This paper presents a novel approach for developing sustainable building materials through Sequential Learning. Data sets with a total of 1367 formulations of different types of alkali-activated building materials, including fly ash and blast furnace slag-based concrete and their respective compressive strength and CO2-footprint, were compiled from the literature to develop and evaluate this approach. Utilizing this data, a comprehensive computational study was undertaken to evaluate the efficacy of the proposed material design methodologies, simulating laboratory conditions reflective of real-world scenarios. The results indicate a significant reduction in development time and lower research costs enabled through predictions with machine learning. This work challenges common practices in data-driven materials development for building materials. Our results show, training data required for data-driven design may be much less than commonly suggested. Further, it is more important to establish a practical design framework than to choose more accurate models. This approach can be immediately implemented into practical applications and can be translated into significant advances in sustainable building materials development.

Although increases in available data have inspired companies’ interest in creating and extracting value from it, many lack the insight and guidance to assess the potential data offer. To address this issue, we conduct a systematic literature review to create a universe of 102 real-world cases from diverse industries with regard to the use of data. Based on an analysis of these cases, this paper provides a set of 12 generic strategies for monetizing data, ranging from sole asset sale to strategically opening data and guaranteeing control. This study supports business practice by aggregating the wide range of established approaches of data monetization from practice for operational purposes. It advances theoretical understanding of value capturing from data and suggests important avenues for future work in this emerging field of research.

Decision-making in the context of organizational information security is highly dependent on various information. For information security managers, not only relevant information has to be clarified but also their interdependencies have to be taken into account. Thus, the purpose of this research is to develop a comprehensive model of relevant management success factors (MSF) for organizational information security. First, a literature survey with an open-axial-selective analysis of 136 articles was performed to identify factors influencing information security. These factors were categorized into 12 areas: physical security, vulnerability, infrastructure, awareness, access control, risk, resources, organizational factors, CIA, continuity, security management, compliance & policy. Second, an interview series with 19 experts from the industry was used to evaluate the relevance of these factors in practice and explore interdependencies between them. Third, a comprehensive model was developed. The model shows that there are key-security-indicators, which directly impact the security-status of an organization while other indicators are only indirectly connected. Based on these results, information security managers should be aware of direct and indirect MSFs to make appropriate decisions.

Due to their tremendous potential in predictive tasks, Machine Learning techniques such as Artificial Neural Networks have received great attention from both research and practice. However, often these models do not provide explainable outcomes which is a crucial requirement in many high stakes domains such as health care or transport. Regarding explainability, Semantic Web Technologies offer semantically interpretable tools which allow reasoning on knowledge bases. Hence, the question arises how Semantic Web Technologies and related concepts can facilitate explanations in Machine Learning systems. To address this topic, we present current approaches of combining Machine Learning with Semantic Web Technologies in the context of model explainability based on a systematic literature review. In doing so, we also highlight domains and applications driving the research field and discuss the ways in which explanations are given to the user. Drawing upon these insights, we suggest directions for further research on combining Semantic Web Technologies with Machine Learning.

Architekturbegriffe spielen in der Wirtschaftsinformatik eine immer prominentere Rolle. Ein Beleg dafür ist der Begriff der Referenzarchitektur, welcher in einer stetig ansteigen Anzahl an Publikationen verwendet wird. Besonders im Kontext des Begriffs „Industrie 4.0“ und unternehmensübergreifenden Digitalisierungsbestrebungen wird der Begriff der Referenzarchitektur verstärkt verwendet, um auf eine gewisse Art der Standardisierung hinzuwirken und die Komplexität bei der Entwicklung von Systemen zu reduzieren. Jedoch ist die Verwendung des Begriffs und das Begriffsverständnis sehr heterogen, sodass der Vergleich, die Bewertung, eine Ableitung oder das Erstellen von Referenzarchitekturen sehr erschwert wird. Um solchen Herausforderung zu begegnen wird in diesem Beitrag das Begriffsverständnis von Referenzarchitekturen systematisiert, mit dem Ziel, eine einheitliche Grundlage für dessen Beschreibung und zum Vergleich von Referenzarchitekturen bereitzustellen. Hierzu werden geläufige Definitionen in Wissenschaft und Praxis dargelegt, diskutiert und an Hand der verfolgten Ziele sowie den Ausprägungen und Eigenschaften von Referenzarchitekturen systematisiert. Diese Systematik bildet dabei die Basis um auch zukünftige Begriffe und Eigenschaften im Kontext von Referenzarchitekturen einzuordnen bzw. zu klassifizieren.

A domain-specific ontology for IT benchmarking has been developed to bridge the gap between a systematic characterization of IT services and their data-based valuation. Since information is generally collected during a benchmark exercise using questionnaires on a broad range of topics, such as employee costs, software licensing costs, and quantities of hardware, it is commonly stored as natural language text; thus, this information is stored in an intrinsically unstructured form. Although these data form the basis for identifying potentials for IT cost reductions, neither a uniform description of any measured parameters nor the relationship between such parameters exists. Hence, this work proposes an ontology for the domain of IT benchmarking, available at https://w3id.org/bmontology. The design of this ontology is based on requirements mainly elicited from a domain analysis, which considers analyzing documents and interviews with representatives from Small- and Medium-Sized Enterprises and Information and Communications Technology companies over the last eight years. The development of the ontology and its main concepts is described in detail (i.e., the conceptualization of benchmarking events, questionnaires, IT services, indicators and their values) together with its alignment with the DOLCE-UltraLite foundational ontology.

In the domain of IT benchmarking (ITBM), a variety of data and information are collected. Although these data serve as the basis for business analyses, no unified semantic representation of such data yet exists. Consequently, data analysis across different distributed data sets and different benchmarks is almost impossible. This paper presents a system architecture and prototypical implementation for an integrated data management of distributed databases based on a domain-specific ontology. To preserve the semantic meaning of the data, the ITBM ontology is linked to data sources and functions as the central concept for database access. Thus, additional databases can be integrated by linking them to this domain-specific ontology and are directly available for further business analyses. Moreover, the web-based system supports the process of mapping ontology concepts to external databases by introducing a semi-automatic mapping recommender and by visualizing possible mapping candidates. The system also provides a natural language interface to easily query linked databases. The expected result of this ontology-based approach of knowledge representation and data access is an increase in knowledge and data sharing in this domain, which will enhance existing business analysis methods.

In the domain of IT benchmarking collected data are often stored in natural language text and therefore intrinsically unstructured. To ease data analysis and data evaluations across different types of IT benchmarking approaches a semantic representation of this information is crucial. Thus, the identification of conceptual (semantical) similarities is the first step in the development of an integrative data management in the domain of IT benchmarking. As an ontology is a specification of such a conceptualization an association of terms, relations between terms and related instances must be developed. Building on previous research we present an approach for an automated term extraction by the use of natural language processing (NLP) techniques. Terms are automatically extracted out of existing IT benchmarking documents leading to a domain specific dictionary. These extracted terms are representative for each document and describe the purpose and content of each file and server as a basis for the ontology development process in the domain of IT benchmarking.