Informatica

Nowadays, there is a lack of smart marine monitoring systems, which have possibilities to integrate multi-dimensional components for monitoring and predicting marine water quality and making decisions for their optimal operations with minimal human intervention. This research aims to extend the smart coastal marine monitoring by proposing a solar energy planning and control component. The proposed approach involves the adaptive neuro-fuzzy inference system (ANFIS) for the wireless buoys, working online during the whole year in the Baltic Sea near the Lithuanian coast. The usage of our proposed fuzzy solar energy planning and control components allows us to prolong the lifespan of batteries in buoys, so it has a positive impact on sustainable development. The novelty and advantage of the proposed approach lie in establishing the ANFIS-based model to predict and control solar energy in a buoy for different lighting and temperature conditions depending on the four year seasons and to make a decision to transfer the collected data. The energy planning and consumption system for the wireless sensor network of buoys is carefully evaluated, and its prototype is developed. The proposed approach can be practically used for environmental monitoring, providing stakeholders with relevant and timely information for sound decision-making about hydro-meteorological situations in coastal marine water.

The data-driven approach is popular to automate learning of fuzzy rules and tuning membership function parameters in fuzzy inference systems (FIS) development. However, researchers highlight different challenges and issues of this FIS development because of its complexity. This paper evaluates the current state of the art of FIS development complexity issues in Computer Science, Software Engineering and Information Systems, specifically: 1) What complexity issues exist in the context of developing FIS? 2) Is it possible to systematize existing solutions of identified complexity issues? We have conducted a hybrid systematic literature review combined with a systematic mapping study that includes keyword map to address these questions. This review has identified the main FIS development complexity issues that practitioners should consider when developing FIS. The paper also proposes a framework of complexity issues and their possible solutions in FIS development.

Nowadays, ontologies play a central role in many computer science problems such as data modelling, data exchange, integration of heterogeneous data and models or software reuse. Yet, if many methods of ontology based conceptual data modelling have been proposed, only few attempts have been made to ontology axioms based modelling of business rules, which make an integral part of each conceptual data model. In this paper, we present the approach how ontology axioms can be used for business rules implementation. Our proposal we apply for the transformation of PAL (Protege Axiom Language) constraints (ontology axioms), which is based on KIF (Knowledge Interchange Format) and is part of KIF ontology, into OCL (Object Constraint Language) constraints, which are part of a UML class diagram. Z language is used to formalise the proposal and describe the transformation. The Axiom2OCL plug-in is created for automation of the transformation and a case study is carried out.