Daniel Seebacher

TL;DR: This survey attempts to report, categorize and unify the diverse understandings and aims to establish a common vocabulary that will enable a wide audience to understand their differences and subtleties.

TL;DR: This work considers team sport as group movement including collaboration and competition of individuals following specific rule sets, and identifies important components of team sport data, exemplified by the soccer case, and explains how to analyzeteam sport data in general.

TL;DR: A visual-interactive and data-analysis support system focuses on key situations by using rule-based filtering and automatically annotating key types of soccer match elements to improve soccer player and match analysis.

TL;DR: Five years after the first state-of-the-art report on Commercial Visual Analytics Systems, a reevaluation of the Big Data Analytics field finds that innovation and research-driven development are increasingly sacrificed to satisfy a wide range of user groups.

TL;DR: An adaptive, scalable, and extensible image-based plagiarism detection approach suitable for analyzing a wide range of image similarities that was observed in academic documents and can complement other content-based feature analysis approaches to retrieve potential source documents for suspiciously similar content from large collections.