Archaeoinformatics - Data Science

BA/MA: Data Science Applications in Marine Sciences

There are multiple open topics available that target data science applications in marine science. If you are interested in one of the topics or have an idea about a related topic, please contact Carola Trahms, M.Sc.
You can find more suggested topics below.

Fish Larvae Trajectories in the Mediterranean Sea

Example data that can be encountered in marine data science.

 

BA/MA: Efficient spatio-temporal indexing for HINs: Getting from measurement tables to HINs

Example of a spatial HIN for marine data science

Much of the data and measurements obtained in marine science are of a spatial and/or temporal nature, i.e. they are associated with geo-coordinates or time stamps.
These spatio-temporal properties can be leveraged to obtain new, and deeper insights into the data. However managing spatial and temporal data often requires careful indexing of the data, in order to remain efficient. In this thesis you will study efficient methods to index spatio-temporal data for Heterogeneous Information Networks (HINs), which are large graphs, where different types of nodes and relationships are modelled. This topic offers the opportunity to hone skills and techniques learned in lectures like Information Systems, Geo-Information Systems, and Methods of Efficient Similarity Search in Large Databases (although the latter two are not a pre-requisite).

BA/MA: (Linear) combinations of MetaStructures for Clustering or Community Detection in (schema-rich) HINs

Heterogeneous Information Networks (HINs) are graphs, where nodes have different types, and edges form different relationships between the nodes (a homogeneous information network would just be a plain graph). In all graphs, but especially HINs, it is of great interest to find groups or communities that exhibit similar behavior or are more closely related to one another. Meta Structures are complex relationships in HINs, which can be used to express the 'relatedness' of nodes within the graph, and thus provide a powerful tool available to be used in clustering or community detection. In this thesis you will study efficient (fast, scalable) and effective (meaningful) clustering and CD algortihms using (linear) combinations of meta structures. Participation in a lecture such as KDDM (or similar) is required.