05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
Browse
4 results
Search Results
Item Open Access CUTE - CRETA Un-/Shared Task zu Entitätenreferenzen(2017) Reiter, Nils; Blessing, André; Echelmeyer, Nora; Kremer, Gerhard; Koch, Steffen; Murr, Sandra; Overbeck, Maximilian; Pichler, AxelDies ist die Veröffentlichung eines shared/unshared Task Workshops (entwickelt in CRETA: Center for Reflected Text Analytics), der im Rahmen der DHd 2017 in Bern (CH) stattfand. Im Gegensatz zu shared tasks, bei denen die Performanz verschiedener Systeme/Ansätze/Methoden direkt anhand einer klar definierten und quantitativ evaluierten Aufgabe verglichen wird, sind unshared tasks offen für verschiedenartige Beiträge, die auf einer gemeinsamen Datensammlung basieren. Shared und Unshared Tasks in den Digital Humanities sind ein vielversprechender Weg, Kollaboration und Interaktion zwischen Geistes-, Sozial- und ComputerwissenschaftlerInnen zu fördern und zu pflegen. Konkret riefen wir dazu auf, gemeinsam an einem heterogenen Korpus zu arbeiten, in dem Entitätenreferenzen annotiert wurden. Das Korpus besteht aus Parlamentsdebatten des Deutschen Bundestags, Briefen aus Goethes Die Leiden des jungen Werther, einem Abschnitt aus Adornos Ästhetischer Theorie und den Büchern von Wolframs von Eschenbach Parzival (mittelhochdeutsch). Auch wenn jede Textsorte ihre eigenen Besonderheiten hat, wurden alle nach einheitlichen Annotationsrichtlinien annotiert, die wir auch zur Diskussion stellten. Wir veröffentlichen hier den Aufruf zu Workshop-Beiträgen, die Annotationsrichtlinien, die Korpusdaten samt Beschreibung und die einführenden Vortragsfolien des Workshops.Item Open Access Visual search and analysis of documents in the intellectual property domain(2012) Koch, Steffen; Ertl, Thomas (Prof. Dr.)Today’s society generates and stores digital information in enormous amounts and at rapidly increasing rates. This trend affects all parts of modern society, such as commerce and economy, politics and governments, health and medicine, science in general, media and entertainment, the private sector, etc. The stored information comprises text documents, images, audio files, videos, structured data from a variety of sources, as well as multimodal combinations of them, and is available in a multitude of electronic formats and flavors. As a consequence, the need for automated and interactive tools supporting tasks, such as searching, exploring, monitoring, sorting, and making sense of this information at different levels of abstraction and within different but steadily converging domains, increases at the same pace as the data is generated and represents one of the biggest challenges for current computer science. A relatively young approach to tackle these tasks by exploiting human analytic power in synergetic combination with advanced computerized techniques has emerged with the research field of visual analytics. Visual analytics aims at combining automated methods, visualization techniques, and approaches from the field of human computer interaction in order to equip analysts with more powerful tools, tailored to domains, where large amounts of data must be analyzed. In this work, visual analytics methods and concepts play a central role. They are used to search and analyze texts or multimodal documents containing a considerable amount of textual content. The presented approaches are primarily employed for analyzing a very special type of document from the intellectual property domain, namely patents. Since the retrieval and analysis tasks carried out in the patent domain differ greatly from standard search and analysis tasks regarding rigorous requirements, high costs, and the involved risks, new, more effective, efficient, and more reliable methods need to be developed. Accordingly, this thesis focuses on researching the combination of automatic methods and information visualization by using advanced interaction techniques in order to improve upon the state of the art in patent literature retrieval. Such integration is achieved and exemplified through different visual analytics prototypes, aiming at creating support for real-world tasks and processes. The main contributions presented in this thesis encompass enhancements for all stages of patent literature analysis processes. This includes improving patent search by presenting techniques for interactive visual query building, which helps analysts to formulate complex information needs, the development of a technique that allows users to build their own precise search mechanism in the form of binary classifiers, and advanced approaches for making sense of a retrieved result set through visual analysis. The latter builds the base to let users generate insights needed for judging and improving previous query formulations. Interaction methods facilitating forward analysis as well as feedback loops, which constitute a critical part of visual analytics approaches, are discussed afterwards. These methods are the key to integrating all stages of the patent analysis process in a seamless visual manner. Another contribution is the discussion of scalability issues in context of the described visual analytics approaches. Especially interaction scalability, the recording of analytic provenance, insight management, the visualization of analytic reporting, and collaborative approaches are addressed. Although the described approaches are exemplified by applying them to the field of intellectual property analysis, the developments regarding search and analysis have the potential to be adapted to complicated text document retrieval and analysis tasks in various domains. The general ideas regarding the facilitation of low-level feedback loops, user-steered machine classification, and technical solutions for diminishing negative scalability effects can be directly transferred to other visual analytics scenarios.Item Open Access Case study on privacy-aware social media data processing in disaster management(2020) Löchner, Marc; Fathi, Ramian; Schmid, David ‘-1’; Dunkel, Alexander; Burghardt, Dirk; Fiedrich, Frank; Koch, SteffenSocial media data is heavily used to analyze and evaluate situations in times of disasters, and derive decisions for action from it. In these critical situations, it is not surprising that privacy is often considered a secondary problem. In order to prevent subsequent abuse, theft or public exposure of collected datasets, however, protecting the privacy of social media users is crucial. Avoiding unnecessary data retention is an important question that is currently largely unsolved. There are a number of technical approaches available, but their deployment in disaster management is either impractical or requires special adaption, limiting its utility. In this case study, we explore the deployment of a cardinality estimation algorithm called HyperLogLog into disaster management processes. It is particularly suited for this field, because it allows to stream data in a format that cannot be used for purposes other than the originally intended. We develop and conduct a focus group discussion with teams of social media analysts. We identify challenges and opportunities of working with such a privacy-enhanced social media data format and compare the process with conventional techniques. Our findings show that, with the exception of training scenarios, deploying HyperLogLog in the data acquisition process will not distract the data analysis process. Instead, several benefits, such as improved working with huge datasets, may contribute to a more widespread use and adoption of the presented technique, which provides a basis for a better integration of privacy considerations in disaster management.Item Open Access Studies and design considerations for animated transitions between small-scale visualizations(2023) Huth, Franziska; Blascheck, Tanja; Koch, Steffen; Ertl, ThomasSmall-scale visualizations can augment text, show information on mobile devices, or geographical information on a map. In such situations, there is often not enough space to show complex data with approaches like juxtaposed visualizations. To alleviate this issue, we propose the use of animated transitions between several small-scale visualizations. We discuss design considerations for animated transitions between small-scale visualizations and differences to normal-sized visualizations. Further, we present the results of two online studies on the effectiveness of those animated transitions to convey information and attribute relations, as well as the mental load of following the animated transitions. As a result, we found that animated transitions between visualizations are understandable in small scale, but performance depends on the specific task and the type of operation carried out with the animated transition.