prof.dr.ir. H.A.M. (Hennie) Daniels

Full Professor
Rotterdam School of Management (RSM)
Erasmus University Rotterdam
Member ERIM
Field: Logistics & Information Systems
Affiliated since 2000

Hennie Daniels is a professor of knowledge management at the Department of Technology and Operations Management, Rotterdam School of Management, Erasmus University.

  • E.A.M. Caron & H.A.M. Daniels (2018). Sensitivity Analysis in OLAP databases. In Proceedings of the 20th International Conference on Enterprise Information Systems
  • H.A.M. Daniels & M. Velikova (2018). Derivation of monotone decision models from noisy data. .
  • R.J.M.A. Triepels, H.A.M. Daniels & A.F. Feelders (2018). Data Driven Fraud Detection in International Shipping. Expert Systems with Applications, Accepted. doi: 10.1016/j.eswa.2018.01.007
  • R.J.M.A. Triepels, H.A.M. Daniels & R. Heijmans (2018). Detection and Explanation of Anomalous Payment Behaviour in Real-Time Gross Settlement Systems. Lecture Notes in Business Information Systems, Accepted.
  • R.J.M.A. Triepels & H.A.M. Daniels (2016). Supervision of Financial Market Infrastructures using Temporal Network Analysis. In Book of Abstracts, 22nd International Conference on Computational Economics. Bordeaux
  • R.J.M.A. Triepels & H.A.M. Daniels (2016). A Comparison of Three Models to Predict Liquidity Flows between Banks Based on Daily Payments Transactions. In 14th Payment and Settlement System Simulation Seminar and Workshop. Helsinki: Bank of Finland
  • E.A.M. Caron & H.A.M. Daniels (2016). Identification of Organization Name Variants in Large Databases using Rule-based Scoring and Clustering - With a Case Study on the Web of Science Database. In 18th International Conference on Enterprise Information Systems (ICEIS 2016) (pp. 182-187). Rome, Italy: SCITEPRESS
  • R.J.M.A. Triepels, A.F. Feelders & H.A.M. Daniels (2015). Uncovering Document Fraud in Maritime Freight Transport Based on Probabilistic Classification. Lecture Notes in Computer Science, 9339 (23), 1-12. doi: http://dx.doi.org/10.1007/978-3-319-24369-623
  • L. Liu, H.A.M. Daniels & W.J. Hofman (2014). Business Intelligence for Improving Supply Chain Risk Management. In Springer Lecture Notes in Business Information Systems
  • L. Liu, H.A.M. Daniels & R.J.M.A. Triepels (2014). Auditing Data Reliability in International Logistics - An Application of Bayesian Networks. In Proceedings of the 16th International Conference on Enterprise Information Systems (pp. 707-712). Lissabon
  • E. van Beek & H.A.M. Daniels (2014). A non-parametric test for partial monotonicity in multiple regression. Computational Economics, 44 (1), 87-100. doi: http://dx.doi.org/10.1007/s10614-013-9386-7[go to publisher's site]
  • L. Lingzhe & H.A.M. Daniels (2013). Analysis for Detecting and Explaining Exceptions in Business Data. In Dianne Lux Wigand et.al. (Ed.), Proceedings of the 26th e Bled Conference (pp. 349-358). Bled
  • L. Liu, H.A.M. Daniels, M.P.A. van Oosterhout & J. van Dalen (2013). Business Intelligence for Improving Supply Chain Risk Management. International Journal in Advanced Logistics, 2 (2), 18-29. doi: http://dx.doi.org/10.1080/2287108X.2013.11006084
  • E.A.M. Caron & H.A.M. Daniels (2013). Explanatory analytics in OLAP. International Journal of Business Intelligence Research, 4 (3), 67-82. doi: http://dx.doi.org/10.4018/ijbir.2013070105
  • L. Lingzhe, H.A.M. Daniels & W. Hoffmann (2013). Detecting and Explaining Business Exceptions for Risk Assessment. In Hammoudi et.al. (Ed.), Proceedings of the 15th International Conference on Enterprise Information Systems (pp. 442-447). Angers
  • E.A.M. Caron & H.A.M. Daniels (2012). Explanatory Analysis in Business Intelligence Systems. In proceedings of the European Conference on Information Systems ECIS (pp. 77-89). Barcelona
  • H.A.M. Daniels & E.A.M. Caron (2011). Analysis of variance in OLAP information systems. In 8th International conference on Computational Management Science (pp. 16). Neuchatel, Switzerland: University of Neuchatel
  • H.A.M. Daniels & M. Velikova (2010). Monotone and partially monotone neural networks. IEEE Transactions on Neural Networks, 21 (6), 906-917. doi: http://dx.doi.org/10.1109/TNN.2010.2044803[go to publisher's site]
  • A. Minin, B. Lang, M. Velikova & H.A.M. Daniels (2010). Comparison of universal approximators incorporating partial monotonicity by structure. Neural Networks, 23 (4), 471-475. doi: http://dx.doi.org/10.1016/j.neunet.2009.09.002[go to publisher's site]
  • E.A.M. Caron & H.A.M. Daniels (2010). What-if analysis in OLAP, with a case study in supermarket sales data. In Proceedings of the 12th International Conference on Enterprise Information Systems (pp. 208-213). Fuchal
  • M. Velikova & H.A.M. Daniels (2009). On testing monotonicity of datasets. In Proceeding of European Conference on Machine Learning (pp. 11-23). Bled
  • E.A.M. Caron & H.A.M. Daniels (2009). Business Analysis in the OLAP context. In J Cordeiro & J Filipe (Eds.), Proceedings ICEIS 2009: Artifical Intelligence and Decision Support Systems (pp. 325-330). Milan: INSTICC
  • H.A.M. Daniels & E.A.M. Caron (2009). Automated explanation of financial data. International Journal of Intelligent Systems in Accounting, Finance and Management, 16 (1-2), 5-19. doi: http://dx.doi.org/10.1002/isaf.290
  • M. Velikova, H.A.M. Daniels & M. Samulski (2008). Partially monotone Networks applied to Breast Cancer Detection on Mammograms. In Neruda.R. Koutnik J. Kurkova-Pohlova V. (Ed.), Proceedings of the 18th International Conference on artificial neural networks (ECANN 2008) Vol. 5163. Lecture Notes in Computer Science (pp. 917-926). Heidelberg: Springer- Verlag
  • E.A.M. Caron & H.A.M. Daniels (2008). Explanation of exceptional values in multi-dimensional databases. European Journal of Operational Research, 188 (3), 884-897. doi: http://dx.doi.org/10.1016/j.ejor.2007.04.039[go to publisher's site]
  • E.A.M. Caron & H.A.M. Daniels (2008). Extensions to the OLAP framework for business analysis. In B..Shishkov A. Ranchordas J. Cordeiro (Ed.), Third international conference on software and data technologies - ICSOFT 2008 (pp. 240-247). Porto: INSTICC
  • H.A.M. Daniels & E.A.M. Caron (2007). Explanation generation in business performance models - With a case study in competition benchmarking. In J..Cordeiro J. Filipe J. Cardoso (Ed.), Proceedings ICEIS 2007 Artificial Intelligence and Decision Support Systems (pp. 119-128). Funchal, Portugal: INSTICC
  • M. Velikova, H.A.M. Daniels & A.J. Feelders (2006). Mixtures of Monotone Networks for Prediction. International Journal of Computational Intelligence, 3 (3), 205-214.
  • H.A.M. Daniels & N. Noordhuis (2005). Project selection based on intellectual capital scorecards. International Journal of Intelligent Systems in Accounting, Finance and Management, 13 (1), 27-32.
  • H.A.M. Daniels & M.T. Smits (2005). Portfolio Optimisation as a Tool for Knowledge Management. In Haasis et. al. (Ed.), Operations Research Proceedings 2005, Part 17 Managerial Accounting (pp. 633-639). Springer Verlag
  • E.A.M. Caron & H.A.M. Daniels (2004). Automated Business Diagnosis in the OLAP Context. In H. Fleuren & .P..Kort D. den Hertog (Eds.), Operations Research Proceedings 2004 (pp. 425-433). Berlin: Springer
  • M. Velikova & H.A.M. Daniels (2004). Decision Tree's for Monotone Price Models. Computational Management Science, 1 (3/4), 231-244.
  • H.A.M. Daniels & M. Velikova (2003). Derivation of monotone decision models from noisy data. In Proceeding International Conference. Crete: Book of Abstracts
  • H.A.M. Daniels & B. de Jonge (2003). Project Selection in Knowledge Intensive Organisations. In proceedings of the 11th European conference on Information Systems. Napels: ECIS
  • H.A.M. Daniels (2001). Fusion of Expert Decision Rules and Knowledge derived from Databases. In EURO 2001. Rotterdam
  • A.J. Feelders & H.A.M. Daniels (2001). A General Model for Automated Business Diagnosis. European Journal of Operational Research, 130 (3), 623-637. doi: http://dx.doi.org/10.1016/S0377-2217(99)00428-2
  • H.A.M. Daniels, A.J. Feelders & M. Holzheimer (2000). Methodological and Practical Aspects of Datamining. Information and Management, 37 (5), 271-281. doi: http://dx.doi.org/10.1016/S0378-7206(99)00051-8
  • H.A.M. Daniels & B. Kamp (1999). Application of MLP Networks to Bond Rating and House Pricing. Neural Computing and Applications, 8 (3), 226-234.
  • J.E.J. Plasmans, H.A.M. Daniels & W. Verkooijen (1998). Estimating structural exchange rate models by artificial neural networks. Applied Financial Economics, 8 (5), 541-551. doi: http://dx.doi.org/10.1080/096031098332844
  • H.A.M. Daniels, B. Kamp & W. Verkooijen (1997). Modelling non-linearity in economic classification with neural networks. International Journal of Intelligent Systems in Accounting, Finance and Management, 6 (4), 287-301.
  • W.J. Verkooijen & H.A.M. Daniels (1995). Building error-correction models with neural networks: an application to the Dutch mortgage loan market. Economic and Financial Computing, 5 (2), 101-130.
  • R.J. Berndsen & H.A.M. Daniels (1994). Causal Reasoning and Explanation in Dynamic Economic Systems. Journal of Economic Dynamics and Control, 251-271.
  • W.J. Verkooijen & H.A.M. Daniels (1994). Connectionist Projection Pursuit Regression. Computational Economics, 7 (3), 155-161.
  • J.M. Broek & H.A.M. Daniels (1991). Application of constraint logic programming to asset and liability management in banks. Computer Science in Economics and Management, 4, 107-116. doi: http://dx.doi.org/10.1007/BF00436285
  • M. Velikova & H.A.M. Daniels (2008). Monotone prediction models in data mining. Saarbrücken: VDM Verlag
  • H.A.M. Daniels (1998). Van Kunstmatige Intelligentie naar de Kenniseconomie. Rotterdam: Eburon
  • H.A.M. Daniels & H. Noordhuis (2002). Management of Intellectual Capital by Optimal Portfolio Selection. In G. Goos & J. Hartmanis (Eds.), Practical Aspects of Knowledge Management - Lecture Notes on Artificial Intelligence. Berlin: Springer
  • H.A.M. Daniels & H.G. van Dissel (2002). Risk Management based on Expert Rules and Data Mining: A case study in Insurance. In Proceedings of the 10th European Conference on Information Systems (ECIS), Gdansk
  • H.A.M. Daniels & A.J. Feelders (2001). Integrating economic knowledge in data mining algorithms. In Proceedings SBIT Symposium, Tilburg
  • H.A.M. Daniels & A.J. Feelders (2001). Combining Domain Knowledge and Data for House Price Modelling with Classification Trees and Neural Networks. In Proceedings of the 5th European Conference on Principles and Practice of Knowledge Discovery in Databases, Datamining in Marketing Applications, Freiburg
  • H.A.M. Daniels & A.J. Feelders (2001). On the Implementation of Monotonicity in Economic Decision Problems. In - - (Ed.), Proceedings of the 8th International Conference on Connexionist Approaches in Economics and Management, Rennes (pp. 25-34). Rennes: ACSEG
  • A.J. Feelders & H.A.M. Daniels (1999). Knowledge Discovery in Practice. In H. Jessen (Ed.), Proceedings of the International Conference Machine Learning and Applications, ACAI 99, Workshop 08: Data Mining in Economics, Marketing and Finance, Chania (pp. 1-8). -: -
  • H.A.M. Daniels & B. Kamp (1998). Application of neural networks to bond rating. In J.-M. Aurifeille & C. Deissenberg (Eds.), Bio-mimetic approaches in management science Chapter 3 (Advances in computational management science, 1) (pp. 27-45). Boston, Dordrecht: Kluwer Academic Publishers
  • H.A.M. Daniels, B. Kamp & W. Verkooijen (1998). Forecasting and classification with neural networks: application to the mortgage market and bond rating. In ? redacteur? (Ed.), Proceedings 18th International Symposium on Forecasting (ISF 98) (pp. 10-10). Edinburgh: Napier University
  • H.A.M. Daniels, W. Verkooijen & A.J. Feelders (1996). Kennissystemen voor financiële diagnose: taak en modelperspectief. In J.A.M. Oonincx, P.M.A. Ribbers & C.A.Th. Takkenberg (Eds.), Organisatie, Besturing en Informatie: Ontwikkeling van Theorie en Praktijk. Liber Amicorum bij het afscheid van Prof.dr.ir. G.C.J.F. Nielen (pp. 307-332). Alphen a/d Rijn: Samson
  • H.A.M. Daniels, B. Kamp & W. Verkooijen (1996). Design of neural networks for prediction and classification in economic problems. In Lj. Vlaecic, T. Nguyen & D. 'Ce'cez-Kecmanovi'c (Eds.), Modelling and Control of National and Regional Economics, 1995. Proceedings of the IFAC/IFIP/IFORS/SEDS Symposium, Gold Coast, Queensland, Australia (pp. 387-397). Brisbane, Australia: Pergamon Press
  • H.A.M. Daniels, B. Kamp & W. Verkooijen (1996). Controlling the Flexibility of Neural Networks: An empirical study in Financial Modelling (Management Report Series,ERASM). In - - (Ed.), Paper presented as abstract in the Proceedings of the third International Conference on Computing in Economics and Finance, Stanford(1997( (pp. 1-20). -: -
  • A.J. Feelders & H.A.M. Daniels (1994). A formal Framework for Diagnosis in Business Performance. In @ @ (Ed.), Proceedings of the International Conference on Intelligent Systems (pp. 123-134). Singapore: 2
  • M. Timmermans, R. Heijmans & H.A.M. Daniels (2017). Cyclical patterns in risk indicators based on financial market infrastructure transaction data.
  • R.J.M.A. Triepels, R. Heijmans & H.A.M. Daniels (2017). Anomaly Detection in Real-Time Gross Payment Data. In Proceedings of the 19th International Conference on Enterprise Information Systems, (ICEIS 2017) (pp. 433-441)
  • R.J.M.A. Triepels, H.A.M. Daniels & R. Heijmans (2017). Anomaly Detection in Real-Time Gross Payment Data. Financial Market Infrastructure Conference II, Contribution to conference: New Thinking in a New Area: Amsterdam, June 2017.
  • H.A.M. Daniels & M. Bosch (2016). Trends in Data Science, interview with Future Consult.
  • H.A.M. Daniels & S. Brinkkemper (2016). Interview Business Intelligence: geen moderne flauwekul, Interviews with Daniels H.A.M. and Brinkkemper S. Smart Industry, juli.
  • M. Velikova, H.A.M. Daniels & A.J. Feelders (Ed.). (2006). Solving Partially Monotone Problems with Neural Networks, Transactions on Engineering Computing and Technology (12). Austria: Proceedings of ICCS'06 Vienna
  • H.A.M. Daniels, M.T. Smits, H.D. Haasis, H. Kopfer & J. Schonberger (Ed.). (2005). Portfolio Optimisation as a Tool for Knowledge Management, Operations Research Proceedings 2005 (Part 17 Managerial Accounting). Bremen: Springer Verlag
  • E.A.M. Caron & H.A.M. Daniels (2004). Extending the OLAP Framework for Automated Explanatory Tasks. In Conference on Computational Economics and Finance (CEF 2004), 1 page extended abstract
  • H.A.M. Daniels & A.J. Feelders (2000). Combining Domain Knowledge and Data in Datamining Systems. (Extern rapport, CentER research paper, no 2000-63). Tilburg: Tilburg University, CentER
  • H.A.M. Daniels, B. Kamp & W.J. Verkooijen (1996). Controlling the flexibility of neural networks: an empirical study in financial modelling. (Intern rapport, Management Report, no 288). :
  • R.P.A.J. Verkooijen & H.A.M. Daniels (1995). Long Run Exchange Rate Determination: a Neural Network Study. (Extern rapport). Tilburg: Tilburg University, Centre for Economic Research
  • Role: Promotor
  • PhD Candidate: Emiel Caron
  • Time frame: 2002 - 2013
  • Role: Member Doctoral Committee
  • PhD Candidate: Viara Popova
  • Time frame: 1999 - 2004
  • Role: Promotor
  • PhD Candidate: Lingzhe Liu
  • Time frame: 2011 -

Address

Visiting address

Office: Mandeville Building T09-10
Burgemeester Oudlaan 50
3062 PA Rotterdam

Postal address

Postbus 1738
3000 DR Rotterdam
Netherlands