For bachelor students we offer German lectures on database systems in addition with paper- or project-oriented seminars. Within a one-year bachelor project students finalize their studies in cooperation with external partners. For master students we offer courses on information integration, data profiling, search engines and information retrieval enhanced by specialized seminars, master projects and advised master theses.
The Web Science group focuses on various topics related to the Web, such as Information Retrieval, Natural Language Processing, Data Mining, Knowledge Discovery, Social Network Analysis, Entity Linking, and Recommender Systems. The group is particularly interested in Text Mining to deal with the vast amount of unstructured and semi-structured information available on the Web.
Most of our research is conducted in the context of larger research projects, in collaboration across students, across groups, and across universities. We strive to make available most of our data sets and source code.
Former PhD student of the HPI Research School at University of Potsdam Email: Johannes Lorey
Large-Scale Data Analysis and Processing
Linked Data Management
SPARQL Endpoint Metrics for Quality-Aware Linked Data Consumption
Proceedings of the 15th International Conference on Information Integration and Web-based Applications & Services (iiWAS '13)
In recent years, dozens of publicly accessible Linked Data repositories containing vast amounts of knowledge presented in the Resource Description Framework (RDF) format have been set up worldwide. By utilizing the SPARQL query language, users can consume, integrate, and present data from a federation of sources for different application scenarios. However, several challenges arise for distributed query processing across multiple SPARQL endpoints, such as devising suitable query optimization or result caching strategies. For implementing these techniques, one crucial aspect is determining appropriate endpoint features. In this work, we introduce several metrics that enable universal and fine-grained characterization of arbitrary Linked Data repositories. We present comprehensive approaches for deriving these metrics and validate them through extensive evaluation on real-world SPARQL endpoints. Finally, we discuss possible implications of our findings for data consumers.