User behavior and the resulting behavioral data forms the basis of personalized feeds, recommendations and advertisements in social networks such as Facebook. These platforms are now required to provide users with their personal data. However, these dumps with chronological data in different files do not provide users insight in overarching themes and connections in their online behavior. In this paper, we discuss the development and preliminary evaluation of an exploratory interface for visual data exploration. First insights include that the less obvious, more associative and obscure connections are more interesting and relevant to the user than very close semantic or temporal connections.

Social networks such as Facebook are required to provide users with their personal data. However, these dumps do not provide users insight in overarching themes in their online behavior. In this poster, we discuss the development of Mother, a spatial hypertext system for visual data exploration. First insights include that the less obvious connections are more interesting and relevant to the user than very close semantic or temporal connections.

Social networks use several user interaction techniques for enabling and soliciting user responses, such as posts, likes and comments. Some of these triggers may lead to posts or comments that a user may regret at a later stage. In this article, we investigate how users may be supported in reflecting upon their past activities, making use of an exploratory spatial hypertext tool. We discuss how we transform raw Facebook data dumps into a graph-based structure and reflect upon design decisions. First results provide insights in users motivations for using such a tool and confirm that the approach helps them in discovering past activities that they perceive as outdated or even embarrassing.

Wesentlicher Bestandteil von KI ist die Fähigkeit, eigenständig zu lernen. Ähnlich wie in der Pädagogik gibt es verschiedene Lehrmethoden, um ein System zu trainieren. Daraus ergeben sich somit auch unterschiedliche Einsatzmöglichkeiten im Marketing.

Der Prozess der Preisfindung hat sich seit dem Aufkommen des Internets deutlich verändert. Schon immer bestand die Herausforderung darin, Informationen über das Verhalten der Konsumenten korrekt erheben und verarbeiten zu können, um die Preisbereitschaften der Kundschaft „optimal“ aus Unternehmenssicht zu identifizieren. Die Datenmenge, die hierzu durch die fortschreitende Digitalisierung bereitsteht, lässt sich schon seit geraumer für „dynamisierte“ Preisfestsetzungsverfahren nutzen. Dieses „Dynamic Pricing“ finden wir heute nicht nur im E-Commerce, wo entlang der vorliegenden Nutzerdaten individuell Angebote unterbreitet und unterschiedliche Preise aufgerufen werden. Auch im stationären Handel existieren zunehmend „digitale“ Preisauszeichnungen, die je nach Nachfrage oder Lagerbestand automatisch justiert werden können, auch sind durchaus Entwicklungen denkbar, die dabei auch vorliegende Informationen über potenzielle Käufer bei der Kalkulation miteinbeziehen.

Während wir hierzulande noch über die DSGVO, die ePrivacy-Verordnung und den Umgang mit Browsercookies diskutieren, erlangt die Datenökonomie andernorts, etwa in den USA oder China, eine völlig neue Qualität: die digitale Verwertung von Informationen aus unserer DNS.

Given the importance of an early anomaly detection, Intrusion Detection Systems (IDSs) are introduced in Supervisory Control And Data Acquisition (SCADA). Agents or probes form the cornerstone of any IDS by capturing network packets and extracting relevant information. However, IDSs are facing unprecedented challenges due to the escalation in the number, scale and diversity of attacks. Software-Defined Network (SDN) then comes into play and can provide the required flexibility and scalability. Building on that, we introduce Traffic Agent Controllers (TACs) that monitor SDN-enabled switches via Open-Flow. By using lightweight statistical metrics such as Kullback-Leibler Divergence (KLD), we are able to detect the slightest anomalies, such as stealth port scans, even in the presence of background traffic. The obtained metrics can also be used to locate the anomalies with precision over 90% inside a hierarchical network topology.

The main objectives of the Intelligent Transportation Systems (ITS) vision is to improve road safety, traffic management, and mobility by enabling cooperative communication among participants. This vision requires the knowledge of the current state of the road traffic, which can be obtained by collecting Floating Car Data (FCD) information using Dedicated Short-Range Communication (DSRC) based on the IEEE 802.11p standard. Most of the existing FCD collection protocols have been evaluated via simulations and mathematical models, while the real-world implications have not been thoroughly investigated. This paper presents an open-source implementation of two state-of-the-art FCD collection algorithms, namely BASELINE and DISCOVER. These algorithms are implemented in an open-source vehicular prototyping platform and validated in a real-world experimental setup.

It is our great pleasure to welcome you to the 30th Anniversary ACM Conference on Hypertext and Social Media at Hof University, Germany, on September 17-20, 2019. The ACM Hypertext conference is a premium venue for high quality peer-reviewed research on hypertext theory, systems and applications. It is concerned with all aspects of modern hypertext research including social media, semantic web, dynamic and computed hypertext and hypermedia as well as narrative systems and applications. The theme of Hypertext 2019 is "HYPERTEXT - TEAR DOWN THE WALL". This motto of the 30th ACM Hypertext conference goes hand in hand with the 30th anniversary of the fall of the Berlin Wall. Inspired by the historical events in Germany, Hypertext 2019 aims at reunifying different hypertext research directions and communities. Therefore, apart from the regular research tracks, Hypertext 2019 will feature a research track on 30 Years of Hypertext as well as an exhibition/creative track. 2019 will also be the 30th anniversary of the WWW. It is a perfect time to join in, reflect our common roots, and discuss how we can jointly address our current and future challenges. The conference will take place at the Institute of Information Systems (iisys) at Hof University, Germany. Hof lies midway between Frankfurt and Prague, Munich and Berlin and is very close to the former German-German border, in particular to the village of Modlareuth, called "little Berlin", which used to be divided by a wall. After exactly 20 years, Hypertext 2019 will take place in Germany again for the 2nd time. Hypertext 2019 is co-locating with the ACM Document Engineering Conference (DocEng'19) organized in Berlin, Germany, between September 23-16.

Welcome to the Human Factors in Hypertext 2019 workshop (HUMAN'19) in Hof, Germany, the second workshop of a young 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 strongly connects 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'19 fosters cross-cutting discussions and the development of new ideas.

Historically, there has been a tendency to consider hypertext as a type of system, perhaps characterized by provision of links or other structure to users. In this paper, we consider hypertext as a method of inquiry, a way of viewing arbitrary systems. In this view, what are traditionally called "navigational hypertext systems" might be considered as information retrieval systems, "spatial hypertext systems" as brainstorming systems, etc., while their "hypertext" nature results from the way in which such systems are conceived, developed, and/or presented. The benefit of such a shift is the ability to apply this hypertextual method of inquiry to systems not normally considered part of the hypertext community. In this paper, we specifically apply this view to artificial intelligence, and examine how this application can be productive.

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.