Spatial hypertext represents associations between chunks of information by spatial or visual attributes (such as proximity, color, shape, etc.). This supports expressing information structures implicitly and in an intuitive way. However, automatic recognition of such informal, implicitly encoded structures by a machine (a so-called spatial parser) is still a challenge. Conventional parsers are conceptually restricted by their underlying source of information. Due to this limitation there are various possible structures that cannot be recognized properly, as the machine has no means to detect them. This inevitably limits both the quality of parser output and hence parser performance. In this paper we show that considering temporal aspects in spatial parser design will lead to significant increase in parsing accuracy, detection of richer structures and thus higher parser performance. We call machines that consider such spatial and temporal information spatio-temporal parsers.

For the purpose of providing evidence, parsers for recognizing spatial, visual, and temporal object relations have been implemented and tested in a series of user surveys. One aim was to find out how "close" the machine interpretetation of structures get to human interpretation. It turned out that in none of the test cases pure spatial or visual parser could outperform the spatio-temporal parser. Instead, the spatio-temporal parser was able to compensate limitations of conventional parsers. Furthermore, we have statistically tested parsing accuracy. The results indicate a non-trivial effect that is recognizable by humans. This shows that spatio-temporal parsers produce output that is significantly closer to what knowledge workers intend to express compared to traditional spatial parsers.

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.

This paper proposes to make complex production machines more user-friendly. Improved machines help the operator in case of an error message or a process event by displaying recommendations, such as “in the last 10 occurences of this event the operators performed the following keystrokes”. The messages are generated from statistical data on former user- interaction and previous process-events. The data represents the knowledge of all the machine operators. The data is gathered by logging user-interaction and process-events during regular operation of the production machine. This approach allows to store the operators’ expert knowledge in the production machine without human intervention.

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%).

Die digitale Transformation bringt ein viel diskutiertes Thema zurück auf die Agenda: IT-Compliance. Wie die Öffentliche Verwaltung davon betroffen ist, untersuchen das Institut für Informationssysteme (iisys) der Hochschule Hof und die Blue Eye Solutions GmbH. Erste Ergebnisse des laufenden Forschungsprojekts liegen nun vor.

In the present mobile climate, both companies as well as their consumers are able to execute various processes at any time in any location using a myriad of wireless technologies. Organizational trends demonstrate that mobile devices are being utilized exponentially more to solve problems from the strategic planning level to the operational level through various departments. This propensity was recently confirmed for Germany in a neoteric IDC study [1]. Utilizing that analysis, this article will evaluate and inspect the scope of such modeling. We will endeavor to define the term "mobile modeling", and our final desideratum will be to present results from an empirical investigation of its requirements and benefits. Finally, we will discuss the future technical implications and conclusions of the model.