Matthias Renz
Biographic Data
| ID | 5216356 |
|---|---|
| NAME | Matthias Renz |
| GIVEN NAMES | Matthias |
| FAMILY NAME | Renz |
| SIGNATURE | RENZ M |
| AFFILIATIONS | Faculty of Engineering, Department of Computer Science Kiel University Kiel Germany |
| ORCID | 0000-0002-2024-7700 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 5 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Author response of
A recommendation of: Steffen Strohm, Matthis thor Straten, Andrea Göhring, Hartwig Bünning, Peer Kröger, Oliver Nakoinz, Matthias Renz, Christoph Rinne Bridging Interdisciplinary Research Data Management and Data Science through Modular Research Processes https://doi.org/10.5281/zenodo.20787457
A Network View on the Big Exchange Project
This study presents a proof-of-concept for integrating 14 datasets on 11 archaeologically relevant raw materials from the Big Exchange project into a heterogeneous information network (HIN), an informational structure explicitly modelling multiple object and relationship types. HIN-based approaches provide archaeologists with a powerful means of identifying structural and semantic patterns in material distributions. In this study, a spatial exten…
Author response of
A recommendation of: Mattis thor Straten, Steffen Strohm, Johanna Hilpert, Benjamin Serbe, Tim Kerig, Matthias Renz A Network View on the Big Exchange Project: Integrating and Analysing Heterogeneous Datasets https://doi.org/10.5281/zenodo.17856414
Provenance Analysis Based on Cluster In‐Betweenness and Support Vector Machines
Provenance reconstruction using strontium and lead stable isotopes can produce complex multidimensional fingerprints, challenging traditional methods. Identifying nonlocals, who migrated between sites, is a major task. Migrants are identifiable by divergent multi‐isotope fingerprints due to isotopic mixing between origin and destination sites. Detecting early migrants, however, is possible in exceptional cases only. This study used a Support Vect…
A Review of Deep Learning Models for Twitter Sentiment Analysis
Microblogging site Twitter (re-branded to X since July 2023) is one of the most influential online social media websites, which offers a platform for the masses to communicate, expresses their opinions, and shares information on a wide range of subjects and products, resulting in the creation of a large amount of unstructured data. This has attracted significant attention from researchers who seek to understand and analyze the sentiments containe…
No prominent works on this page.
A Review of Deep Learning Models for Twitter Sentiment Analysis
Microblogging site Twitter (re-branded to X since July 2023) is one of the most influential online social media websites, which offers a platform for the masses to communicate, expresses their opinions, and shares information on a wide range of subjects and products, resulting in the creation of a large amount of unstructured data. This has attracted significant attention from researchers who seek to understand and analyze the sentiments containe…
Author response of
A recommendation of: Mattis thor Straten, Steffen Strohm, Johanna Hilpert, Benjamin Serbe, Tim Kerig, Matthias Renz A Network View on the Big Exchange Project: Integrating and Analysing Heterogeneous Datasets https://doi.org/10.5281/zenodo.17856414
Provenance Analysis Based on Cluster In‐Betweenness and Support Vector Machines
Provenance reconstruction using strontium and lead stable isotopes can produce complex multidimensional fingerprints, challenging traditional methods. Identifying nonlocals, who migrated between sites, is a major task. Migrants are identifiable by divergent multi‐isotope fingerprints due to isotopic mixing between origin and destination sites. Detecting early migrants, however, is possible in exceptional cases only. This study used a Support Vect…
Author response of
A recommendation of: Steffen Strohm, Matthis thor Straten, Andrea Göhring, Hartwig Bünning, Peer Kröger, Oliver Nakoinz, Matthias Renz, Christoph Rinne Bridging Interdisciplinary Research Data Management and Data Science through Modular Research Processes https://doi.org/10.5281/zenodo.20787457
A Network View on the Big Exchange Project
This study presents a proof-of-concept for integrating 14 datasets on 11 archaeologically relevant raw materials from the Big Exchange project into a heterogeneous information network (HIN), an informational structure explicitly modelling multiple object and relationship types. HIN-based approaches provide archaeologists with a powerful means of identifying structural and semantic patterns in material distributions. In this study, a spatial exten…
Archaeology and ancient environmental studies (2 works) · Big data (2 works) · Artificial Intelligence (1 works) · Assemblage (archaeology (1 works) · Big Data and Digital Economy (1 works) · Cluster (spacecraft) (1 works) · Complex Network Analysis Techniques (1 works) · Computer Science (1 works) · Data Analysis with R (1 works) · Data exchange (1 works)