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Zarembo, Imants
Preferred name
Zarembo, Imants
Official Name
Zarembo, Imants
Alternative Name
Imants Zarembo
Zarembo, I
Email
imants.zarembo@rta.lv
ORCID
Scopus Author ID
56326264300
Researcher ID
AAI-1965-2020
Research Output
2 results
Now showing 1 - 2 of 2
- PublicationAutomatic Transformation of Relational Database Schema into OWL Ontologies(2015)Ontology alignment, or ontology matching, is a technique to map different concepts between ontologies. For this purpose at least two ontologies are required. In certain scenarios, such as data integration, heterogeneous database integration and data model compatibility evaluation, a need to transform a relational database schema to an ontology can arise. To conduct a successful transformation it is necessary to identify the differences between relational database schema and ontology information representation methods, and then to define transformation rules. The most straight forward but time consuming way to carry out transformation is to do it manually. Often this is not an option due to the size of data to be transformed. For this reason there is a need for an automated solution. The automatic transformation of OWL ontology from relational database schema is presented in this paper; the data representation differences between relational database schema and OWL ontologies are described; the transformation rules are defined and the transformation tool’s prototype is developed to perform the described transformation.
Scopus© Citations 2 - PublicationANALYSIS OF ARTIFICIAL INTELLIGENCE APPLICATIONS FOR AUTOMATED TESTING OF VIDEO GAMES(2019)Game testing is a software testing process for quality control in video games. Game environments, sometimes called levels or maps, are complex and interactive systems. These environments can include level geometry, interactive entities, player and non-player controllable characters etc. Depending on the number and complexity of levels, testing them by hand may take a considerable effort. This is especially true for video games with procedurally generated levels that are automatically created using a specifically designed algorithm. A single change in a procedural generation algorithm can alter all of the video game levels, and they will have to be retested to ensure they are still completable or meet any other requirements of the game. This task may be suitable for automation, in particular using Artificial Intelligence (AI). The goal of this paper is to explore the most promising and up-to-date research on AI applications for video game testing to serve as a reference for anyone starting in the field.
Scopus© Citations 8