content watched on TV or public opinion, questioning and other methods are used today and respondents know about the research process. This knowledge forces people to give untruthful answers, because sometimes they don’t want to share their thoughts. This kind of research result is not satisfactory and conclusions can create misconceptions. Fast development of IPTV gives new opportunities for research using collected statistics. To make research legal, all statistical data must be anonymous. If a TV watcher doesn’t think that he has to make a choice, he will watch TV content he is interested in. At the moment there is no one united standard for collecting and processing IPTV statistical data. Each vendor handles data differently. Some solutions do not allow collecting data about watched TV channels and programs. In this paper the author will present a possible, universal method for data collection in a variety of IPTV networks, and different types of streaming and Middleware.
  • Publication
    Domain specific language for securities settlement systems
    (2012-08-06)
    Ojars Krasts
    ;
    ;
    Arnis Kleins
    Actual problems during design, implementation and maintenance of securities settlement systems software are achieving complementarity of several different, connected, asynchronously communicating settlement systems and verification of this complementarity. The aim of this paper is to create domain specific language for modeling of settlement systems and their interactions. Then use models to calculate settlement systems behavior. Specific of settlement systems requires that they perform accordingly to business rules in any situation. This makes use of model checking a very desirable step in development process of settlement systems. Defining a domain specific language and creating editor supporting it is a first step to enable use of model checking techniques. Created models also can be used as input for other analysis methods and tools, for example, basis path testing, simulation and as base for deriving test cases
  • Publication
    Assessment of the present and future offshore wind power potential: a case study in a target territory of the Baltic Sea near the Latvian coast.
    (2013)
    Lizuma, Lita
    ;
    Avotniece, Zanita
    ;
    Rupainis, Sergejs
    ;
    Offshore wind energy development promises to be a significant domestic renewable energy source in Latvia. The reliable prediction of present and future wind resources at offshore sites is crucial for planning and selecting the location for wind farms. The overall goal of this paper is the assessment of offshore wind power potential in a target territory of the Baltic Sea near the Latvian coast as well as the identification of a trend in the future wind energy potential for the study territory. The regional climate model CLM and High Resolution Limited Area Model (Hirlam) simulations were used to obtain the wind climatology data for the study area. The results indicated that offshore wind energy is promising for expanding the national electricity generation and will continue to be a stable resource for electricity generation in the region over the 21st century.