Dr Frederic Stahl

1. Rettig, R., Becker, F., Berghoff, A., Binkele, T., Butter, W.M., Floehr, T., Kumm, M., Leluschko, C., Littau, F., Reinders, E. and Rodenbäck, E., Schmid, T., Schründer, S., Schweigert, S., Sinhuber, M., Wellhausen, J., Stahl, F., Tholen, C. (2025). Multi-Resolution Remote Sensing Dataset for the Detection of Anthropogenic Litter: A Multi-Platform and Multi-Sensor Approach. MDPI Data, 10(7), DOI: 10.3390/data10070113
2. Budimir, S., Fontaine, J. R. J., Huijts, N. M. A., Haans, A., IJsselsteijn, W. A., Oostveen, A. -M., Stahl, F., Heartfield, R., Loukas, G., Bezemskij, A., Filippoupolitis, A., Ras, I., & Roesch, E. B. (2024). We Are Not Equipped to Identify the First Signs of Cyber–Physical Attacks: Emotional Reactions to Cybersecurity Breaches on Domestic Internet of Things Devices. MDPI Applied Sciences, 14(24), article number 11855. DOI: 10.3390/app142411855
3. Lukats, D., Zielinski, O., Hahn, A., Stahl, F. (2024). A benchmark and survey of fully unsupervised concept drift detectors on real-world data streams. International Journal of Data Science and Analytics, pp. 1-31, ISSN: 2364-4168, DOI: 10.1007/s41060-024-00620-y
4. Idrees, M. M., Stahl, F., Badii, A., (2022) Adaptive Learning With Extreme Verification Latency in Non-Stationary Environments, IEEE Access, vol. 10, pp. 127345-127364, ISSN: 2169-3536, DOI: 10.1109/ACCESS.2022.3225225.
5. Ashlam, A. , Badii, A. , Stahl, F. (2022). ‘WebAppShield: An Approach Exploiting Machine Learning to Detect SQLi Attacks in an Application Layer in Run-Time’. World Academy of Science, Engineering and Technology, International Journal of Computer and Information Engineering, 16 (8), pp. 294 – 302, ISSN: 1307-6892
6. Almutairi, M., Stahl, F., Bramer, M., (2921) ReG-Rules: An Explainable Rule-Based Ensemble Learner for Classification, IEEE Access, 9, pp. 52015-52035, ISSN: 2169-3536, DOI 10.1109/ACCESS.2021.3062763
7. Stahl, F., Le, T., Badii, A., Gaber, M.M. (2021) A frequent pattern conjunction Heuristic for rule generation in data streams. Information 12(1) (2021), ISSN 2078-2489, doi: 10.3390/info12010024
8. Dubuc, T., Stahl, F. and Roesch, E.B., (2020) Mapping the Big Data Landscape: Technologies, Platforms and Paradigms for Real-Time Analytics of Data Streams. IEEE Access, 9, pp. 15351-15374, ISSN: 2169-3536,
   doi: 10.1109/ACCESS.2020.3046132.
9. Wolf, M., van den Berg, K., Garaba, S. P., Gnann, N., Sattler, K., Stahl, F. and Zielinski, O. (2020) Machine learning for aquatic plastic litter detection, classification and quantification (APLASTIC–Q). Environmental Research Letters, 15(11), ISSN 1748-9326, doi: 10.1088/1748-9326/abbd01
10. Stahl, F. and Badii, A. (2020) Building adaptive data mining models on streaming data in real-time. Expert Update, 20 (2). ISSN 1465-4091
11. Idrees, M. M., Minku, L. L., Stahl, F. and Badii, A. (2020) A heterogeneous online learning ensemble for non-stationary environments. Knowledge-Based Systems. ISSN 0950-7051 doi: https://doi.org/10.1016/j.knosys.2019.104983
12. Hammoodi, M. S., Stahl, F. and Badii, A., (2018) Real-time feature selection technique with concept drift detection using Adaptive Micro-Clusters for data stream mining. Knowledge-Based Systems, Elsevier, 75, pp. 205-239, ISSN 0950-7051 doi: 10.1016/j.knosys.2018.08.007
13. Tennant, M., Stahl, F., Rana, O. and Gomes,J.B., (2017) Scalable real-time classification of data streams with concept drift, Future Generation Computer Systems, Elsevier, 75, pp. 187-199, ISSN 0167-739X doi: 10.1016/j.future.2017.03.026
14. Le, T., Stahl, F., Gaber, M.M., Gomes, J.B., and Di Fatta,G., (2017) On expressiveness and uncertainty awareness in rule-based classification for data streams, Neurocomputing, Elsevier, 265, pp. 127-141, ISSN 0925-2312, doi: 10.1016/j.neucom.2017.05.081.
15. Hammoodi, M., Stahl, F., Tennant, M., and Badii, A., (2017) Towards Real-Time Feature Tracking Technique using Adaptive Micro-Clusters, SGAI, Expert Update (Special Issue on the 1st BCS SGAI Workshop on Data Stream Mining Techniques and Applications), 17 (1). ISSN 1465-4091.
16. Pavlopoulou, N., Abushwashi, A., Stahl, F. and Vittorio Scibetta (2017) A Text Mining Framework for Big Data, SGAI, Expert Update (Special Issue on the 1st BCS SGAI Workshop on Data Stream Mining Techniques and Applications), 17 (1). ISSN 1465-4091.
17. Adedoyin-Olowe, M., Gaber, M.M., Dancausa, C.M., Stahl, F., Gomes, J.B., (2016) A rule dynamics approach to event detection in Twitter with its application to sports and politics, Expert Systems with Applications, Elsevier, 55, pp. 351-360, ISSN 0957-4174, DOI 10.1016/j.eswa.2016.02.028
18. Stahl, F., May, D., Mills, H., Bramer, M., Gaber, M.M., (2015) A Scalable Expressive Ensemble Learning using Random Prism: A MapReduce Approach,  Transactions on large-scale data and knowledge-centered systems, Springer, 9070, pp. 90-107. ISBN 978-3-662-46702-2 DOI 10.1007/978-3-662-46703-9_4.
19. Adedoyin-Olowe, M., Gaber, M.M., Stahl, F. (2014) A Survey of Data Mining Techniques for Social Network Analysis, Journal of Data Mining and Digital Humanities, Episciences, 2014. ISSN 2416-5999
20. Zliobaite, I., Budka, M. and Stahl, F. (2015) Towards cost-sensitive adaptation: When is it worth updating your predictive model? Neurocomputing, Elsevier, 150 (A), pp. 240-249. ISSN 0925-2312 doi: 10.1016/j.neucom.2014.05.084.
21. Stahl, F. and Bramer, M. (2014) Random Prism: a noise‐tolerant alternative to Random Forests. Expert Systems, Wiley, 31(5), pp. 411–420, ISSN 1468-0394 doi: 10.1111/exsy.12032.
22. Gaber, M.M., Gama, J, Krishnaswamy, S, Gomes, J.B., Stahl, F. (2014) Data stream mining in ubiquitous environments: state‐of‐the‐art and current directions, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 4 (2), pp. 116-138, DOI: 10.1002/widm.1115.
23. Roesch, E., Stahl, F. and Gaber, M.M. (2014) Bigger data for Big Data: from Twitter to Brain-Computer Interface. Behavioral and Brain Sciences, Cambridge Journals, 37(1), pp. 97-98, ISSN 1469-1825 doi: 10.1017/S0140525X13001854.
24. Stahl, F., Gabrys, B., Gaber, M. M, and Berendsen, M. (2013) An overview of interactive visual data mining techniques for knowledge discovery. WIREs: Data Mining and Knowledge Discovery, Wiley, 3 (4). pp. 239-256. ISSN 1942-4795 doi: 10.1002/widm.1093
25. Stahl, F. and Bramer, M. (2012) Computationally efficient induction of classification rules with the PMCRI and J-PMCRI frameworks. Knowledge-Based Systems, Elsevier, 35. pp. 49-63. ISSN 0950-7051 doi: 10.1016/j.knosys.2012.04.014
26. Stahl, F. and Jordanov, I. (2012) An overview of the use of neural networks for data mining tasks. WIREs: Data Mining and Knowledge Discovery, Wiley, 2 (3). pp. 193-208. ISSN 1942-4795 doi: 10.1002/widm.1052
27. Stahl, F., Gaber, M. M., Aldridge, P., May, D., Liu, H., Bramer, M. and Yu, P. S. (2012) Homogeneous and heterogeneous distributed classification for pocket data mining. In: Hameurlain, A., Küng, J. and Wagner, R. (eds.) Transactions on large-scale data and knowledge-centered systems V. Lecture Notes in Computer Science (7100). Springer, pp. 183-205. ISBN 9783642281471
28. Stahl, F. and Bramer, M. (2012) Jmax-pruning: a facility for the information theoretic pruning of modular classification rules.Knowledge-Based Systems, Elsevier, 29. pp. 12-19. ISSN 0950-7051 doi: 10.1016/j.knosys.2011.06.016
29. Stahl, F. and Bramer, M. (2012) Scaling up classification rule induction through parallel processing. Knowledge Engineering Review, Cambridge Journals, ISSN 1469-8005 doi: 10.1017/S0269888912000355 
30. Swain, M., Silva, C. G., Loureiro-Ferreira, N., Ostropytskyy, V., Brito, J., Riche, O., Stahl, F., Dubitzky, W. and Brito, R. M. M.(2010) P-found: grid-enabling distributed repositories of protein folding and unfolding simulations for data mining. Future Generation Computer Systems, Elsevier, 26 (3). pp. 424-433. ISSN 0167-739X doi: 10.1016/j.future.2009.08.008
31. Berrar, D., Stahl, F., Silva, C., Rodrigues, J.R., Brito, R.M.M. and Dubitzky, W. (2005) Towards data warehousing and mining of protein unfolding simulation data.  Journal of clinical monitoring and computing, Springer, 19 (4-5). pp. 307-317. ISSN 1573-2614 doi: 10.1007/s10877-005-0676-z

1. Stahl, F., Nolle, L., Kumm, M., Tholen, C. (2025) On the Optimisation of Machine Learning Models for Predicting the Photosynthetically Available Radiation in the Water Column, ECMS 2024 Proceedings Edited By: Scarpa, M., Cavalieri, S., Serrano, S., De Vita, F., European Council for Modeling and Simulation, pp. 531-537

2. Kumm, M., Tholen, C., Nolle, L., Stahl, F., (2025) On the Location-Independent Reconstruction of Photosynthetically Active Radiation in the Water Column Using Neural Networks, ECMS 2024 Proceedings Edited By: Scarpa, M., Cavalieri, S., Serrano, S., De Vita, F., European Council for Modeling and Simulation, pp. 538-544

3. Elsayed, A. H., El-Mihoub, T. A., Manß, C., Miedtank, A., Nolle, L., Stahl, F. (2024) Interactive Simulator Framework for XAI Applications in Aquatic Environments, In International Conference on Innovative Techniques and Applications of Artificial Intelligence pp. 144-157, Cambridge, Springer LNCS. DOI: 10.1007/978-3-031-77915-2_11
4. Tholen, C., Rodenbäck, E., Nolle, L., Rettig, R., Stahl, F. (2024) On the Development of a Pixel-wise Plastic Waste Identification System for Multispectral Remote Sensing Applications, In International Conference on Innovative Techniques and Applications of Artificial Intelligence pp. 47-60, Cambridge, Springer LNCS. DOI: 10.1007/978-3-031-77915-2_4
5. Schneider, J., Lukats, D., Berghöfer, E., Paulenz, I., Nolle, L., Stahl, F., Wollschläger, J., (2024), On the Importance of Domain Knowledge for Real-Time Event Detection, In: Proceedings of OCEANS 2024. OCEANS MTS/IEEE Conference (OCEANS-2024), Singapore, IEEE, DOI: 10.1109/OCEANS51537.2024.10752608
6. Theodorakopoulos, D., Stahl, F., Lindauer, M. (2024) Hyperparameter Importance Analysis for Multi-Objective AutoML, In European Conference on Artificial Intelligence (ECAI-2024) pp. 1100-1107. Santiago de Compostela, Spain, IOS Press, Frontiers in Artificial Intelligence and Applications, ISBN: 978-1-64368-548-9, DOI 10.3233/FAIA240602
7. Tholen, C., Nolle, L., Wollschläger, J., Stahl, F., O. (2024) Model Generalisation for Predicting the Amount of Photosynthetically Available Radiation in the Water Column from Freefall Profiler Observations, ECMS 2024 Proceedings Edited By: Grzonka, D., Rylo, N., Suchacka, G., Mityushev, V., European Council for Modeling and Simulation, ISBN: 978-3-937436-84-5, DOI 10.7148/2022
8. El-Mihoub, T. A. , El Gadi, A., Nolle, L., Stahl, F. (2024) On Object Detection and Explainability with Sonar Imagery, 2024 IEEE 4th International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), Tripoli, Libya DOI: 10.1109/MI-STA61267.2024.10599670
9. Lejman, A., Rüssmeier, N., Ferdinand, O., Stahl, F. (2024) Eine integrierte Datenstromverarbeitung zur Erfassung von Umweltlagebildern, In 22. GMA/ITG-Fachtagung Sensoren und Messsysteme 2024, pp. 499-504, Nürnberg, Germany, AMA Publications, ISBN: 978-3-910600-01-0, DOI: 10.5162/sensoren2024/P20
10. Nolle, L., Stahl, F., & El-Mihoub, T. (2023). On Explanations for Hybrid Artificial Intelligence. In International Conference on Innovative Techniques and Applications of Artificial Intelligence (pp. 3-15). Cambridge, Springer LNCS. DOI 10.1007/978-3-031-47994-6_1
11. Ferdinand, O., Rüssmeier, N., Lejman, A., Günther, M., Kammler, F., Stahl, F., Zielinski, O. (2023), Comprehensive Perception in Marine Environments for Dynamic Anchoring, In: Proceedings of OCEANS 2023. OCEANS MTS/IEEE Conference (OCEANS-2023), ISBN: 979-8-3503-3226-1
12. Lukats, D., & Stahl, F. (2023). On Reproducible Implementations in Unsupervised Concept Drift Detection Algorithms Research. In International Conference on Innovative Techniques and Applications of Artificial Intelligence (pp. 204-209). Cambridge, Springer LNCS. DOI 10.1007/978-3-031-47994-6_16
13. Ashlam, A. A., Badii, A., Stahl, F. (2023). Data-Mining and Hashing to Prevent Application-Layer DDoS and SQL Injection Attacks. In International Conference on Advanced Systems and Emergent Technologies (IC_ASET), IEEE, pp. 1-6, doi: 10.1109/IC_ASET58101.2023.10150694.
14. Theodorakopoulos, D., Manss, C., Stahl, F., & Lindauer, M. (2023). Green AutoML for Plastic Litter Detection, ICLR 2023 Workshop on Tackling Climate Change with Machine Learning.
15. Paulenz, I., Lukats, D., Schneider, J., Berghöfer, E., Stahl, F. T., Nolle, L., & Zielinski, O. (2023). Anforderungsanalyse für ein System zur automatisierten Ereignisdetektion in marinen Umgebungen. In Umweltinformationssysteme–Vielfalt, Offenheit, Komplexität: Tagungsband des 29. Workshops “Umweltinformationssysteme (UIS 2022)“des Arbeitskreises „Umweltinformationssysteme “der Fachgruppe „Informatik im Umweltschutz ‘‘der Gesellschaft für Informatik eV (GI), Wilhelmshaven, Germany, Springer, Pages 149-165, ISBN 978-3-658-39795-1, DOI 10.1007/978-3-658-39796-8_10.
16. Ashlam, A., Badii, A., Stahl, F., (2022). Multi-Phase Algorithmic Framework to Prevent SQL Injection Attacks using Improved Machine learning and Deep learning to Enhance Database security in Real-time. Proceedings of the 15th International Conference on Security of Information and Networks (SIN-2022). Sousse, Tunisia, Pages 1-4, IEEE Xplore. pp. 1-4, ISBN 978-1-6654-5465-0, DOI 10.1109/SIN56466.2022.9970504.
17. Ashlam, A. A., Badii, A., Stahl, F. (2022). A Novel Approach Exploiting Machine Learning to Detect SQL-I Attacks. In 2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET), IEEE, pp. 513-517. ISBN: 978-1-6654-2735-7, DOI 10.1109/IC_ASET53395.2022.9765948
18. Almutairi, M., L., Stahl, F., Bramer, M. (2022) CRC: Consolidated Rules Construction for Expressive Ensemble Classification. Proceedings of the Fourty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, Springer LNCS.
19. El-Mihoub, T., Nolle, L., Stahl, F. (2022) Explainable Boosting Machines for Network Intrusion Detection with Features Reduction. Proceedings of the Fourty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, Springer LNCS.
20. Kumm, M., Nolle, L., Stahl, F., Jemai, A., Zielinski, O. (2022) On an Artificial Neural Network Approach for Predicting Photosynthetically Active Radiation in the Water Column. Proceedings of the Fourty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, Springer LNCS.
21. Stahl, F., Nolle, L., Jemai, A., & Zielinski, O. (2022) A Model for Predicting the Amount of Photosynthetically Available Radiation from BGC-ARGO Float Observations in the Water Column, ECMS 2022 Proceedings Edited By: Ibrahim A. Hameed, Agus Hasan, Saleh Abdel-Afou Alaliyat European Council for Modeling and Simulation, ISBN: 978-3-937436-77-7, DOI 10.7148/2022.
22. Stahl, F., Ferdinand, O., Nolle, L., Pehlken, A., Zielinski, O., (2021), AI enabled Bio Waste Contamination-Scanner. Proceedings of the Fourty-first SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, Springer LNCS, pp. 357-363. ISBN: 978-3-030-91100-3, DOI 10.1007/978-3-030-91100-3_28.
23. Lukats, D., Berghöfer, E., Stahl, F., Schneider, J., Pieck, D., Idrees, M.M., Nolle, L., Zielinski, O. (2021), Towards Concept Change Detection in Marine Ecosystems, In: IEEE Journal of Oceanic Engineering (OES) OCEANS 2021 San Diego – Porto Online Proceedings. OCEANS MTS/IEEE Conference (OCEANS-2021), pp. 1-10. ISBN: 978-1-6654-2788-3, DOI 10.23919/OCEANS44145.2021.9706015.
24. Prakash, N., Stahl, F., Mueller, C.L., Ferdinand, O., Zielinski, O., (2021), Intelligent Marine Pollution Analysis on Spectral Data. In: IEEE Journal of Oceanic Engineering (OES) OCEANS 2021 San Diego – Porto Online Proceedings. OCEANS MTS/IEEE Conference (OCEANS-2021), pp. 1-6. ISBN: 978-1-6654-2788-3, DOI 10.23919/OCEANS44145.2021.9706056.
25. Alzubi, S., Stahl, F., Gaber, M.M. (2021) Towards Intrusion Detection Of Previously Unknown Network Attacks, ECMS 2021 Proceedings Edited By: Khalid Al-Begain, Mauro Iacono, Lelio Campanile, Andrzej Bargiela European Council for Modeling and Simulation, ISBN: 978-3-937436-72-2, doi: 10.7148/2021-0035.
26. Wrench, C., Stahl, F., Di Fatta, G., Karthikeyan, V., Nauck, D. (2019) A rule induction approach to forecasting critical alarms in a telecommunication network. In: 2019 IEEE International Conference on Data Mining Workshops (ICDMW), 8-11 Nov 2019, Beijing, China.
27. Almutairi, M., Stahl, F. and Bramer, M. (2018), A rule-based classifier with accurate and fast rule term induction for continuous attributes, In Proceedings of 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, Florida, USA. IEEE, pp. 413-420. ISBN 978-1-5386-6805-4 DOI 10.1109/ICMLA.2018.00068.
28. Almutairi, M., Stahl and Bramer, M. (2017) Improving Modular Classification Rule Induction with G-Prism Using Dynamic Rule Term Boundaries. Proceedings of the Thirty-Seventh SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, Springer LNCS, pp 115-128. ISBN 978-3-319-71078-5 DOI 10.1007/978-3-319-71078-5_9.
29. Le, T., Stahl, F., Wrench, C. and Gaber, M. M. (2016),  A Statistical Learning Method to Fast Generalised Rule Induction Directly from Raw Measurements, In Proceedings of 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, California, USA. IEEE, pp. 935-938. ISBN 978-1-5090-6167-9 DOI 10.1109/ICMLA.2016.0168.
30. Wrench, C., Stahl, F., Le, T., Di Fatta, G., Karthikeyan, V. and Nauck, D. (2016) A method of rule induction for predicting and describing future alarms in a telecommunication network, In Proceedings of the Thirty-Sixth SGAI International Conference on Artificial Intelligence, December, Cambridge. Springer, pp. 309-323. ISBN 978-3-319-47175-4, DOI 10.1007/978-3-319-47175-4_23.
31. Almutairi, M., Stahl, F., Jennings, M., Le, T. and Bramer, M. (2016) Towards expressive modular rule induction for numerical attributes, Proceedings of the Thirty-Sixth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, Springer, pp 229-235. ISBN 9978-3-319-47175-4 DOI 10.1007/978-3-319-47175-4_16.
32. Hammoodi, M., Stahl, F. and Tennant, M. (2016), Towards online concept drift detection with feature selection for data stream classification, In Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI), The Hague, Holland, pp. 1549-1550, IOS Press. DOI 10.3233/978-1-61499-672-9-1549.
33. Wrench, C., Stahl, F., Di Fatta, G., Karthikeyan, V., Nauck, D. (2015), Towards Expressive Rule Induction on IP Network Event Streams, In Proceedings of the Thirty-Fifth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge. Springer, pp 191-196. ISBN978-3-319-25030 DOI 10.1007/978-3-319-25032-8_14.
34. Tennant, M., Stahl, F., and Gomes, J.B. (2015), Fast Adaptive Real-Time Classification for Data Streams with Concept Drift, Proceedings of 8th International Conference on Internet and Distributed Computing Systems, Windsor, England, Springer LNCS, pp 265-272. ISBN 978-3-319-23236-2 DOI 10.1007/978-3-319-23237-9_23.
35. Ghamdi, S.A., Di Fatta, G., and Stahl, F. (2015) Optimisation Techniques for Parallel K-Means on MapReduce, Proceedings of 8th International Conference on Internet and Distributed Computing Systems, Windsor, England, Springer LNCS, pp. 193-200. ISBN 978-3-319-23236-2 DOI 10.1007/978-3-319-23237-9_17.
36. Adedoyin-Olowe M., Gaber M. M., Martn-Dancausa C., and Stahl F. (2014), Extraction of Unexpected Rules from Twitter Hashtags and its Application to Sport Events, Proceedings of 13th International Conference on Machine Learning and Applications 2014, Detroit, MI USA on December 3-6, IEEE press, pp 207-212. doi:  ISBN 10.1109/ICMLA.2014.38.
37. Le, T., Stahl, F., Gomes, J.B., Gaber, M.M., and Di Fatta, G. (2014) Computationally Efficient Rule-Based Classification for Continuous Streaming Data. In Proceedings of the Thirty-Fourth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge. Springer, pp 21-34. ISBN 978-3-319-12068-3 doi: 10.1007/978-3-319-12069-0_2.
38. Tennant, M., Stahl, F., Di Fatta, G. and Gomes, J.B. (2014) Towards a Parallel Computationally Efficient Approach to Scaling up Data Stream Classification.  In Proceedings of the Thirty-Fourth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge.  Springer, pp 51-65. ISBN 978-3-319-12068-3 doi: 10.1007/978-3-319-12069-0_4.
39. Rausch, P., Stahl, F. and Stumpf, M. (2013) Efficient Interactive Budget Planning and Adjusting Under Financial Stress.  In Proceedings of the Thirty-Third SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge.  Springer,  pp 375-388. ISBN 978-3-319-02620-6 doi: 10.1007/978-3-319-02621-3_28.
40. Gomes, J.B., Adedoyin-Olowe, M., Gaber, M.M. and Stahl, F. (2013) Rule Type Identification using TRCM for Trend Analysis in Twitter.  In Proceedings of the Thirty-Third SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge.  Springer, pp 273-278. ISBN 978-3-319-02620-6 doi: 10.1007/978-3-319-02621-3_20.
41. Adedoyin-Olowe, M., Gaber, M.M. and Stahl, F. (2013) TRCM: a methodology for temporal analysis of evolving concepts in Twitter. In Procedings of the Twelfth ICAISC International Conference on Artificial Intelligence and Soft Computing, Zakopane, Poland. Springer, pp. 135-145. ISBN 978-3-642-38609-1 doi: 10.1007/978-3-642-38610-7_13.
42. Stahl, F., May, D. and Bramer, M. (2012) Parallel random prism: a computationally efficient ensemble learner for classification.  In Proceedings of the Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge.  Springer, pp. 21-34. ISBN 9781447147381 doi: 10.1007/978-1-4471-4739-8_2
43. Stahl, F., Gaber, M. M. and Salvador, M. M. (2012) eRules: a modular adaptive classification rule learning algorithm for data streams. In Proceedings of The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge. Springer, pp. 65-78. ISBN 9781447147381 doi: 10.1007/978-1-4471-4739-8_5
44. Stahl, F. and Bramer, M. (2011) Random prism: an alternative to random forests. In Proceedings of the Thirty-first SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, Springer, pp. 5-18. ISBN 9781447123170 doi:10.1007/978-1-4471-2318-7_1
45. Stahl, F., Gaber, M. M., Bramer, M. and Yu, P. S. (2011) Distributed hoeffding trees for pocket data mining. In Proceedings of the International Conferance on High Performance Computing and Simulation (HPCS),  Istanbul. IEEE, pp. 686-692. ISBN 9781612843803 doi:10.1109/HPCSim.2011.5999893
46. Stahl, F., Gaber, M. M., Liu, H., Bramer, M. and Yu, P. S. (2011) Distributed classification for pocket data mining.  In Proceedings of the Nineteenth International Symposium on Methodologies for Intelligent Systems (ISMIS), Warsaw. Lecture Notes in Computer Science (6804). Springer, pp. 336-345. ISBN 9783642219153 doi: 10.1007/978-3-642-21916-0_37
47. Stahl, F. and Bramer, M. (2011) Induction of modular classification rules: using Jmax-pruning. In Proceedings of the Thirtieth International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge. Springer, pp. 79-92. ISBN 9780857291295 doi: 10.1007/978-0-85729-130-1_6
48. Stahl, F., Gaber, M. M., Bramer, M. and Yu, P. S. (2010) Pocket data mining: towards collaborative data mining in mobile computing environments. In Proceedings of the Twenty-second IEEE Int. Conf. on Tools with Artificial Intelligence. IEEE, Arras. pp. 323-330. ISBN 9781424488179 doi: 10.1109/ICTAI.2010.118
49. Stahl, F., Bramer, M. and Adda, M. (2010) J-PMCRI: a methodology for inducing pre-pruned modular classification rules. In Proceedings of the Twenty-first IFIP World Computer Congress, Brisbane. Springer, pp. 47-56. ISBN 9783642152856 doi: 10.1007/978-3-642-15286-3_5
50. Stahl, F., Bramer, M. and Adda, M. (2010) Parallel rule induction with information theoretic pre-pruning. In Proceedings of the Twenty-ninth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge. Springer, pp. 151-164. ISBN 9781848829824 doi: 10.1007/978-1-84882-983-1_11
51. Stahl, F., Bramer, M. and Adda, M. (2009) PMCRI: a parallel modular classification rule induction framework. In Proceedings of the Sixth International Conference on Machine Learning and Data Mining in Pattern Recognition, Springer, Lecture Notes in Computer Science (5632), pp. 148-162. ISBN 9783642030697 doi: 10.1007/978-3-642-03070-3_12
52. Swain, M., Ostropytskyy, V., Silva, C. G., Stahl, F., Riche, O., Brito, R. M. M. and Dubitzky, W. (2008) Grid computing solutions for distributed repositories of protein folding and unfolding simulations. In Proceedings of the International Conference on Computational Science 2008. Lecture Notes in Computer Science (5103), Kraków. Springer, pp. 70-79. ISBN 9783540693888 doi:10.1007/978-3-540-69389-5_10
53. Stahl, F., Bramer, M. and Adda, M. (2009) Parallel induction of modular classification rules. In Proceedings of the Twenty-eighth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge. Springer, . ISBN 9781848821705 doi: 10.1007/978-1-84882-171-2_25
54. Stahl, F., Bramer, M. and Adda, M. (2008) P-Prism: a computationally efficient approach to scaling up classification rule induction. In Proceedings of the Twentieth IFIP World Computer Congress, Milan. Springer, pp. 77-86. ISBN 9780387096940 doi: 10.1007/978-0-387-09695-7_8
55. Stahl, F. and Bramer, M. (2008) Towards a computationally efficient approach to modular classification rule induction. In Proceedings of the Twenty-seventh SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge. Springer, pp. 357-362. ISBN 9781848000933 doi: 10.1007/978-1-84800-094-0_27
56. Stahl, F., Berrar, D., Silva, C., Rodrigues, R., Brito, R.M.M. and Dubitzky, W. (2005) Grid warehousing of molecular dynamics protein unfolding data. In Proceedings of the Fifth International Symposium on Cluster Computing and the Grid, Cardiff. IEEE, pp. 496-503. ISBN 978-3-319-02710-4 doi: 10.1109/CCGRID.2005.1558594

Book Chapters

1. Wrench, C., Stahl, F., Di Fatta, G., Karthikeyan, V. and Nauck, D. D. (2016) Data stream mining of event and complex event streams: a survey of existing and future technologies and applications in big data. In: Atzmueller, M., Oussena, S. and Roth-Berghofer, T. (eds.) Enterprise Big Data Engineering, Analytics, and Management. IGI Global, pp. 24-47. ISBN 9781522502937 DOI: 10.4018/978-1-5225-0293-7.
2. Adedoyin-Olowe, M., Gaber, M.M., Stahl, F. and Gomes, J.B. (2015) Autonomic Discovery of News Evolvement in Twitter, In: Hassanien, A.E., Taher Azar, A., Snasel, V., Kacprzyk, J., Abawajy, J.H. (eds.) Big Data in Complex Systems: Challenges and Opportunities, Studies in Big Data, Springer, pp. 205-229. ISBN 978-3-319-11055-4 DOI 10.1007/978-3-319-11056-1_7.
3. Gaber, M. M., Stahl, F., Gomes, J.B.  (2014) Introduction,  In Pocket Data Mining, Big Data on Small Devices, Studies in Big Data, 2, Springer, pp. 1-5. ISBN 978-3-319-02710-4 DOI 10.1007/978-3-319-02711-1_1.
4. Gaber, M. M., Stahl, F., Gomes, J.B.  (2014) Background,  In Pocket Data Mining, Big Data on Small Devices, Studies in Big Data, 2, Springer, pp. 7-21. ISBN  978-3-319-02710-4 DOI 10.1007/978-3-319-02711-1_2.
5. Gaber, M. M., Stahl, F., Gomes, J.B.  (2014) Pocket Data Mining Framework,  In Pocket Data Mining, Big Data on Small Devices, Studies in Big Data, 2, Springer, pp. 23-40. ISBN  978-3-319-02710-4 DOI 10.1007/978-3-319-02711-1_3.
6. Gaber, M. M., Stahl, F., Gomes, J.B.  (2014) Implementation of Pocket Data Mining,  In Pocket Data Mining, Big Data on Small Devices, Studies in Big Data, 2, Springer, pp. 41-59. ISBN  978-3-319-02710-4 DOI 10.1007/978-3-319-02711-1_4.
7. Gaber, M. M., Stahl, F., Gomes, J.B.  (2014) Context-Aware PDM (Coll-Stream),  In Pocket Data Mining, Big Data on Small Devices, Studies in Big Data, 2, Springer, pp. 61-68. DOI 10.1007/978-3-319-02711-1_5.
8. Gaber, M. M., Stahl, F., Gomes, J.B.  (2014) Experimental Validation of Context-Aware PDM,  In Pocket Data Mining, Big Data on Small Devices, Studies in Big Data, 2, Springer, pp. 69-80. ISBN  978-3-319-02710-4 DOI 10.1007/978-3-319-02711-1_6.
9. Gaber, M. M., Stahl, F., Gomes, J.B.  (2014) Potential Applications of Pocket Data Mining,  In Pocket Data Mining, Big Data on Small Devices, Studies in Big Data, 2, Springer, pp. 81-94. ISBN  978-3-319-02710-4 DOI 10.1007/978-3-319-02711-1_7.
10. Gaber, M. M., Stahl, F., Gomes, J.B.  (2014) Conclusions, Discussion and Future Work,  In Pocket Data Mining, Big Data on Small Devices, Studies in Big Data, 2, Springer, pp. 95-98. ISBN  978-3-319-02710-4 DOI 10.1007/978-3-319-02711-1_8.
11. Stahl, F., Gaber, M. M., Bramer, M.  (2013) Scaling up Data Mining Techniques to Large Datasets Using Parallel and Distributed Processing, In: Rausch, P. Sheta, A. F. and Ayesh, A. (eds.) Business Intelligence and Performance Management. Advanced Information and Knowledge Processing. Springer London, pp. 243-259. ISBN 9781447148654 doi: 10.1007/978-1-4471-4866-1_16

1. Stahl, F. (2009). Parallel Rule Induction. Doctoral dissertation, Portsmouth University.
2. Stahl,F.(2006). System Architecture  for Distributed Data Mining on Data  Warehouses of Molecular Dynamics Simulation Data. Dissertation for the degree Diplomingennieur (FH), University of Applied Science Weihenstephan.