Teilāns, Artis

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Teilāns, Artis
Official Name
Teilans, Artis
Alternative Name
Teilands, Artis
Teilans, A
Artis Teilans
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Web Site
Email
artis.teilans@rta.lv
ORCID
Scopus Author ID
24723234500
Researcher ID
AAJ-1139-2020

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Now showing 1 - 3 of 3
  • Publication
    FUNCTIONAL MODELLING OF IT RISK ASSESSMENT SUPPORT SYSTEM
    (2011)
    Teilāns, Artis 
    ;
    Andrejs Romanovs
    ;
    Yuri Merkuryev
    ;
    Arnis Kleins
    ;
    Pjotrs Dorogovs
    ;
    Ojars Krasts
    Information technology systems represent the backbone of a company's operational infrastructure. A company's top management typically ensures that computer software and hardware mechanisms are adequate, functional and in adherence with regulatory guidelines and industry practices. Nowadays, due to depressed economic and increased intensity of performed operations, business highly recognizes the influence of effective Information Technology risk management on profitability. The purpose of this paper is to develop IT risks assessment systems support functional model, based on analysis of IT risks and assessment mechanisms, IT governance and risk management frameworks, functional analysis of IT risks assessment and management software, and, finally, to develop IT risk management domain specification language with a metamodel that defines an abstract UML based language for supporting model-based risk assessment. Usage of UML based domain specific language achieves synergy from in IT industry widely used UML modelling technique and the domain specific risk management extensions.
  • Publication
    Object-Oriented Modelling and Simulation of Large-Scale Systems
    (2017)
    Teilāns, Artis 
    ;
    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)
    Teilāns, Artis 
    ;
    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.