http://dl.gi.de/handle/20.500.12116/36361 Thu, 23 Jul 2026 15:33:44 GMT 2026-07-23T15:33:44Z http://dl.gi.de/handle/20.500.12116/36391 The Data Mining Group at University of Vienna Altinigneli, Can; Bauer, Lena Greta Marie; Behzadi, Sahar; Fritze, Robert; Hlaváčková-Schindler, Kateřina; Leodolter, Maximilian; Miklautz, Lukas; Perdacher, Martin; Sadikaj, Ylli; Schelling, Benjamin; Plant, Claudia How can we extract meaningful knowledge from massive amounts of data? The data mining group at University of Vienna contributes novel methods for exploratory data analysis. Our main research focus is on unsupervised learning, where we want to identify any kind of non-random structure or patterns in the data without restricting ourselves to a pre-defined target variable or analysis goal. Our major lines of current research are clustering, causality detection and highly efficient exploratory data analysis on massive data. Besides that, we develop application-specific methods addressing specific challenges in biomedicine, neuroscience and environmental sciences. In teaching, we offer fundamental and advanced courses in data mining, machine learning and scientific data management for Bachelor and Master students of computer science and related programs. Wed, 01 Jan 2020 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/36391 2020-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/36392 Evaluation Infrastructures for Academic Shared Tasks Schaible, Johann; Breuer, Timo; Tavakolpoursaleh, Narges; Müller, Bernd; Wolff, Benjamin; Schaer, Philipp Academic search systems aid users in finding information covering specific topics of scientific interest and have evolved from early catalog-based library systems to modern web-scale systems. However, evaluating the performance of the underlying retrieval approaches remains a challenge. An increasing amount of requirements for producing accurate retrieval results have to be considered, e.g., close integration of the system’s users. Due to these requirements, small to mid-size academic search systems cannot evaluate their retrieval system in-house. Evaluation infrastructures for shared tasks alleviate this situation. They allow researchers to experiment with retrieval approaches in specific search and recommendation scenarios without building their own infrastructure. In this paper, we elaborate on the benefits and shortcomings of four state-of-the-art evaluation infrastructures on search and recommendation tasks concerning the following requirements: support for online and offline evaluations, domain specificity of shared tasks, and reproducibility of experiments and results. In addition, we introduce an evaluation infrastructure concept design aiming at reducing the shortcomings in shared tasks for search and recommender systems. Wed, 01 Jan 2020 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/36392 2020-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/36387 Editorial Schaer, Philipp; Berberich, Klaus; Härder, Theo Wed, 01 Jan 2020 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/36387 2020-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/36386 Comparing Wizard of Oz & Observational Studies for Conversational IR Evaluation Elsweiler, David; Frummet, Alexander; Harvey, Morgan Systematic and repeatable measurement of information systems via test collections, the Cranfield model, has been the mainstay of Information Retrieval since the 1960s. However, this may not be appropriate for newer, more interactive systems, such as Conversational Search agents. Such systems rely on Machine Learning technologies, which are not yet sufficiently advanced to permit true human-like dialogues, and so research can be enabled by simulation via human agents. In this work we compare dialogues obtained from two studies with the same context, assistance in the kitchen, but with different experimental setups, allowing us to learn about and evaluate conversational IR systems. We discover that users adapt their behaviour when they think they are interacting with a system and that human-like conversations in one of the studies were unpredictable to an extent we did not expect. Our results have implications for the development of new studies in this area and, ultimately, the design of future conversational agents. Wed, 01 Jan 2020 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/36386 2020-01-01T00:00:00Z