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Publications

Books

Gaber, M.M., Stahl, F. and Gomes, J.B. (2014). Pocket Data Mining, Big Data on Small Devices, Studies in Big Data, 2, Springer.

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Di Fatta, G., Fortino, G., Li, W., Pathan, M., Stahl, F., Guerrieri, A. (Eds.) (2015). Internet and Distributed Computing Systems, 8th International Conference, IDCS 2015, Windsor, UK, September 2-4, 2015. Proceedings, Springer.

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Journal Papers

  1. 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
  2. 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.
  3. 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.
  4. 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.
  5. 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
  6. 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.
  7. 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
  8. 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.
  9. Stahl, F. and Bramer, M. (2014) Random Prism: a noise‐tolerant alternative to Random Forests. Expert Systems, Wiley, 31(5), pp. 411420, ISSN 1468-0394 doi: 10.1111/exsy.12032.
  10. 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.
  11. 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.
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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 
  18. 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
  19. 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

Conference Papers

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. Stahl, F. and Bramer, M. (2008) Towards a computationally efficient approach to modular classification rule inductionIn 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
  28. 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

Theses

  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.
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