Suchergebnisse

Gedruckt erschienen im Universitätsverlag der TU Berlin, ISBN 978-3-7983-2899-0 ; Die digitale Revolution ist in vollem Gange. Längst hat die Digitalisierung alle Lebensbereiche der Gesellschaft erfasst. Angefangen bei der Kommunikation, über die industrielle Produktion, die Landwirtschaft, den Verkehr, die Medizin, das Bankwesen und die Politik. Selbst alte Kulturtechniken wie das handschriftliche Schreiben sind davon betroffen. Welche Auswirkungen und Folgen dieser fundamentale Umwälzungsprozess auf den Mensch, die Verfasstheit der Gesellschaft und die demokratische Ordnung hat, analysiert Christian Thomsen in seinen Kolumnen, die seit 2015 in der Tageszeitung Berliner Morgenpost erscheinen. Obwohl er sich für die Möglichkeiten und Potenziale der Digitalisierung begeistert, geraten ihm die Gefahren und Herausforderungen nicht aus dem Blick. Seine Kolumnen begleiten diesen Veränderungsprozess – klug, kritisch, optimistisch, nachdenklich. ; The digital revolution is in full swing. Digitalization has long captured all areas of life and society. It touches communication, industrial production, agriculture, transport, medicine, banking, and politics. Even ancient cultural technologies like handwriting are affected. In his columns, Christian Thomsen analyzes the effects and impact of this fundamental upheaval process on people, the constitution of society and on the democratic order. The texts have been published weekly in the daily newspaper Berliner Morgenpost since 2015. Thomsen is enthusiastic about the possibilities and potentials of digitalization, but he does not overlook its risks and challenges. His columns accompany this process of change – clever, critical, optimistic, and thoughtful.

Literaturverz. S. 217 - 220

On-Line Analytical Processing (OLAP) is a data analysis technique typically used for local and well-prepared data. However, initiatives like Open Data and Open Government bring new and publicly available data on the web that are to be analyzed in the same way. The use of semantic web technologies for this context is especially encouraged by the Linked Data initiative. There is already a considerable amount of statistical linked open data sets published using the RDF Data Cube Vocabulary (QB) which is designed for these purposes. However, QB lacks some essential schema constructs (e.g., dimension levels) to support OLAP. Thus, the QB4OLAP vocabulary has been proposed to extend QB with the necessary constructs and be fully compliant with OLAP. In this paper, we focus on the enrichment of an existing QB data set with QB4OLAP semantics. We first thoroughly compare the two vocabularies and outline the benefits of QB4OLAP. Then, we propose a series of steps to automate the enrichment of QB data sets with specific QB4OLAP semantics; being the most important, the definition of aggregate functions and the detection of new concepts in the dimension hierarchy construction. The proposed steps are defined to form a semi-automatic enrichment method, which is implemented in a tool that enables the enrichment in an interactive and iterative fashion. The user can enrich the QB data set with QB4OLAP concepts (e.g., full-fledged dimension hierarchies) by choosing among the candidate concepts automatically discovered with the steps proposed. Finally, we conduct experiments with 25 users and use three real-world QB data sets to evaluate our approach. The evaluation demonstrates the feasibility of our approach and shows that, in practice, our tool facilitates, speeds up, and guarantees the correct results of the enrichment process. ; Peer Reviewed ; Postprint (author's final draft)

On-Line Analytical Processing (OLAP) is a data analysis technique typically used for local and well-prepared data. However, initiatives like Open Data and Open Government bring new and publicly available data on the web that are to be analyzed in the same way. The use of semantic web technologies for this context is especially encouraged by the Linked Data initiative. There is already a considerable amount of statistical linked open data sets published using the RDF Data Cube Vocabulary (QB) which is designed for these purposes. However, QB lacks some essential schema constructs (e.g., dimension levels) to support OLAP. Thus, the QB4OLAP vocabulary has been proposed to extend QB with the necessary constructs and be fully compliant with OLAP. In this paper, we focus on the enrichment of an existing QB data set with QB4OLAP semantics. We first thoroughly compare the two vocabularies and outline the benefits of QB4OLAP. Then, we propose a series of steps to automate the enrichment of QB data sets with specific QB4OLAP semantics; being the most important, the definition of aggregate functions and the detection of new concepts in the dimension hierarchy construction. The proposed steps are defined to form a semi-automatic enrichment method, which is implemented in a tool that enables the enrichment in an interactive and iterative fashion. The user can enrich the QB data set with QB4OLAP concepts (e.g., full-fledged dimension hierarchies) by choosing among the candidate concepts automatically discovered with the steps proposed. Finally, we conduct experiments with 25 users and use three real-world QB data sets to evaluate our approach. The evaluation demonstrates the feasibility of our approach and shows that, in practice, our tool facilitates, speeds up, and guarantees the correct results of the enrichment process. ; Peer Reviewed ; Postprint (author's final draft)