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Prof. Dr. Heiko Schuldt

Department of Mathematics and Computer Sciences
Profiles & Affiliations

Projects & Collaborations

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Polypheny-DDI

Research Project  | 3 Project Members

In recent years, data-driven research has established itself as the fourth pillar in the spectrum of scientific methods, alongside theory, empiric research, and computer-based simulation. In various scientific disciplines, increasingly large amounts of -both structured and unstructured- data are being generated or existing data collections that have originally been isolated from each other are being linked in order to gain new insights. he process of generating knowledge from raw data is called Data Science or Data Analytics. The entire data analytics pipeline is quite complex, and most work focuses on the actual data analysis (using machine learning or statistical methods), while largely neglecting the other elements of the pipeline. This is particularly the case for all aspects related to data management, storage, processing, and retrieval - even though these challenges actually play an essential role. A Distributed Data Infrastructure (DDI) supports a large variety of data management features as demanded by the data analytics pipeline. However, DDIs are usually very heterogeneous in terms of data models, access characteristics, and performance expectations. In addition, DDIs for integrating, continuously updating, and querying data from various heterogeneous applications need to overcome the inherent structural heterogeneity and fragmentation. Recently, polystore databases have gained attention because they help overcome these limitations by allowing data to be stored in one system, yet in different formats and data models and by offering one joint query language. In past work, we have developed Polypheny-DB, a distributed polystore that integrates several different data models and heterogeneous data stores. Polypheny-DB goes beyond most existing polystores and even supports data accesses with mixed workloads (e.g., OLTP and OLAP). However, polystores are limited to rather simple object models, static data and exact queries. When individual data items follow a complex inherent structure and consist of several heterogeneous parts between which dedicated constraints exist, when the access goes beyond exact Boolean queries, when data is not static but continuously produced, and/or when objects need to be preserved in multiple versions, then polystores quickly reach their limits. At the same time, these are typical requirements for data management within a data analytics pipeline. Examples are scientific instruments that continuously produce new data as data streams; social network analysis that requires support for complex object models including structured and unstructured content; data produced by imaging devices that requires sophisticated similarity search support, or frequently changing objects that are subject to time-dependent analyses. The objective of the Polypheny-DDI project is to seamlessly combine the functionality of a polystore database with that of a distributed data infrastructure to meet the requirements of data science applications. It will focus on i.) supporting complex composite object models and enforcing constraints between the constituent parts; ii.) supporting similarity search in multimedia content, and iii.) supporting continuous data streams and temporal/multiversion data.

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Video for Scientific Outreach of the Research Network Responsible Digital Society

Research Networks of the University of Basel  | 8 Project Members

The research network "Responsible Digital Society" is involved in a variety of ways to strengthen the promotion of interdisciplinary exchange and cooperative research in the field of digital transformation.

In the area of research, the network creates forums for regular scientific exchange and supports the coordination of interdisciplinary research proposals. In the area of promoting young researchers, the network organizes summer and winter schools for them. In the area of networking, the network promotes regular exchanges with industrial partners in the region. In the area of outreach, the network strengthens the public dialogue by organizing colloquia and panel discussions on digitization with guests from various disciplines.

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Visual Politician

Research Project  | 4 Project Members

VIP - the VIsual Politician Citizens and especially the younger generations increasingly turn towards digital forms of information when they update themselves about political matters: Not only do established media invest in their online presence, social media platforms also allow politicians to distribute information more directly and without an intermediary (eg. Kruikemeier, Van Noort et al. 2013, Rauchfleisch and Metag 2015). We aim to shed new light in this field of representative- voter communication by analyzing how politicians use visuals in their online communication with voters and how they react to it. In particular, the use of images (visuals) has mostly been neglected or investigated on a small scale (see e.g.Kruikemeier, Gattermann et al. 2018) since it is technically demanding. However, visual - photos and videos - of politicians in social media are of particular interest since visuals have the potential to have a different effect than verbal communication. Visuals show a more human image of politicians (Loader, Vromen et al. 2016) and have the potential to transmit personalized information better than ideas (Zamora 2010) and have a stronger emotional effect (Samuel-Azran, Yarchi et al. 2018). Online news consumption has been linked to the filter bubble and echo chamber by various authors (Conover, Ratkiewicz et al. 2011, Bessi, Coletto et al. 2015, DiFranzo and Gloria-Garcia 2017). Both terms capture the notion that users on social media have a tendency to build homogeneous communities with polarized views of the world. Thus, we will track how visuals are shared, quoted, or re-used otherwise. This audience engagement plays a particularly central role in politicians - voter communication since it gives a measure how users react to the signals sent by politicians (Metz, Kruikemeier et al. 2019). This will be done based on existing applications (e.g. TwitterStand (Sankaranarayanan, Samet et al. 2009)) which need to be further developed to include visual information. In a last part of the project, we will focus on the perception of visual information by politicians and study experimentally how citizens react to it.