GIRAFFE.LU


Scientific Papers


  • 69 Despotovic, V., Skovranek, T., & Schommer, C. (2020). Speech Based Estimation of Parkinson’s Disease Using Gaussian Processes and Automatic Relevance Determination. Neurocomputing, 401, 173-181.
  • 68. Schommer, C. (2020, March 28). Eine Doktorarbeit zu beginnen, ist (relativ) leicht. Luxemburger Wort, 10.
  • 67. Sharma, R., Schommer, C., & Vivarelli, N. (2020). Building up Explainability in Multi-layer Perceptrons for Credit Risk Modeling. In R., Sharma, Building up Explainability in Multi-layer Perceptrons for Credit Risk Modeling (pp. 2).
  • 66. Sirajzade, J., Gierschek, D., & Schommer, C. (2020). An Annotation Framework for Luxembourgish Sentiment Analysis. In L., Besacier, S., Sakti, C., Soria, & D., Beermann (Eds.), Proceedings of the LREC 2020 1st Joint SLTU and CCURL Workshop (SLTU-CCURL 2020) (pp. 172-176). Paris, France: European Language Resources Association (ELRA).
  • 65. Sirajzade, J., Gierschek, D., & Schommer, C. (2020). Component Analysis of Adjectives in Luxembourgish for Detecting Sentiments. In D., Beermann, L., Besacier, S., Sakti, & C., Soria (Eds.), Proceedings of the LREC 2020 1st Joint SLTU and CCURL Workshop(SLTU-CCURL 2020) (pp. 159-166). Paris, France: European Language Resources Association (ELRA).
  • 64. Gierschek, D., Gilles, P., Purschke, C., Schommer, C., & Sirajzade, J. (2019). A Temporal Warehouse for Modern Luxembourgish Text Collections. Paper presented at DHBeNeLux 2019, Liège, Belgium.
  • 63. Guo, S., Höhn, S., & Schommer, C. (2019). A Personalized Sentiment Model with Textual and Contextual Information. The SIGNLL Conference on Computational Natural Language Learning, Hong Kong 3-4 November 2019.
  • 62. Guo, S., Höhn, S., & Schommer, C. (2019). Topic-based Historical Information Selection for Personalized Sentiment Analysis. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges 24-26 April 2019.
  • 61. Guo, S., Höhn, S., & Schommer, C. (2019). Looking into the Past: Evaluating the Effect of Time Gaps in a Personalized Sentiment Model. ACM/SIGAPP Symposium On Applied Computing, Limassol 8-12 April 2019.
  • 60. Guo, S., Höhn, S., Xu, F., & Schommer, C. (2019). Personalized Sentiment Analysis and a Framework with Attention-Based Hawkes Process Model. Agents and Artificial Intelligence. Springer.
  • 59. Sirajzade, J., & Schommer, C. (2019). The LuNa Open Toolbox for the Luxembourgish Language. In P., Perner (Ed.), Advances in Data Mining, Applications and Theoretical Aspects, Poster Proceedings 2019. Leipzig, Germany: ibai publishing.
  • 58. Bouvry, P., Bisdorff, R., Schommer, C., Sorger, U., Theobald, M., & van der Torre, L. (2018). Proceedings - 2017 ILILAS Distinguished Lectures. Luxembourg, Luxembourg: University of Luxembourg.
  • 57. Bustan S, Gonzalez-Roldan AM, Schommer, C., Kamping S, Löffler M, Brunner M, Flor H, & Anton, F. (2018, July). Psychological, cognitive factors and contextual influences in pain and pain-related suffering as revealed by a combined qualitative and quantitative assessment approach. PLoS ONE.
  • 56. Guo, S., Höhn, S., Xu, F., & Schommer, C. (2018). PERSEUS: A Personalization Framework for Sentiment Categorization with Recurrent Neural Network. International Conference on Agents and Artificial Intelligence , Funchal 16-18 January 2018 (pp. 9).
  • 55. Guo, S., & Schommer, C. (2018, September 10). A Bilingual Study for Personalized Sentiment Model PERSEUS. Paper presented at PhD Forum at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Dublin, Ireland.
  • 54. Kamlovskaya, E., Schommer, C., & Sirajzade, J. (2018). A Dynamic Associative Memory for Distant Reading. International Conference on Artificial Intelligence Humanities, Book of Abstracts. Seoul, Korea: Chung-Ang University.
  • 53. Sirajzade, J., & Schommer, C. (2018). Mind and Language. AI in an Example of Similar Patterns of Luxembourgish Language. International Conference on Artificial Intelligence Humanities, Book of Abstracts (pp. 2). Seoul, Korea: Chung-Ang University.
  • 52. Vijayakumar, B., Höhn, S., & Schommer, C. (2018). Quizbot: Exploring Formative Feedback with Conversational Interfaces. In B., Vijayakumar, S., Höhn, & C., Schommer, Proceedings of the. Springer.
  • 51. Zheng, Y., Guo, S., & Schommer, C. (2018). An Approach to Incorporate Emotions in a Chatbot with Seq2Seq Model. Benelux Conference on Artificial Intelligence, ‘s-Hertogenbosch 8-9 November 2018.
  • 50. Guo, S., & Schommer, C. (2017). Embedding of the Personalized Sentiment Engine PERSEUS in an Artificial Companion. International Conference on Companion Technology, Ulm 11-13 September 2017. IEEE.
  • 49. Höhn, W., & Schommer, C. (2017). Georeferencing of Place Markers in Digitized Early Maps by Using Similar Maps as Data Source. Digital Humanities 2017: Conference Abstracts.
  • 48. Höhn, W., & Schommer, C. (2017). RAT 2.0. Digital Humanities 2017: Conference Abstracts.
  • 47. Schommer, C. (2017, January 27). Q&A with Data Scientists: Christopher Schommer. Operational Database Management Systems.
  • 46. van Dijk, T., & Schommer, C. (Eds.). (2017). Proceedings of the 2nd International Workshop on Exploring Old Maps (2nd). Würzburg, Germany: University of Würzburg.
  • 45. Bustan, S., Gonzalez-Roldan, A. M., Schommer, C., Kamping, S., Loeffler, M., Brunner, M., Flor, H., & Anton, F. (2016, November). Facteurs psychologiques, cognitifs et les influences contextuelles dans la douleur et la souffrance liée à la douleur. Poster session presented at 16e congrès national de la société française d'étude et de traitement de la douleur (SFETD), Bordeaux, France.
  • 44. Höhn, W., & Schommer, C. (2016, July). Annotating and Georeferencing of Digitized Early Maps. Poster session presented at Digital Humanities 2016.
  • 43. Höhn, W., & Schommer, C. (2016, June). RAT: A Referencing and Annotation Tool for Digitized Early Maps. Paper presented at DHBenelux Cconference 2016, Luxembourg.
  • 42. van Dijk, T., & Schommer, C. (Eds.). (2016). Proceedings International Workshop Exploring Old Maps 2016. Luxembourg, Luxembourg: University of Luxembourg.
  • 41. Höhn, S., Busemann, S., Max, C., Schommer, C., & Ziegler, G. (2015, September). Interaction Profiles for an Artificial Conversational Companion. Paper presented at International Symposium on Companion Technology, Ulm, Germany.
  • 40. Bouleau, F., & Schommer, C. (2014). Finding Outliers in Satellite Patterns by Learning Pattern Identities. In J., Filipe & A., Fred (Eds.), Proceedings "6th International Conference on Agents an Artificial Intelligence".
  • 39. Kampas, D., Schommer, C., & Sorger, U. (2014). A Hidden Markov Model to detect relevance in financial documents based on on/off topics. European Conference on Data Analysis.
  • 38. Schommer, C. (2014). Sentiment Barometer in Financial News (-). Luxembourg, Luxembourg: Internal Report.
  • 37. Bouleau, F., & Schommer, C. (2013). Outlier Identification in Spacecraft Monitoring Data using Curve Fitting Information. Proceedings ECDA.
  • 36. Danilava, S., Busemann, S., Schommer, C., & Ziegler, G. (2013). Towards Computational Models for a Long-term Interaction with an Artificial Conversational Companion. Proceedings "5th International Conference on Agents an Artificial Intelligence" (pp. 241-248).
  • 35. Danilava, S., Busemann, S., Schommer, C., & Ziegler, G. (2013). Why are you Silent? - Towards Responsiveness in Chatbots. Avec le Temps! Time, Tempo, and Turns in Human-Computer Interaction". Workshop at CHI 2013, Paris, France.
  • 34. Kampas, D., & Schommer, C. (2013). A Hybrid Classification System to find News that is relevant. ECDA 2013.
  • 33. Minev, M., & Schommer, C. (2013). News Representation with Multi-Word Features. Proceedings ECDA.
  • 32. Minev, M., & Schommer, C. (2013). Domain-driven news representation using conditional attribute-value pairs. In N., 31. Ferro (Ed.), PROMISE Winter School 2013: Bridging between Information Retrieval and Databases. Springer Publishing.
  • 30. Schommer, C., Kampas, D., & Bersan, R. (2013). A Prospect on How to Find the Polarity of a Financial News by Keeping an Objective Standpoint. Proceedings ICAART 2013.
  • 29. Schommer, C., Minev, M., Grammatikos, T., & Schaefer, U. (2013). Feature Extraction and Representation for Economic Surveys. Poster session presented at Bridging between Information Retrieval and Databases.
  • 28. Danilava, S., Busemann, S., & Schommer, C. (2012). Artificial Conversational Companions A Requirements Analysis. Proceedings "4th International Conference on Agents and Artificial Intelligence" (pp. 282-289). SciTePress 2012.
  • 27. Danilava, S., Schommer, C., & Ziegler, G. (2012). Long-term Human-machine Interaction: Organisation and Adaptability of Talk-in-interaction. CHIST-ERA Conference, Edinburgh, Scotland.
  • 26. Minev, M., Schommer, C., & Grammatikos, T. (2012). News and stock markets: A survey on abnormal returns and prediction models. Technical Report, UL.
  • 25. Poray, J., & Schommer, C. (2012). Operations on Conversational Mind-Graphs. Proceedings "4th International Conference on Agents and Artificial Intelligence" (pp. 511-514). SciTePress.
  • 24. Kaufmann, S., & Schommer, C. (2010). Towards E-Conviviality in Web-Based Systems by considering the Wisdom of Crowds. Abstract book of 2nd Conference on Agents and Artificial Intelligence (ICAART 2010), 305 - 307.
  • 23. Poray, J., & Schommer, C. (2010). Managing Conversational Streams with Explorative Mind-Maps. Proceedings "AICCSA" (pp. 1 - 4).
  • 22. Schommer, C. (2010). A Molecular Concept of Managing Data. Abstract book of 2nd Conference on Agents and Artificial Intelligence (ICAART 2010), 300-305.
  • 21. Kaufmann, S., & Schommer, C. (2009). e-Conviviality in Web Systems by the Wisdom of Crowds. Abstract book of 4th ICITST09 - International Conference for Internet Technology and Secured Transactions, 74-76.
  • 20. Poray, J., & Schommer, C. (2009). A Cognitive Mind-Map Framework to Foster Trust. Proceedings "Fifth International Conference on Natural Computation, ICNC 2009" (pp. 3-7).
  • 19. Brucks, C., Hilker, M., Schommer, C., Wagner, C., & Weires, R. (2008). Symbolic Computing with Incremental Mind-maps to Manage and Mine Data Streams - Some Applications. Abstract book of 4th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy'08). ECAI 2008, 120-125.
  • 18. Hilker, M., & Schommer, C. (2008). Service Oriented Architecture in Network Security - a novel Organisation in Security Systems. Abstract book of 3rd International Workshop on Theory of Computer Viruses (TCV 2008), 120-125.
  • 17. Kaufmann, S., Schommer, C., & Weires, R. (2008). SEREBIF - Search Engine Result Enhancement by Implicit Feedback. Proceedings WebIst (pp. 263-266).
  • 16. Schommer, C. (2008). Sieving publishing communities in DBLP. Proceedings "Third IEEE International Conference on Digital Information Management (ICDIM)" (pp. 621-625).
  • 15. Brucks, C., Hilker, M., Schommer, C., Wagner, C., & Weires, R. (2007). Semi-automated Content Zoning of Spam Emails. Lecture Notes on Business Information Processing. Web Information Systems and Technologies, 35-44.
  • 14. Brucks, C., Hilker, M., Schommer, C., Wagner, C., & Weires, R. (2007). Semi-automated Content Zoning of Spam Emails. Proceedings "WebIst" (pp. 35-44).
  • 13. Hilker, M., & Schommer, C. (2007). SANA - Network Protection through artificial Immunity. Abstract book of 2nd International Workshop on Theory of Computer Viruses (TCV 2007), 120-125.
  • 12. Hilker, M., & Schommer, C. (2006). AGNOSCO - Identification of Infected Nodes with artificial Ant Colonies.
  • 11. Hilker, M., & Schommer, C. (2006). AGNOSCO - Identification of Infected Nodes with artificial Ant Colonies. Abstract book of 6th International Conference on Recent Advances in Soft Computing (RASC 2006), 120-125.
  • 10. Hilker, M., & Schommer, C. (2006). SANA - Security Analysis in Internet Traffic through Artificial Immune Systems. Technical Documentary Report. United States. Air Force. Systems Command. Electronic Systems Division.
  • 9. Hilker, M., & Schommer, C. (2006). Description of bad-signatures for Network Intrusion Detection. Proceedings of the 4th. Australasian Information Security Workshop (AISW-NetSec 2006).
  • 8. Hilker, M., & Schommer, C. (2005). A new queueing strategy for the Adversarial Queueing Theory. IPSI-2005, 120-125.
  • 7. Schommer, C., & Schroeder, B. (2005). ANIMA: Associate Memories for Categorical data Streams. Abstract book of 3rd International Conference on Computer Science and its Applications (ICCSA-2005), 120-125.
  • 6. Schommer, C. (2004). An incremental neural-based method to discover temporal skeletons in transactional data streams. Abstract book of 5th Recent Advances in Soft Computing (RASC 2004), 80-85.
  • 5. Schommer, C. (2004). An SQL-like interface to retrieve associative patterns from neural skeletons. Proceedings 2004 International Conference on Advances in Intelligent Systems - Theory and Applications, 80-85.
  • 4. Schommer, C. (2004). Incremental Discovery of Association Rules with Dynamic Neural Cells. Workshop on Symbolic Networks. ECAI 2004, 10-15.
  • 3. Schroeder, B., Hansen, F., & Schommer, C. (2004). A methodology for pattern discovery in tennis rallys using the adaptive framework ANIMA. Workshop Knowledge Discovery in Data Streams. ECML/PKDD 2005, 10-15.
  • 2. Sun, Q., Schommer, C., & Lang, A. (2004). Integration of Manual and Automatic Text Categorization. A Categorization Workbench for Text-Based Email and Spam. Proceedings KI 2004 (pp. 156-167).
  • Schommer, C. (2003). Anwendung von Data Mining. Shaker Publishing.
  • 1. Bayerl, S., Bollinger, T., & Schommer, C. (2002). Applying Mining with Scoring. Data Mining III, 6 (pp. 757-766). WIT Press.


Books

  • 5. C. Schommer (2003). Anwendung von Data Mining. Shaker Publishing.    
  • 4. C. M. Andersen, S. Bayerl, G. Bent, J. Lee, C. Schommer: Mining your own Business. IBM Redbook Series, Vol. 1 (Retail). - ISBN 0738422940. IBM Press. 2001.
  •     3. C. M. Andersen, S. Bayerl, G. Bent, J. Lee, C. Schommer: Mining your own Business. IBM Redbook Series. Vol. 2 (Finance) - ISBN 0738422959. IBM Press. 2001.
  •     2. C. M. Andersen, S. Bayerl, G. Bent, J. Lee, C. Schommer: Mining your own Business. IBM Redbook Series. Vol. 3 - (Telecommunications). ISBN 0738422967. IBM Press. 2001.
  •     1. C. M. Andersen, S. Bayerl, G. Bent, J. Lee, C. Schommer: Mining your own Business. IBM Redbook Series. Vol. 4 (Healthcare) - ISBN 0738423025. IBM Press. 2001.



Book Chapters

  •     3. P. Gentsch, U. Mueller, C. Schommer: Analytisches CRM in der Praxis: Vorgehensmodell, Praxisbeispiele und Erfolgsfaktoren. In Ahlert et al.: Customer Relationship Marketing im Handel - Strategien, Anwendungen, Erfahrungen. Springer Verlag, 2002.
  •     2. C. Schommer, U. Mueller: Data Mining im eCommerce: ein Fallbeispiel zur erweiterten Logfile-Analyse In HMD - Praxis der Wirtschaftsinformatik. Band 06/2001 Business Intelligence, Vol. 222.
  •     1. C. Schommer, H. Muster, R. Grund: Churn Prediction mit neuronalen Klassifikationsverfahren. In Neuronale Netze im Marketing-Management. Praxisorientierte Einfuehrung in modernes Data-Mining von Klaus-Peter Wiedmann und Frank Buckler. Gabler, zweite Auflage 2003, p 275. ISBN: 978-3409216739.



Industrial Papers

  •     6. C. Schommer: Discovering Fraud Behaviour in Call Detailed Records. SecDay - 2010 Grande Region Security and Reliability Day. Saarbruecken 2010.
  •     5. W. Mayer, C. Schommer: Mobile Patient Record Management through DB2 Everyplace In: Mobile Computing in Medicine, Springer Verlag, Heidelberg, 2002. Bibtex
  •     4. C. Schommer, H.D. Wehle: From VSE/ESA data to Business Intelligence VSE Newsletter G225-4508-21, November 2000, published in VDI-Newsletter 2000.
  •     3. T. Bollinger, C. Schommer, H. D. Wehle: Profitabler Einsatz von Data Mining im Customer Relationship am Beispiel der Investitionsgueterindustrie In: VDI-Berichte, Band 1381 Computational Intelligence - Neuronale Netze - Evolutionaere Algorithmen - Fuzzy Control im industriellen Einsatz. 2000.
  •     2. T. Bollinger, C. Schommer, H.D. Wehle: Vorhersage von Lieferzeiten. IS Report - Zeitschrift fuer betriebswirtschaftliche Informationssysteme. 4. Jahrgang, 6/2000.
  •   1.  C. Schommer: Data Mining: die Suche nach versteckten Informationen im Data Warehouse Proceedings 7. Kolloquium Software - Entwicklungen, Methoden, Werkzeuge. Ostfildern, September 1997.