Virtual reality (VR) is on the edge of getting a mainstream platform for gaming, education and product design. The feeling of being present in the virtual world is influenced by many factors and even more intriguing a single negative influence can destroy the illusion that was created with a lot of effort by other measures. Therefore, it is crucial to have a balance between the influencing factors, know the importance of the factors and have a good estimation of how much effort it takes to bring each factor to a certain level of fidelity. This paper collects influencing factors discussed in literature, analyses the immersion of current off-the-shelf VR-solutions and presents results from an empirical study on efforts and benefits from certain aspects influencing presence in VR experiences. It turns out, that sometimes delivering high fidelity is easier to achieve than medium fidelity and for other aspects it is worthwhile investing more effort to achieve higher fidelity to improve presence a lot.

While many speech synthesis systems based on deep neural networks are thoroughly evaluated and released for free use in English, models for languages with far less active speakers like German are scarcely trained and most often not published for common use. This work covers specific challenges in training text to speech models for the German language, including dataset selection and data preprocessing, and presents the training process for multiple models of an end-to-end text to speech system based on a combination of Tacotron 2 and Multi- Band MelGAN. All model compositions were evaluated against the mean opinion score, which revealed comparable results to models in literature that are trained and evaluated on English datasets. In addition, empirical analyses identified distinct aspects influencing the quality of such systems, based on subjective user experience. All trained models are released for public use.

Software integration, especially in Open Source Software (OSS), suffers from technical debt as software evolves over time. Evolvement often ends in incompatibility issues like changed or removed APIs. The problems increase, as more components need to be integrated. This article analyzes common problems in OSS integration with respect to technical debt esp. externally induced technical debt and collects empirical evidence from the case study "AMiProSI" which combines different functions of various OSS systems to create a unified intranet solution. As part of the analysis, this paper discusses component-based development and its related dependencies and explains related technical debt (TD). By bringing down the individual problems in sub-categories of technical debt, we show what kind of TD has arisen in our case study AMiProSI and give advice on how this could be prevented in other projects.

The increasing availability of audio data on the internet lead to a multitude of datasets for development and training of text to speech applications, based on neural networks. Highly differing quality of voice, low sampling rates, lack of text normalization and disadvantageous alignment of audio samples to corresponding transcript sentences still limit the performance of deep neural networks trained on this task. Additionally, data resources in languages like German are still very limited. We introduce the "HUI-Audio-Corpus-German", a large, open-source dataset for TTS engines, created with a processing pipeline, which produces high quality audio to transcription alignments and decreases manual effort needed for creation.

Deep learning has revived the field of automatic speech recognition (ASR) in the last ten years and pushed recognition rates into regions on par with humans. Applications like Siri, Amazon Alexa and Google Assistant are very popular, but have inherent privacy problems. In this paper, we evaluate state of the art open source ASR models regarding their usability in a smart speaker without cloud, both in terms of accuracy and runtime performance on cost-effective low power edge devices. We found Kaldi to be the most accurate solution and also among the fastest ones. It runs more than fast enough on an Nvidia Jetson Nano. It is still not on par with commercial cloud services, but getting close to it.

Deep learning is moving more and more from the cloud towards the edge. Therefore, embedded devices are needed that are reasonably cheap, energy-efficient and fast enough. In this paper we evaluate the performance and energy consumption of popular, off-the-shelf commercial devices for deep learning inferencing. We compare the Intel Neural Compute Stick 2, the Google Coral Edge TPU and the Nvidia Jetson Nano with the Raspberry Pi 4 for their suitability as a central controller in an autonomous vehicle for the formula student driverless.

Sprachsteuerung wurde mit Siri populär und ist dank Alexa dabei unseren Alltag weiter zu vereinfachen. Sprachassistenten und Chatbots können aber auch in Unternehmen selbst, oder in der Kommunikation mit deren Kunden gewinnbringend eingesetzt werden. Dank künstlicher Intelligenz wird die Qualität solcher Lösungen immer besser. Man sollte jedoch nicht den Versuchungen einer schnellen Cloud-Lösung erliegen, bei der der Datenschutz auf der Strecke bleibt.

Manufacturing execution systems (MES) are the central integration point for the shop floor. They collect information about customer orders from ERP systems (enterprise resource planning), calculate the best plans for production orders and their assignment to machines and monitor the execution of these plans. It is therefore also the central data hub on the shop floor and communicates progress back to the ERP system. However, each company and production facility is a bit different and it is a hard decision to find a good compromise between standard products with a long customization period, industry-specific solutions with less customization need and company-specific solutions with long development times. This paper proposes the use of business process management (BPM) as a means for easy graphical customization of production processes that lead to immediately executable workflows (zero code development) or need very few code additions to get executable (low code development).

Blockchain ist derzeit ein Hype. Bekanntlich folgt nach dem Gipfel der überzogenen Erwartungen der Absturz in das Tal der Enttäuschungen. Das liegt unter anderem daran, dass bisher nur wenige der sinnvollen Anwendungsfälle auch tatsächlich praktisch umgesetzt werden konnten und viele existierende Szenarien auch mit herkömmlichen Technologien ohne Blockchain funktionieren würden. Und dennoch kann Blockchain sinnvoll eingesetzt werden, beispielsweise auf dem Katasteramt oder in einer elektronischen Patientenakte.

IT support for collaboration has gone through quite some change since the beginning of the century and is providing more and more support for knowledge workers. Although single systems are getting easier to use, they are often not replacing former systems but accompany them, which makes the overall system landscape harder to oversee for knowledge workers. Future information systems should therefore combine the existing building blocks under a consistent user interface and assist the user in storing information at the right place. Seamlessly switching between formats, so that the user doesn’t have to decide upfront whether a blog entry, a wiki page or a text processor document is better suited for the information. This section discusses the development of digital collaboration solutions and shows how social software has changed them to better support knowledge processes.

Im folgenden Kapitel geht es um das Privatleben in der digitalen Welt. Wie wird es sich verändern? Welche Produkte werden wir nutzen? Was können sie heute schon, welche Funktionen und Fähigkeiten werden sie in naher Zukunft haben? Wird unser Leben dadurch angenehmer oder sollten wir uns vor so einer digitalen Zukunft fürchten? Anhand vieler Anwendungsbeispiele werden Sie verstehen, was das aktuelle Schlagwort Digitalisierung für das Privatleben bedeutet, und anhand zweier ausgewählter Technologien wird Ihnen ein kleiner Einblick hinter die Kulissen der technischen Entwicklung eröffnet.

Purpose – Technologies such as smart advisors, smart factories and the “Internet of things” have again sparked large interest in IT-enabled knowledge management. Although there have been numerous case studies and empirical studies on how the management of knowledge in large organizations has benefitted from these technologies, we have only scarce information on how small and medium-sized enterprises (SMEs) can manage the corresponding digital transformation necessary. In line with the emerging paradigm of physical-cyber-social computing (Sheth, Anantharam & Henson, 2013), we intend to foster the digital transformation that might boost SMEs’ resilience concerning the increasing demands of hypercompetitive environments. The research question discussed in this paper is how enterprise knowledge infrastructure services that are available publicly can support SMEs in building knowledge capabilities that strengthen their organizational resilience. Design/methodology/approach – The methodology used for this discussion consists of (1) a review of related work on the knowledge-based view, resilience and enterprise knowledge infrastructures plus (2) case examples taken from exploratory interviews with representatives of six German SMEs. As we study the relationships between IT, activities and outcomes, we build our research on the input-process-output model (McGrath, 1964) or, more specifically, an integrative framework for studying knowledge management enablers, processes, and organizational performance (Lee & Choi, 2003). Originality/value –Our contributions are two-fold: (1) we propose to employ capabilities in order to detail the concept of organizational resilience and (2) we present a model to illustrate how services of an enterprise knowledge infrastructure can support organizational resilience with the help of knowledge activities. Practical implications – The application of our approach to decompose organizational resilience into capabilities and to weave enterprise knowledge infrastructure services together with cloud service models to support such capabilities provides the basis for SMEs to benefit from comprehensive cloud-based offerings to build organizational resilience. By using such cloud offerings, SMEs can avoid issues with the development of own services. Cloud providers can use our decomposition of organizational resilience to design cloud-based IT services in a way that these services help to build capabilities for such resilience.

One could think that knowledge management (KM) is already fully digitized and therefore the discussion about digital transformation is already too late here. Documents are mostly created digitally for quite some years. Communication is happening mostly in digital form and also cooperation is often social media that raises awareness for things colleagues are doing. However, the mega trend digitaliza-tion stands for more than just electronic forms of data creation, management and storage.

The capability to operate cloud-native applications can generate enormous business growth and value. But enterprise architects should be aware that cloud-native applications are vulnerable to vendor lock-in. We investigated cloud-native application design principles, public cloud service providers, and industrial cloud standards. All results indicate that most cloud service categories seem to foster vendor lock-in situations which might be especially problematic for enterprise architectures. This might sound disillusioning at first. However, we present a reference model for cloud-native applications that relies only on a small subset of well standardized IaaS services. The reference model can be used for codifying cloud technologies. It can guide technology identification, classification, adoption, research and development processes for cloud-native application and for vendor lock-in aware enterprise architecture engineering methodologies.

The Semantic Web has matured from a vision and research area of a few AI specialists to an important technology being used in a large number of research and a few practice projects. Most building blocks of the Semantic Web stack are filled with concrete technologies and W3C standards, but there are still enough areas for research. However, even with existing technologies, the potential of semantic applications within corporations is not yet fully harnessed as the adoption of Semantic Web technologies lacks behind other technologies like NoSQL databases or Web 2.0 technologies. This paper reviews the state of the art of Semantic Web technologies, discusses important terms and developments as well as currently active research streams. It further analyses available tools and applications with a focus on corporate scenarios and open source software and concludes with the suggestion of an architecture for a corporate semantic intranet.

Docker provides a good basis to run composite applications in the cloud, especially if those are not cloud-aware, or cloud-native. However, Docker concentrates on managing containers on one host, but SaaS providers need a container management solution for multiple hosts. Therefore, a number of tools emerged that claim to solve the problem. This paper classifies the solutions, maps them to requirements from a case study and identifies gaps and integration requirements. We close some of these gaps with our own integration components and tool enhancements, resulting in the currently most complete management suite.

Der Hype um das Container-Werkzeug Docker ist auch drei Jahre nach seiner ersten Veröffentlichung ungebrochen. Docker
adressiert sowohl Entwickler, die einen einfachen Weg suchen, um ihre Anwendungen zu testen und anschließend in die
Produktivumgebung zu bringen, als auch Cloud-Betreiber, die durch die Verwendung von Containern statt Virtuellen Maschinen
(VMs) eine höhere Packungsdichte und Performance realisieren können. Während die Handhabung für Entwickler mit einzelnen
Docker-Containern recht einfach ist, setzen die „Googles“ und „Netflixe“ dieser Welt häufig selbstentwickelte Werkzeugketten
ein, um die Komplexität in großen Umgebungen durch Automatisierung beherrschbar zu machen. Dazwischen stehen mittel-
ständische Cloud-Anbieter oder Rechenzentren größerer Unternehmen, die sich keine Entwicklerschar für Docker-Tools leisten
können oder wollen, andererseits aber die Komplexität manuell nicht mehr handhaben können. Für diese gibt es eine schier
unüberschaubare Anzahl an Open-Source-Werkzeugen, die das eine oder andere Problem mildern, jedoch kaum eine um-
fassende Lösung aus einem Guss. Der vorliegende Artikel zeigt, welche Aufgaben zu erfüllen sind, welche Werkzeuge welche
Aspekte davon abdecken und welche Kombinationen von Werkzeugen miteinander harmonieren, um eine umfassende Lösung
zu erreichen.

Bei auf die Infrastruktur bezogenen Szenarien hat Open-Source-Software in den vergangenen Jahren ihre Tauglichkeit für den Firmeneinsatz bereits hinreichend unter Beweis gestellt.

Entgegen der landläufigen Meinung bietet freie Software auch im Bereich klassischer Enterprise-Applikationen durchaus ernstzunehmende Alternativen zu den proprietären Produkten.

Anwender sind bei Problemen aber nicht ausschließlich auf die Gnade der Community angewiesen; ein mittlerweile existierendes Dienstleistungsökosystem kann Service-Ansprüche auch von Unternehmen zufriedenstellen.

NoSQL and especially graph databases are constantly gaining popularity among developers as they promise to deliver superior performance when handling highly interconnected data compared to relational databases. Apache Shindig is the reference implementation for OpenSocial with a highly interconnected data model. However, it had a relational database as back-end. In this paper we describe our experiences with the graph database Neo4j as back-end and compare Cypher, Gremlin and Java as alternatives for querying data with MySQL. We consider performance as well as usability from a developer's perspective. Our results show that Cypher is a good query language in terms of code readability and has a moderate overhead for most queries (20–200%). However, it has to be supplemented with “stored procedures” to make up for some performance deficits in pattern matching queries (>1000%). The RESTful API is unusable slow, whereas our WebSocket connection performs significantly better (>650%).