The task of organizing and retrieving knowledge is often elaborative and involves different types of media including digital or analog. In this paper we describe a system that is based on related research in the fields of spatial hypertext, information retrieval, and visualization. It utilizes a 2D space on which users can add, remove, or manipulate information entities (so-called user nodes) visually. A spatial parser recognizes the evolving structure and queries a knowledge base for helpful other information entities (so-called suggestions nodes). Similar to user nodes, those suggestions are presented as visual objects in the space. We propose a physics model to simulate their behavior. Their characteristics encode the relevance of suggestions to user nodes and to each other. This enables human recipients to interpret the given visual clues and, thus, identify information of interest. The way users organize nodes spatially influences the parsed spatial structures, i.e., the placement of suggestion nodes. This allows the creation of complex queries without any prior knowledge, yet the users do not have to be aware of that, because they can express their thoughts implicitly by manipulating their nodes. We discuss the strengths of a physics based simulation to encode context visually and point to open issues and potential solutions. On the basis of an implemented demonstrator we show the benefits compared to similar and related applications in the field of information visualization, especially when it comes to tasks where a high portion of creativity is involved and the information space is not well known.

In this paper, we describe our project DemoMedia, a software demonstrator that combines hypertext and recommender functionality in the context of video acquisition or use. DemoMedia fills the gap that exists in today's video platforms which include recommender functionalities, but only trivial support for users to structure information. Thus, users are forced to write down notes from or about videos (needed for various reasons) on additional media, such as paper. This opens a media gap between video platform and note-taking or communication to others. DemoMedia becomes a note taking and communication tool for the user, as it offers a knowledge space on which users can freely arrange and associate information. Furthermore, its intelligent parsers compute relations that are implicitly expressed and queries knowledge bases for relevant information or related videos. Those get positioned on the space in a semantically meaningful way. DemoMedia and the underlying component-based open hypermedia system Mother combine both the machine's capability of extracting knowledge from huge amounts of data and the human capability of sensemaking, intuition, and creativity.

In the context of the Internet of Things (IoT), there is the need to manage huge amounts of time series sensor data, if high frequency device monitoring and predictive analytics are targeted for improving the overall process quality in production or supervision of quality management. The key challenge here is to be able to collect, transport, store and retrieve such high frequency data from multiple sensors with minimum resource usage, as this allows to scale such systems with low costs. For evaluating the performance impact of such an IoT scenario, we produce 1000 datasets per second for five sensors. We send them to three different types of popular database management systems (i.e., MariaDB, MongoDB and InfluxDB) and measure the resource impacts of the writing and reading operations over the whole processing pipeline. These measurements are CPU usage, network usage, disk performance and usage, and memory usage results plus a comparison of the difficulty for the developers to engineer such a processing pipeline. In the end, we have a recommendation depending on the needs, which database management system is best suited for processing high frequency sensor data in an IoT context.

Künstliche Intelligenz gilt als das große „Buzzword“ unserer Tage. Insbesondere im Marketing wird der Technologie ein enormes Potenzial zugeschrieben. Dabei besteht eigentlich bis heute kein Konsens darüber, was nun genau unter KI zu verstehen ist. Eine einheitliche Definition existiert nicht. Allerdings gibt es beim Verständnis von KI so etwas wie einen „gemeinsamen Nenner“. Der Aspekt, bei dem sich alle einig darüber sind, dass es sich hierbei um etwas „künstlich Intelligentes“ handeln muss, ist der, wenn wir von der Lernfähigkeit eines Systems sprechen.

In der Digitalisierung sind Geschäftsmodelle in der Regel Daten getrieben. Der Erfolg der vielzitierten „Plattformökonomie“ beruht zu einem erheblichen Anteil auf der Sammlung und Auswertung von Informationen. Der Bedarf an Datenvielzahl und Datenvielfalt von Zukunftstechnologien wie Künstlicher Intelligenz, Blockchain und Virtual Reality ist ebenfalls enorm. Aber wie kann diese Masse an Daten und Informationen bereitgestellt und verarbeitet werden? Vielleicht ist ein Umdenken in den Unternehmen notwendig: Tradierte Verhaltensmuster und Strategien gehören auf den Prüfstand.

Since Critical Infrastructures (CIs) use systems and equipment that are separated by long distances, Supervisory Control And Data Acquisition (SCADA) systems are used to monitor their behaviour and to send commands remotely. For a long time, operator of CIs applied the air gap principle, a security strategy that physically isolates the control network from other communication channels. True isolation, however, is difficult nowadays due to the massive spread of connectivity: using open protocols and more connectivity opens new network attacks against CIs. To cope with this dilemma, sophisticated security measures are needed to address malicious intrusions, which are steadily increasing in number and variety. However, traditional Intrusion Detection Systems (IDSs) cannot detect attacks that are not already present in their databases. To this end, we assess in this paper Machine Learning (ML) techniques for anomaly detection in SCADA systems using a real data set collected from a gas pipeline system and provided by the Mississippi State University (MSU). The contribution of this paper is two-fold: 1) The evaluation of four techniques for missing data estimation and two techniques for data normalization, 2) The performances of Support Vector Machine (SVM), Random Forest (RF), Bidirectional Long Short Term Memory (BLSTM) are assessed in terms of accuracy, precision, recall and F1 score for intrusion detection. Two cases are differentiated: binary and categorical classifications. Our experiments reveal that RF and BLSTM detect intrusions effectively, with an F1 score of respectively > 99% and > 96%.

Blockchain ist derzeit in aller Munde. Die Technologie, die oft auch präziser als ‚Distributed Ledger‘-Verfahren bezeichnet wird, soll großes ‚Disruptionspotenzial‘ besitzen. Immer noch sprechen wir jedoch in diesem Kontext vorwiegend über Kryptowährungen, wie Bitcoin, die auf diese Ansätze maßgeblich zurückgreifen sowie deren Probleme und davon ausgehende Gefahren. Dazu zählen insbesondere die teils erheblichen Kursschwankungen an den Märkten, die zahlreichen Betrugsversuche sowie der extrem hohe Energieverbrauch, der mit dem Mining-Prozess einhergeht.

Künstliche Intelligenz und Blockchain sind derzeit womöglich die am intensivsten diskutierten Themen im Bereich der digitalen Transformation. Beiden wird immer wieder erhebliches Potenzial zugeschrieben, die Spielregeln auf den Märkten aufzubrechen und zu disruptieren. Unweigerlich stellt sich dann die Frage, jenseits allen Buzz-Word-Bingos, ob sich hierbei dann nicht Szenarien ergeben, die einen kombinierten Einsatz nahelegen. Diese finden sich vor allem im Bereich des (Industrial) Internet of Things sowie der dezentralen Koordinierung autonomer intelligenter Systeme.

Blockchain-Technologie kann nicht auf den Märkten für die dezentrale Transaktionsabwicklung genutzt werden. Auch der Rückgriff als Gestaltungselement im politischen System wird diskutiert. Damit verbunden ist aber auch eine Ideologisierung politischer Prozesse.

Diskussionen über Blockchain kreisen oft um deren „disruptives Potenzial“ im Bereich der Transaktionsabwicklung etwa bei Kryptowährungen wie Bitcoin oder bei Smart Contracts. Jenseits dieser ökonomischen Belange könnte die Technologie jedoch auch die politischen und gesellschaftlichen Gegebenheiten auf den Kopf stellen – im Wege einer „Politischen Disruption“.

We use an application scenario that collects, transports and stores sensor data in a database. The data is gathered with a high frequency of 1000 datasets per second. In the context of this scenario, we analyze the performance of multiple popular database systems. The benchmark results include the load on the system writing the data and the system running the database.

Welcome to the Human Factors in Hypertext 2018 workshop (HUMAN'18) in Baltimore, Maryland, USA, the first workshop of a new series of workshops for the ACM Conference on Hypertext and Social Media. It has a strong focus on hypertext users and thus complements the machine analytics research that we experienced in previous conferences. The user-centric view on hypertext not only includes user interfaces and interaction, but also discussions about hypertext application domains. Furthermore, the workshop raises the question of how original hypertext ideas (e.g., Doug Engelbart's "augmenting human intellect" or Frank Halasz' "hypertext as a medium for thinking and communication") can improve today's hypertext systems. Historically, hypertext research is strongly connected to human factors. Hypertext pioneers, such as Doug Engelbart or Ted Nelson, focused on the usage of and interaction with hypertext. This workshop combines original hypertext research ideas with recent hypertext research trends. In addition, it consolidates different hypertext research areas from the viewpoint of human factors. Thus, HUMAN'18 fosters cross-cutting discussions and the development of new ideas.

The idea to associate information with so-called links was developed by hypertext pioneers in the 1960s. In the 1990s the Dexter Hypertext Reference Model was developed with the goal to provide a general model for node-link hypertext systems. In the 1990s and 2000s there were important steps made for hypertext infrastructures, which led to component-based open hypermedia systems (CB-OHS). In this paper we provide a detailed description of node-link structures. We argue that Dexter does not match the need of CB-OHS, as it supports a mix of multiple structure domains. Based on the implementation of link support in our system Mother we demonstrate how Dexter needs to be tailored accordingly. We further describe Mother's ability of node-link structures to interoperate with other available structure services and vice versa.

Wie die Blockchain-Technologie die Politik beeinflussen könnte und heute bereits tatsächlich beeinflusst.

Software applications become more and more dominant in our daily life and work. However, it is very difficult to provide sophisticated tools for all arising use cases. End-User Development (EUD) is a term that describes the development of applications by end users rather than professional developers. This enables them creating highly specialized solutions. End users are laypersons when it comes to building software, therefore they need appropriate tools for managing the whole development process. This includes designing, implementing and deploying applications. While there are already various tools available, we focus on a recommendation feature for graphical EUD tools, utilizing their spatial hypertext capabilities. In this paper we provide an overview of some common issues such tools are often struggling with. We explain how visually analyzing the workspace, parsing an implicit spatial hypertext and eventually presenting recommendations may tackle them. We further describe the project HEIMDALL in detail, especially the way of generating recommendations for software modules with the aim to raise users' awareness. Furthermore, we discuss the use of such a system that reaches a similar understanding of relationships between software modules as users have. Finally, we point to open issues that still need to be addressed to improve results and their presentation.

Blockchain und Künstliche Intelligenz sind derzeit die „Buzzwords“ schlechthin, wenn es um Gegenwart und Zukunft der Digitalisierung geht. Das Zusammenspiel dieser beiden Technologien dürfte jedoch für eine völlig neue Qualität in der Digitalen Transformation sorgen – zum Beispiel in der Medienbranche.