Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
A survey of context modelling and reasoning techniques
Countries citing papers authored by Daniela Nicklas
Since
Specialization
Citations
This map shows the geographic impact of Daniela Nicklas's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniela Nicklas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniela Nicklas more than expected).
This network shows the impact of papers produced by Daniela Nicklas. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniela Nicklas. The network helps show where Daniela Nicklas may publish in the future.
Co-authorship network of co-authors of Daniela Nicklas
This figure shows the co-authorship network connecting the top 25 collaborators of Daniela Nicklas.
A scholar is included among the top collaborators of Daniela Nicklas based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Daniela Nicklas. Daniela Nicklas is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Kohlhase, Michael, Marc Berges, Jens Grubert, et al.. (2024). Project VoLL-KI. KI - Künstliche Intelligenz. 39(4). 299–309.
Nicklas, Daniela, et al.. (2018). Semi-Automatic Ontology Population for Online Quality Assessment of Particulate Matter Sensors.. 119–128.3 indexed citations
6.
Nicklas, Daniela, et al.. (2017). Towards Quality Aware Sensor Data Stream Processing in a Smart City Living Lab.. 36–41.1 indexed citations
7.
Finzel, Bettina, et al.. (2017). Towards Understanding Mobility in Museums.. 127–134.6 indexed citations
8.
Nicklas, Daniela, et al.. (2015). Towards a framework for sensor-based research and development platform for critical, socio-technical systems. 97–98.3 indexed citations
9.
Nicklas, Daniela, et al.. (2015). Semantic Data Exchange in e-Navigation. BTW. 601–611.1 indexed citations
10.
Nicklas, Daniela, et al.. (2011). StreamCars - Datenstrommanagementbasierte Verarbeitung von Sensordaten im Fahrzeug.. BTW. 710–713.
11.
Boll, Susanne, et al.. (2011). Open Sensor Platforms: The Sensor Web Enablement Framework and Beyond.. Multimedia Systems. 39–52.4 indexed citations
12.
Nicklas, Daniela, et al.. (2010). Sensordatenverarbeitung mit dem Open Source Datenstrommanagementframework Odysseus. GI Jahrestagung (2). 404–409.2 indexed citations
13.
Nicklas, Daniela, et al.. (2009). Vertailte Datenstromverarbeitung von Sensordaten.. Datenbank-Spektrum. 9. 37–43.1 indexed citations
14.
Westermann, Utz, et al.. (2009). SCAMPI - Sensor Configuration and Aggregation Middleware for Multi Platform Interchange. 2084–2097.2 indexed citations
Volz, Steffen, Daniela Nicklas, Matthias Großmann, & Matthias Wieland. (2008). On Creating a Spatial Integration Schema for Global, Context-aware Applications.. Biblioteca Digital da Memória Científica do INPE (National Institute for Space Research). 13–24.1 indexed citations
19.
Schwarz, Thomas, et al.. (2004). Keeping track of "flying elephants": Challenges in large-scale management of complex mobile objects. GI Jahrestagung (1). 288–292.4 indexed citations
20.
Rothermel, Kurt, Dieter Fritsch, Paul J. Kühn, et al.. (2003). SFB 627 Umgebungsmodelle für mobile kontextbezogene Systeme.. GI Jahrestagung (1). 103–115.1 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.