Daniel Puschmann

721 total citations
9 papers, 495 citations indexed

About

Daniel Puschmann is a scholar working on Signal Processing, Artificial Intelligence and Social Psychology. According to data from OpenAlex, Daniel Puschmann has authored 9 papers receiving a total of 495 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Signal Processing, 6 papers in Artificial Intelligence and 1 paper in Social Psychology. Recurrent topics in Daniel Puschmann's work include Time Series Analysis and Forecasting (7 papers), Data Stream Mining Techniques (4 papers) and Data Management and Algorithms (3 papers). Daniel Puschmann is often cited by papers focused on Time Series Analysis and Forecasting (7 papers), Data Stream Mining Techniques (4 papers) and Data Management and Algorithms (3 papers). Daniel Puschmann collaborates with scholars based in United Kingdom, Ireland and Austria. Daniel Puschmann's co-authors include Payam Barnaghi, Frieder Ganz, Rahim Tafazolli, María Bermúdez-Edo, François Carrez, Ralf Tönjes, Josiane Xavier Parreira, Alessandra Mileo, João F. P. Fernandes and Cosmin-Septimiu Nechifor and has published in prestigious journals such as PLoS ONE, IEEE Access and IEEE Internet of Things Journal.

In The Last Decade

Daniel Puschmann

8 papers receiving 479 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Daniel Puschmann United Kingdom	7	219	193	103	92	80	9	495
Ammar Gharaibeh Jordan	7	306 1.4×	95 0.5×	35 0.3×	141 1.5×	62 0.8×	21	571
Mohamed Ben Ahmed Morocco	11	160 0.7×	242 1.3×	76 0.7×	87 0.9×	130 1.6×	97	570
Yuhong Li China	12	182 0.8×	93 0.5×	146 1.4×	67 0.7×	120 1.5×	31	525
Jaesoo Yoo South Korea	12	289 1.3×	156 0.8×	83 0.8×	178 1.9×	95 1.2×	158	588
Bryan Scotney United Kingdom	10	290 1.3×	74 0.4×	24 0.2×	137 1.5×	123 1.5×	57	563
Yaron Kanza United States	18	297 1.4×	217 1.1×	473 4.6×	152 1.7×	77 1.0×	86	845
Tao Guo China	14	147 0.7×	213 1.1×	288 2.8×	182 2.0×	89 1.1×	66	625
Fazel Anjomshoa United States	7	137 0.6×	81 0.4×	45 0.4×	118 1.3×	30 0.4×	9	377
Wuman Luo Hong Kong	8	119 0.5×	257 1.3×	317 3.1×	124 1.3×	78 1.0×	17	582
Wee Siong Ng Singapore	12	355 1.6×	126 0.7×	147 1.4×	105 1.1×	72 0.9×	42	570

Countries citing papers authored by Daniel Puschmann

Since Specialization

Citations

This map shows the geographic impact of Daniel Puschmann'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 Daniel Puschmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Puschmann more than expected).

Fields of papers citing papers by Daniel Puschmann

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Puschmann. 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 Daniel Puschmann. The network helps show where Daniel Puschmann may publish in the future.

Co-authorship network of co-authors of Daniel Puschmann

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Puschmann. A scholar is included among the top collaborators of Daniel Puschmann 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 Daniel Puschmann. Daniel Puschmann is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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9 of 9 papers shown

1.

Papachristou, Nikolaos, Daniel Puschmann, Payam Barnaghi, et al.. (2018). Learning from data to predict future symptoms of oncology patients. PLoS ONE. 13(12). e0208808–e0208808. 21 indexed citations

2.

Puschmann, Daniel, Payam Barnaghi, & Rahim Tafazolli. (2017). Using LDA to Uncover the Underlying Structures and Relations in Smart City Data Streams. IEEE Systems Journal. 12(2). 1755–1766. 11 indexed citations

3.

Puschmann, Daniel, Payam Barnaghi, & Rahim Tafazolli. (2016). Marginal distribution clustering of multi-variate streaming IoT data. 466–471. 1 indexed citations

4.

Puschmann, Daniel, et al.. (2016). On the Effect of Adaptive and Nonadaptive Analysis of Time-Series Sensory Data. IEEE Internet of Things Journal. 3(6). 1084–1098. 33 indexed citations

5.

Puiu, Dan, Payam Barnaghi, Ralf Tönjes, et al.. (2016). CityPulse: Large Scale Data Analytics Framework for Smart Cities. IEEE Access. 4. 1086–1108. 170 indexed citations

6.

Puschmann, Daniel, Payam Barnaghi, & Rahim Tafazolli. (2016). Adaptive Clustering for Dynamic IoT Data Streams. IEEE Internet of Things Journal. 4(1). 64–74. 91 indexed citations

7.

Ganz, Frieder, Daniel Puschmann, Payam Barnaghi, & François Carrez. (2015). A Practical Evaluation of Information Processing and Abstraction Techniques for the Internet of Things. IEEE Internet of Things Journal. 2(4). 340–354. 87 indexed citations

8.

Bermúdez-Edo, María, et al.. (2014). A Knowledge-Based Approach for Real-Time IoT Data Stream Annotation and Processing. 215–222. 81 indexed citations

9.

Puschmann, Daniel, et al.. (2005). State of the art design of rigid-flex substrates: A manufacturer’s point of view. CERN Document Server (European Organization for Nuclear Research).

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.

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