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Nowadays, none can expect that knowledge about some part of reality will not change. Consequently, a representation of such evolving knowledge (for example, ontologies) also changes. Such changes entail that applications incorporating such knowledge may become compromised and yield wrong results. An example of such an application is ontology alignment which can be informally described as a set of connections between two ontologies. Those connections mark elements from two ontologies that relate to the same parts of reality. In changing one of the corresponding ontologies, such connections may become invalid. One may designate the ontology alignment once again from scratch for altered ontologies. However, such an approach is time and resource-consuming. The paper comprehensively presents our ontology evolution and alignment maintenance framework. It can be used to preserve the validity of ontology alignment using only the analysis of changes introduced to maintained ontologies. The precise definition of ontologies is provided, along with a definition of the ontology change log. A set of algorithms that allow revalidating ontology alignments have been built based on such elements.

The retailers are interested in different profit maximization approaches, which help them to perform daily shelf arrangement tasks, improve customers’ satisfaction and avoid out of stocks on the shelves. Most of the retail literature proposes basic models which don’t reflect complicated merchandising rules. The aim of the paper is to develop a shelf space allocation model which investigates vertical shelf levels, horizontal shelf division into segments, and product item allocation rules such as cappings and nestings. A genetic algorithm has been developed to implement the model. The efficiency was estimated with the help of CPLEX solver. The computational experiments show that the proposed approach allows getting the results of sufficient quality for different problem sizes in a short running time without requiring large computing resources. GA and CPLEX found statistically the same profitable solution within the same computational time. However, GA found a better solution in situations where the commercial solver can’t perform calculations during the increased computational time and stopped after a couple of minutes. This proves the reason for GA implementation for shelf space allocation problems.

A federated data warehouse is a tool that provides an end-user a unified perspective on a finite set of independent data warehouses. This requires creating a global schema from partial schemas, which remains purely virtual. This is a result of iterative integration of participating data warehouses. It is then used to simulate that the aforementioned set of participating warehouses as an effectively one, “super” data warehouse exposed to the end-user. In this paper, authors present a framework, that can be used to evaluate the profitability of adding a new data warehouse to the existing federation, in terms of increased data richness and its expressiveness. Solid formal foundations are provided, along with heuristic algorithms , an experimental verification (which involved two different experimental procedures) and a statistical analysis of obtained results.

The production of food in an ecologically and economically sustainable manner is of significant importance today. Agricultural producers are increasingly being accompanied by elements of Agriculture 4.0 such as automation and decision-making support. This work shows an example of how the digitization of viticulture can be significantly supported by Deep Learning. The work presents an approach that can overcome the loss of human expertise in grape identification by using image-recognition-techniques and residual network architectures. Our developed model for grape identification at a vineyard reaches an accuracy of 99% of correctly recognized grape varieties.

Ontology alignment, although widely researched and discussed, has still some issues that need addressing. One of those is designating alignments of relations defined in two independent ontologies. According to the found literature, this topic is frequently omitted by many state-of-the-art ontology alignment solutions, that focus mainly on aligning concepts. In this paper, we propose a novel approach to designating mappings of ontology relations, which is based on fuzzy logic. It is used to combine several different similarity measures calculated between elements that are used to define relations. The approach has been experimentally verified using the widely accepted datasets provided by Ontology Alignment Evaluation Initiative, yielding promising results.