Now showing 1 - 4 of 4
  • 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.
    Scopus© Citations 1
  • 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
    Domain specific language for securities settlement systems
    (2012-08-06)
    Ojars Krasts
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    ;
    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
    Scopus© Citations 2
  • 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.