Prof. Hennie Daniels

Hennie Daniels

Professor of Knowledge Management
Rotterdam School of Management (RSM)
Erasmus University Rotterdam

Profile

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

Professional experience

Full Professor

Erasmus University Rotterdam
RSM - Rotterdam School of Management
Department of Technology and Operations Management

Publications

Professional Publications (12)

  • 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 (2017). Agile biedt projectmanager oude stijl kansen.
  • 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.
  • 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

Work in Progress (3)

  • M. Van Beek, A. De Waegenaere & H.A.M. Daniels (2019). Audit Economics: Optimal Sampling Across Objectives when Samples are Costly. Management Science .
  • R.J.M.A. Triepels, H.A.M. Daniels & R.J. Berndsen (2019). Monitoring Liquidity Management of Banks Using Recurrent Neural Networks. IEEE Transactions on Neural Networks and Learning Systems .
  • H.A.M. Daniels & M. Velikova (2019). Derivation of monotone decision models from noisy data. .

Scholarly Publications (66)

  • W. Heijden, M. Homberg, M. Marijnis, M. Graaff de & H.A.M. Daniels (2018). Combining Open Data and Machine Learning to predict Food Security in Ethiopia. 5th International Conference on Technologies for Developed: Lausanne.
  • R.J.M.A. Triepels, H.A.M. Daniels & A.F. Feelders (2018). Data Driven Fraud Detection in International Shipping. Expert Systems with Applications, 99 , 193-202. 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. In Lecture Notes in Business Information Processing (pp. 145-161). Cham: Springer Verlag
  • 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
  • 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
  • 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
  • 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 & H.A.M. Daniels (2016). A Comparison of Three Models to Predict Liquidity Flows between Banks Based on Daily Payments Transactions.
  • R.J.M.A. Triepels & H.A.M. Daniels (2015). Detecting shipping fraud in global supply chains using probabilistic trajectory classification. 17th International Conference on Enterprise Information Systems: Barcelona (2015, april 27 - 2015, april 30).
  • 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: 10.1007/978-3-319-24369-623
  • 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: 10.1007/s10614-013-9386-7
  • 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, 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: 10.1080/2287108X.2013.11006084
  • 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 (2013). Explanatory analytics in OLAP. International Journal of Business Intelligence Research, 4 (3), 67-82. doi: 10.4018/ijbir.2013070105
  • 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. Lingzhe, H.A.M. Daniels & H. Weigand (2012). A business intelligence framework for risk assessment in business networks. European Conference on Information Systems ECIS: Barcelona.
  • 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
  • L. Lingzhe & H.A.M. Daniels (2012). Towards a value model for collaborative, business intelligence-supported risk assessment. International workshop on Value Based Modelling: Vienna.
  • 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
  • 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
  • H.A.M. Daniels & M. Velikova (2010). Monotone and partially monotone neural networks. IEEE Transactions on Neural Networks, 21 (6), 906-917. doi: 10.1109/TNN.2010.2044803
  • 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: 10.1016/j.neunet.2009.09.002
  • 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
  • M. Velikova & H.A.M. Daniels (2009). On testing monotonicity of datasets. In Proceeding of European Conference on Machine Learning (pp. 11-23). Bled
  • 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: 10.1002/isaf.290
  • 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: 10.1016/j.ejor.2007.04.039
  • 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
  • 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
  • M. Velikova & H.A.M. Daniels (2008). Monotone prediction models in data mining. Saarbrücken: VDM Verlag
  • 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
  • M. Velikova & H.A.M. Daniels (2004). Decision Tree's for Monotone Price Models. Computational Management Science, 1 (3/4), 231-244.
  • 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
  • E.A.M. Caron & H.A.M. Daniels (2004). Diagnosis in the OLAP context. (Intern rapport, ERIM report series Research in Management, no 2004-063). :
  • 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 & 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 Directed by Intellectual Capital Scorecards. (Intern rapport, ERIM series, no ERS-2003-l). onbekend: Rotterdam School of Management
  • 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 (2001). Fusion of Expert Decision Rules and Knowledge derived from Databases. In EURO 2001 . Rotterdam
  • 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
  • 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: 10.1016/S0377-2217(99)00428-2
  • 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
  • H.A.M. Daniels, A.J. Feelders & M. Holzheimer (2000). Methodological and Practical Aspects of Datamining. Information and Management, 37 (5), 271-281. doi: 10.1016/S0378-7206(99)00051-8
  • H.A.M. Daniels & A.J. Feelders (2000). Combining Domain Knowledge and Data in Datamining Systems. (Extern rapport, 2000-63). Tilburg: Tilburg University
  • 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 (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: 10.1080/096031098332844
  • 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 (1998). Van Kunstmatige Intelligentie naar de Kenniseconomie. Rotterdam: Eburon
  • 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 (1997). Modelling non-linearity in economic classification with neural networks. International Journal of Intelligent Systems in Accounting, Finance and Management, 6 (4), 287-301.
  • H.A.M. Daniels, B. Kamp & W. Verkooijen (1997). Application of neutral networks to house pricing and bond rating. (Extern rapport, Discussion paper, no 9796). Tilburg: Tilburg 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). 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). -: -
  • 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
  • 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.
  • 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
  • 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: 10.1007/BF00436285

Semi Scientific Publications

  • H.A.M. Daniels (2016). Beveiliging Persoonsgegevens van Levensbelang (interview by Ruby Sanders). Elseviers Weekblad, oktober (bijlage Digital Transformation), 11-12.

Other (2)

  • A.J. Feelders & H.A.M. Daniels (1999). Business aspects of Data Mining. (Intern rapport, ERASM Management Report Series, no 48-1999). :
  • A.J. Feelders & H.A.M. Daniels (1998). A general model for automated business diagnosis. (Intern rapport, Management Report Series, no 22-1998). : ERIM

Media

Media items

  • Business Intelligence geen moderne flauwekul

    Not every company is convinced of the use of a Business Intelligence consultant and expert. However, nowadays almost every company is data driven, and BI can truly help choosing the best strategy. Hennie Daniels even explains the...

  • Beveiliging van persoonsgegevens van levensbelang

    Due to a digitalizing society, the need for data security becomes increasingly important. Therefore, new policies and laws are formulated to stop data leakages. Hennie Daniels is interviewed regarding the topic in the case of the...