Workshopband MuC 2016

Hier finden Sie die Beiträge, die im Workshopband der Mensch und Computer 2016 veröffentlicht wurden.

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  • Konferenzbeitrag
    Smart Factories: Mitarbeiter-zentrierte Informationssysteme für die Zusammenarbeit der Zukunft
    (Mensch und Computer 2016 – Workshopband, 2016) Kaiser, Christian; Stocker, Alexander; Richter, Alexander; Wifling, Martin; Fritz, Johannes; Kittl, Christian; Weyers, Benjamin; Dittmar, Anke
    In Unternehmen mit komplexen Produkten und Dienstleistungen wachsen Anforderungen, welche an Kommunikation, Koordination und Zusammenarbeit zwischen Informations- und Wissensarbeitern gestellt werden, stetig. Dieser Workshop will vor dem Hintergrund des durch die Initiatoren im „Factory of the Future“ Programm gestarteten EU-Projects FACTS4WORKERS eine nachhaltige Plattform schaffen, um aktuelle und zukünftige Fragestellungen rund um den Einsatz neuer, nutzerzentrierter Informationssysteme und -technologien in Industrieunternehmen interdisziplinär zu diskutieren. Der Workshop baut auf eine ganze Reihe vorangegangener Workshops auf, welche das Thema soziale Interaktion in Organisationen aus unterschiedlichen Gesichtspunkten beleuchtet haben. Er spricht daher auch Praktiker und Wissenschaftler an, die in den letzten Jahren an diesen Workshops teilgenommen haben und möchte diese Community und die betrachteten Fragestellungen erweitern.
  • Konferenzbeitrag
    Trustworthiness of visualizations of mobility-induced CO2 emissions
    (Mensch und Computer 2016 – Workshopband, 2016) Platte, Laura; Freitag, Svenja; Schubert, Lisa; Molis, Jerg; Valdez, André Calero; Ziefle, Martina; Weyers, Benjamin; Dittmar, Anke
    In the face of global warming, CO2 emissions have to be reduced. Everybody can contribute by making CO2 aware decisions. But what decisions are good? Next to texts and figures, visualizations are an important communicative tool to encapsulate information in a way that is understood quickly and potentially changes consumer behavior. To have an impact, they have to evoke the trust of the recipient. In this qualitative study we address the mostly neglected topic of how individuals come to trust visualizations. We conducted interviews with eight subjects to compare trustworthiness of two visualizations of CO2 emissions of different means of transportation, either using a bar chart or a chart that depicts differently sized clouds. We analyzed the answers by categorizing different criteria. Overall, the bar chart was considered more trustworthy. We argue that trustworthiness of visualizations follows a complex process, which considers different criteria that interact with each other. The criteria with the most influence trustworthiness are: completeness of information, necessity of information, neutrality, reading accuracy and plainness of the graph. Lastly, trust might follow a U-shaped curve when plotted over the density of graph-features.
  • Konferenzbeitrag
    Ein Konzept für die Klassifizierung subjektiver Sicherheit in Tweets
    (Mensch und Computer 2016 – Workshopband, 2016) Rother, Kristian; Karl, Inga; Nestler, Simon; Weyers, Benjamin; Dittmar, Anke
    In diesem Beitrag wird die erste Iteration eines Prozesses zur Konzeption einer Annotationsrichtlinie zur Klassifizierung von Tweets hinsichtlich des in ihnen ausgedrückten subjektiven Sicherheitsgefühls dargelegt. Basierend auf einer initialen, rudimentären Annotationsrichtlinie wurden Tweets von vier Annotatoren klassifiziert. Diese Klassifizierung wurde zu einem späteren Zeitpunkt wiederholt und die Annotationen wurden hinsichtlich der Interrater- und Intrarater-Reliabilität untersucht. Anhand qualitativer Interviews wurden Handlungsempfehlungen für die Überarbeitung der Annotationsrichtlinie abgeleitet.
  • Konferenzbeitrag
    Usable Security Policy Specification
    (Mensch und Computer 2016 – Workshopband, 2016) Rudolph, Manuel; Feth, Denis; Weyers, Benjamin; Dittmar, Anke
    Security policies determine which security requirements have to be met in a domain and how they are implemented organizationally and/or technically. However, their specification at run-time poses a challenge for policy authors (e.g., IT administrators or end users), especially if they are inexperienced in this task. Thus, specification interfaces have to guide the policy author during the specification process. However, matching appropriate specification processes to the policy authors’ individual needs is challenging due to a high variability in the authors’ skill levels and security perceptions. In this paper, we identify existing specification approaches, derive generic specification paradigms and show the feasibility of one of them in an industrial case study.
  • Konferenzbeitrag
    Human-in-the-Loop Processes as Enabler for Data Analytics in Digitalized Organizations
    (Mensch und Computer 2016 – Workshopband, 2016) Thiele, Thomas; Sommer, Thorsten; Schröder, Stefan; Richert, Anja; Jeschke, Sabina; Weyers, Benjamin; Dittmar, Anke
    As a key driver for innovation in science, economy and society, digitalization affects almost every aspect of our daily working and living environments. The opportunity to track data about processes, persons, and other entities in organizations allows new opportunities for digitalized working scenarios and the creation of new perspectives on matters such as inter- and intra-organizational relationships. The aim of this paper is to elaborate on these perspectives on the basis of studies that are currently a part of our research activities. Firstly, a framework is outlined that combines topic modeling of textual data and machine learning to derive thematic synergies in the data, for example, between organizations or research projects. Secondly, classical benchmarking approaches are extended by developing a suitable text-mining process for interdisciplinary research. Lastly, a brief concept about evolution as a method for further optimizations and its implications for the human-in-the-loop process is outlined. Altogether, the approaches contribute to a comprehensive human-in-the-loop model – defined as a model that combines intelligent data technologies with human interaction – in the culture of innovation amongst modern, highly digitalized organizations.
  • Konferenzbeitrag
    Age-dependent health data visualizations: a research agenda
    (Mensch und Computer 2016 – Workshopband, 2016) Theis, Sabine; Bröhl, Christina; Rasche, Peter; Wille, Matthias; Schlick, Christopher M.; Mertens, Alexander; Weyers, Benjamin; Dittmar, Anke
    While current data visualization research is profoundly driven by innovation and technical aspects, the project Tech4Age focusses on the evaluation of human factors in health-related data and information visualization. This workshop paper initially describes the background and motivation of ergonomic health-data visualization research. Subsequently we present planned studies as well as preliminary results from one general and one task-dependent study which we see as basis for generalizable results of ongoing ergonomic health-visualization evaluations. Finally, we present the research design of an evaluation study aiming at general recommendations for the age-differentiated design of health data visualizations.
  • Konferenzbeitrag
    Graph Complexity in visual recommender systems for scientific literature
    (Mensch und Computer 2016 – Workshopband, 2016) Abels, Stephan; Greven, Christoph; Valdez, André Calero; Schroeder, Ulrik; Ziefle, Martina; Weyers, Benjamin; Dittmar, Anke
    Digital libraries are becoming larger, while suffering from inefficient interfaces and search patterns. Recommender Systems are a sensible and important service for users of digital libraries. The aim of recommender systems is to reduce cognitive effort, simplify search and to embed results in a larger context. In this article we compare to recommender systems – the Action Science Explorer and Papercube. Both systems are used to recommend scientific literature and use graph-based approaches. From user studies we derive the need for research to understand complexity of graphs.
  • Konferenzbeitrag
    Towards Interactive Recommender Systems with the Doctor-in-the-Loop
    (Mensch und Computer 2016 – Workshopband, 2016) Holzinger, Andreas; Valdez, André Calero; Ziefle, Martina; Weyers, Benjamin; Dittmar, Anke
    Recommender Systems are a perfect example for automatic Machine Learning (aML) – which is the fastest growing field in computer science generally and health informatics specifically. The general goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions and decision support – which is of the central interest of health informatics. Whilst automatic approaches greatly benefit from big data with many training sets, in the health domain experts are often confronted with a small number of complex data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive Machine Learning (iML) may be of help, which can be defined as “algorithms that can interact with agents and can optimize their learning behaviour through these interactions, where the agents can also be human”. Such a human can be an expert, i.e. a medical doctor, and this “doctor-in-the-loop” can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human expert agent involved in the learning phase. Important future research aspects are in the combined use of both human intelligence and computer intelligence, in the context of hybrid multi-agent recommender systems which can also make use of the power of crowdsourcing to make use of joint decision making – which can be very helpful e.g. in the diagnosis and treatment of rare diseases.
  • Konferenzbeitrag
    Human Factors in Information Visualization and Decision Support Systems
    (Mensch und Computer 2016 – Workshopband, 2016) Valdez, André Calero; Brauner, Philipp; Ziefle, Martina; Kuhlen, Torsten Wolfgang; Sedlmair, Michael; Weyers, Benjamin; Dittmar, Anke
    With the increase in data availability and data volume it becomes increasingly important to extract information and actionable knowledge from data. Information Visualization helps the user to understand data by utilizing vision as a relatively parallel input channel to the user’s mind. Decision Support systems on the other hand help users in making information actionable, by suggesting beneficial decisions and presenting them in context. Both fields share a common need for understanding the interface between the computer and the human. This makes human factors research critical for both fields. Understanding limitations of human perception, cognition and action, as well as their variance must be understood to fully leverage information visualization and decision support. This article reflects on research agendas for investigating human factors in the aforementioned fields.
  • Konferenzbeitrag
    On Studying Human Factors in Complex Cyber-Physical Systems
    (Mensch und Computer 2016 – Workshopband, 2016) Brauner, Philipp; Valdez, André Calero; Philipsen, Ralf; Ziefle, Martina; Weyers, Benjamin; Dittmar, Anke
    Deep penetration of modern information and communication technology in manufacturing companies (vertical integration) and across supply chains (horizontal integration) leads to an increasing amount and complexity of information that needs to be perceived, filtered, processed, and reacted to. Yet, the human factors that influence performance are insufficiently understood. This article outlines that individual factors, interface factors, and system factors affect overall performance and it presents two complementary research methodologies for identifying and quantifying these factors. On one side, we show that controlled laboratory experiments with singular decision tasks can precisely identify and quantify factors contributing to performance. On the other side, we use business simulation games with realistic decision tasks that can quantify the complexity of the underlying system. Our studies show that information amount, complexity, and presentation affect performance and that Decision Support Systems can increase performance and decrease error rates if and only if they are designed correctly. The article concludes with a research agenda to specifically understand which factors influence performance and how humans in the loop can be supported.