SemDaServ: A Systematic Approach for Semantic Data Specification of AI-Based Smart Service Systems

To develop smart services to successfully operate as a component of smart service systems (SSS), they need qualitatively and quantitatively sufficient data. This is especially true when using statistical methods from the field of artificial intelligence (AI): training data quality directly determines the quality of resulting AI models. However, AI model quality is only known when AI training can take place. Additionally, the creation of not yet available data sources (e.g., sensors) takes time. Therefore, systematic specification is needed alongside smart service systemsSSS development. Today, there is a lack of systematic support for specifying data relevant to smart services. This gap can be closed by realizing the systematic approach SemDaServ presented in this article. The research approach is based on Blessing’s Design Research Methodology (literature study, derivation of key factors, success criteria, solution functions, solution development, applicability evaluation). SemDaServ provides a three-step process and five accompanying artifacts. Using domain knowledge for data specification is critical and creates additional challenges. Therefore, the SemDaServ approach systematically captures and semantically formalizes domain knowledge in SysML-based models for information and data. The applicability evaluation in expert interviews and expert workshops has confirmed the suitability of SemDaServ for data specification in the context of SSS development. SemDaServ thus offers a systematic approach to specify the data requirements of smart services early on to aid development to continuous integration and continuous delivery scenarios.

Atmospheric air quality monitoring over the territory of Ukraine with specification over the cities using Sentinel-5P satellite data

The study describes methods for operative monitoring of atmospheric air quality over the territory of Ukraine using the Sentinel-5P satellite data. The methods provide possibility for data specification over the cities. The data processing is fully automatic and deals with the column data of nitrogen dioxide (NO2), carbon monoxide (CO), formaldehyde (HCHO), sulfur dioxide (SO2) and total ozone (O3). The system works every day and starts processing approximately 3 hours after the scanning of Ukrainian territory. The paper describes the procedure of files creation which represents the third level of data archiving. There are implemented the procedures of the adjusting to regular grids and the filtering of statistically unreliable data. The methods for data specification are developed which allow to analyze the content of chemical compounds over the cities. The paper discusses the main features for the interpretation of chemicals’ spatio-temporal distribution. It is emphasized the typical reasons for false interpretation and mistaken conclusions about atmospheric air quality while analyzing the satellite observations.

A method of semi-automated ontology population from multiple semi-structured data sources

Organisations use data in different formats: Word documents, Excel spreadsheets, databases, HTML pages and so on. It is not easy to make decisions with such data due to the lack of integration between the different sources and built-in decision-making rules. Decisions can be reached with knowledge bases, which, unlike databases, make it possible to store not only objects, facts and attributes but also more sophisticated patterns such as rules and axioms. The article proposes an ontology-based method for knowledge base creation that allows for the simultaneous integration of semi-structured data sources and extendibility while remaining context independent. At the initial steps of the method, data specification should be performed with the Data Sources Ontology developed by the authors. This ontology provides data structure description that forms supportive knowledge graph. The graph’s schema should be mapped with the domain ontology to be populated. Finally, the data are inserted into the domain ontology according to the mapping rules. Manual input is needed during data specification and data-to-ontology schema mapping.

The PHA4GE SARS-CoV-2 Contextual Data Specification for Open Genomic Epidemiology

The Public Health Alliance for Genomic Epidemiology (PHA4GE) (https://pha4ge.org) is a global coalition that is actively working to establish consensus standards, document and share best practices, improve the availability of critical bioinformatic tools and resources, and advocate for greater openness, interoperability, accessibility and reproducibility in public health microbial bioinformatics. In the face of the current pandemic, PHA4GE has identified a clear and present need for a fit-for-purpose, open source SARS-CoV-2 contextual data standard. As such, we have developed an extension to the INSDC pathogen package, providing a SARS-CoV-2 contextual data specification based on harmonisable, publicly available, community standards. The specification is implementable via a collection template, as well as an array of protocols and tools to support the harmonisation and submission of sequence data and contextual information to public repositories. Well-structured, rich contextual data adds value, promotes reuse, and enables aggregation and integration of disparate data sets. Adoption of the proposed standard and practices will better enable interoperability between datasets and systems, improve the consistency and utility of generated data, and ultimately facilitate novel insights and discoveries in SARS-CoV-2 and COVID-19.

Reduction of Circular Arcs in European Cadastral Systems—The Proposal of a Solution Referring to the Recommendations of the INSPIRE Data Specification on Cadastral Parcels

Circular arcs are a graphical element present in the cadastral systems of many countries. Unfortunately, this type of record of the geometry of parcel borders is a problem described by the directives of Infrastructure for Spatial Information in Europe (INSPIRE) Data Specification on Cadastral Parcels. Because of the difficulties of using such geometric objects, the solution to this problem, as recommended by the European Commission, should be monitored. The target effect should be a cadastral data model based solely on linear segments. Solutions based on a classic approach of converting such data (like arcs), unfortunately, always involves changes of one of the most important attributes of a parcel—its area. The paper presents a proposal for solving this important problem using an algorithm, ensuring the preservation of the area of the parcels after converting the arcs into linear segments. Moreover, attention was paid to the technical aspects of the proposed changes.