Simon Gottschalk
Research Group Leader at L3S Research Center, Leibniz University Hannover
Individual travel planning and the regional control of travel flows require innovative algorithms and data diversity. In order to enable attractive, personalised, environmentally aware and cross-modal travel offers, a major opportunity for mobility and tourism providers lies in capturing user needs in a dialogue-based manner and creating AI-based offers to reduce the time required for travel planning and to optimally utilise the infrastructure. The MoToRes project will explore the potential of a variety of heterogeneous data and develop new AI algorithms, the MoToRes platform for technical orchestration and prototypes for end users.
The ATTENTION! project project will systematically collect, model and analyse multiple global data sources such as trade data for imports and exports, company and web data. Based on global trade activities and their contextual information, artificial intelligence (AI) models will be developed and applied to detect and uncover illicit trade activities and their patterns in global, heterogeneous data. Among the challenges to be overcome are the lack of known cases, the complexity of the patterns and the need to explain detected suspicious cases. To this end, ATTENTION! relies on the interplay of supervised and unsupervised learning as well as the creation of a knowledge graph.
Die zunehmende Digitalisierung in vielen Anwendungsbereichen der Wirtschaft, Verwaltung und Wissenschaft führt zu ständig wachsenden Datenmengen und damit immer häufiger zur Notwendigkeit, datenbasierte Prognosen zu erstellen und Zusammenhänge in großen heterogenen Datenmengen zu erkennen. Machine Learning (ML) ist ein Kernthema in diesem Bereich, mit dem sich 64% aller deutschen Unternehmen aktiv beschäftigen. Die effiziente Anwendung aktueller ML-Verfahren erfordert jedoch ein sehr hohes Maß an Expertenwissen, was einer verbreiteten Nutzung von Machine Learning-Ansätzen, insbesondere durch kleine und mittlere Unternehmen (KMU), im Wege steht.Die zentrale Forschungsfrage des Simple-ML-Projekts lautet daher: Wie kann die Benutzbarkeit von ML-Verfahren signifikant verbessert werden um diese für einen breiteren Anwenderkreis leichter zugänglich zu machen?
The ALEXANDRIA project (ERC Nr. 339233) aims to develop models, tools and techniques necessary to explore and analyze Web archives in a meaningful way. ALEXANDRIA will significantly advance semantic and time-based indexing for Web archives using human-compiled knowledge available on the Web, to efficiently index, retrieve and explore information about entities and events from the past. The ALEXANDRIA Testbed will provide relevant collections and algorithms that enable further research on and practical application of research results to existing archives.