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243 results on '"Stahlbock, Robert"'

1. From Digitalization to Data-Driven Decision Making in Container Terminals

Author

Heilig, Leonard, Stahlbock, Robert, and Voß, Stefan

Subjects
Computer Science - Databases, Computer Science - Machine Learning
Abstract

With the new opportunities emerging from the current wave of digitalization, terminal planning and management need to be revisited by taking a data-driven perspective. Business analytics, as a practice of extracting insights from operational data, assists in reducing uncertainties using predictions and helps to identify and understand causes of inefficiencies, disruptions, and anomalies in intra- and inter-organizational terminal operations. Despite the growing complexity of data within and around container terminals, a lack of data-driven approaches in the context of container terminals can be identified. In this chapter, the concept of business analytics for supporting terminal planning and management is introduced. The chapter specifically focuses on data mining approaches and provides a comprehensive overview on applications in container terminals and related research. As such, we aim to establish a data-driven perspective on terminal planning and management, complementing the traditional optimization perspective., Comment: 20 pages, 5 figures, book chapter

Published
2019

4. Blockchain in der maritimen Logistik

Author

Stahlbock, Robert, Heilig, Leonard, Cammin, Philip, Voß, Stefan, Fröschle, Hans-Peter, Series Editor, Hildebrand, Knut, Series Editor, Hofmann, Josephine, Series Editor, Knoll, Matthias, Series Editor, Meier, Andreas, Series Editor, Meinhardt, Stefan, Series Editor, Reinheimer, Stefan, Series Editor, Robra-Bissantz, Susanne, Series Editor, Strahringer, Susanne, Series Editor, and Fill, Hans-Georg, editor

Published
2020
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5. Introduction

Author

Abou-Nasr, Mahmoud, Lessmann, Stefan, Stahlbock, Robert, Weiss, Gary M., Sharda, Ramesh, Series editor, Voß, Stefan, Series editor, Abou-Nasr, Mahmoud, editor, Lessmann, Stefan, editor, Stahlbock, Robert, editor, and Weiss, Gary M., editor

Published
2015
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6. Correction to: Principles of Data Science

Author

Arabnia, Hamid R., Daimi, Kevin, Stahlbock, Robert, Soviany, Cristina, Heilig, Leonard, Brüssau, Kai, Arabnia, Hamid, Series Editor, Arabnia, Hamid R., editor, Daimi, Kevin, editor, Stahlbock, Robert, editor, Soviany, Cristina, editor, Heilig, Leonard, editor, and Brüssau, Kai, editor

Published
2020
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9. Erratum zu: Blockchain in der maritimen Logistik

Author

Stahlbock, Robert, Heilig, Leonard, Cammin, Philip, Voß, Stefan, Fröschle, Hans-Peter, Series Editor, Hildebrand, Knut, Series Editor, Hofmann, Josephine, Series Editor, Knoll, Matthias, Series Editor, Meier, Andreas, Series Editor, Meinhardt, Stefan, Series Editor, Reinheimer, Stefan, Series Editor, Robra-Bissantz, Susanne, Series Editor, Strahringer, Susanne, Series Editor, and Fill, Hans-Georg, editor

Published
2020
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14. Container Terminal Yard Operations – Simulation of a Side-Loaded Container Block Served by Triple Rail Mounted Gantry Cranes

Author

Klaws, Jan, Stahlbock, Robert, Voß, Stefan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Böse, Jürgen W., editor, Hu, Hao, editor, Jahn, Carlos, editor, Shi, Xiaoning, editor, Stahlbock, Robert, editor, and Voß, Stefan, editor

Published
2011
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15. Economic Impacts of the Alternative Reuse of Empty ISO Containers

Author

Großkurth, Peter, Stahlbock, Robert, Voß, Stefan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Böse, Jürgen W., editor, Hu, Hao, editor, Jahn, Carlos, editor, Shi, Xiaoning, editor, Stahlbock, Robert, editor, and Voß, Stefan, editor

Published
2011
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20. Introduction

Author

Abou-Nasr, Mahmoud, primary, Lessmann, Stefan, additional, Stahlbock, Robert, additional, and Weiss, Gary M., additional

Published
2014
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22. The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing

Author

Crone, Sven F., Lessmann, Stefan, and Stahlbock, Robert

Subjects
Neural networks -- Marketing, Direct marketing, Algorithms, Decision-making, Data mining, Marketing industry, Management science, Algorithm, Data warehousing/data mining, Company marketing practices, Neural network, Business, Business, general, Business, international
Abstract

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2005.07.023 Byline: Sven F. Crone (a), Stefan Lessmann (b), Robert Stahlbock (b) Keywords: Data mining; Neural networks; Data preprocessing; Classification; Marketing Abstract: Corporate data mining faces the challenge of systematic knowledge discovery in large data streams to support managerial decision making. While research in operations research, direct marketing and machine learning focuses on the analysis and design of data mining algorithms, the interaction of data mining with the preceding phase of data preprocessing has not been investigated in detail. This paper investigates the influence of different preprocessing techniques of attribute scaling, sampling, coding of categorical as well as coding of continuous attributes on the classifier performance of decision trees, neural networks and support vector machines. The impact of different preprocessing choices is assessed on a real world dataset from direct marketing using a multifactorial analysis of variance on various performance metrics and method parameterisations. Our case-based analysis provides empirical evidence that data preprocessing has a significant impact on predictive accuracy, with certain schemes proving inferior to competitive approaches. In addition, it is found that (1) selected methods prove almost as sensitive to different data representations as to method parameterisations, indicating the potential for increased performance through effective preprocessing; (2) the impact of preprocessing schemes varies by method, indicating different 'best practice' setups to facilitate superior results of a particular method; (3) algorithmic sensitivity towards preprocessing is consequently an important criterion in method evaluation and selection which needs to be considered together with traditional metrics of predictive power and computational efficiency in predictive data mining. Author Affiliation: (a) Department of Management Science, Lancaster University, Lancaster LA1 4YX, United Kingdom (b) Institute of Information Systems, University of Hamburg, Von-Melle-Park 5, 20146 Hamburg, Germany Article History: Received 15 November 2004; Accepted 18 July 2005

Published
2006

25. Yin, Yong, Ikou Kaku, Jiafu Tang, Jian-Ming Zhu. 2011. Data Mining: Concepts, Methods and Applications in Management and Engineering Design

Author

Stahlbock, Robert

Subjects
Data Mining: Concepts, Methods and Applications in Management and Engineering Design (Nonfiction work) -- Yin, Yong -- Kaku, Ikou -- Tang, Jiafu -- Zhu, Jian-Ming -- Book reviews, Books -- Book reviews, Business, general, Business
Abstract

YIN, YONG, IKOU KAKU, JIAFU TANG, JIAN-MING ZHU. 2011. Data Mining: Concepts, Methods and Applications in Management and Engineering Design. Springer, Heidelberg, Germany. 312 pp. $179.00. Data mining (DM) can [...]

Published
2012
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26. Data Science

Author

Stahlbock, Robert, Weiss, Gary M., Abou-Nasr, Mahmoud, Stahlbock, Robert, Weiss, Gary M., and Abou-Nasr, Mahmoud

Subjects
Data mining--Congresses, Big data--Congresses, Artificial intelligence--Congresses
Abstract

Proceedings of the 2019 International Conference on Data Science (ICDATA'19) held July 29th - August 1st, 2019 in Las Vegas, Nevada.

Published
2019

27. Data Mining

Author

Arabnia, Hamid R., ProQuest (Firm), Stahlbock, Robert, Abou-Nasr, Mahmoud, Weiss, Gary M., Arabnia, Hamid R., ProQuest (Firm), Stahlbock, Robert, Abou-Nasr, Mahmoud, and Weiss, Gary M.

Subjects
Data mining--Congresses
Abstract

This volume contains the proceedings of the 2017 International Conference on Data Mining (DMIN'17).

Published
2017

29. Data Mining

Author

Stahlbock, Robert, Weiss, Gary M., Stahlbock, Robert, and Weiss, Gary M.

Subjects
Data mining--Congresses
Abstract

Data Mining is a compendium of articles and papers that were presented at DMIN'16, an international conference that serves researchers, scholars, professionals, students, and academicians.

Published
2016

30. Data Mining

Author

Stahlbock, Robert, Weiss, Gary M., Abou-Nasr, Mahmoud, Arabnia, Hamid R., Stahlbock, Robert, Weiss, Gary M., Abou-Nasr, Mahmoud, and Arabnia, Hamid R.

Subjects
Data mining--Congresses
Abstract

Data Mining is a compendium of articles and papers that were presented at DMIN'15, an international conference that serves researchers, scholars, professionals, students, and academicians.

Published
2015

31. Data Mining

Author

Stahlbock, Robert, Weiss, Gary M., Stahlbock, Robert, and Weiss, Gary M.

Subjects
Electronic data processing--Distributed processing, Big data, Database management, File organization (Computer science), Cloud computing
Abstract

New research by international contributors on Data Mining

Published
2014

33. The Liner Shipping Fleet Repositioning Problem with Cargo Flows

Author

Tierney, Kevin, Møller Jensen, Rune, Hu, Hao, Shi, Xiaoning, Stahlbock, Robert, and Voß, Stefan

Subjects
Operations research, Computer science, Graph (abstract data type), Real world data, Liner shipping
Abstract

We solve an important problem for the liner shipping industry called the Liner Shipping Fleet Repositioning Problem (LSFRP). The LSFRP poses a large financial burden on liner shipping firms. During repositioning, vessels are moved between services in a liner shipping network. Shippers wish to reposition vessels as cheaply as possible without disrupting the cargo flows of the network. The LSFRP is characterized by chains of interacting activities with a multi-commodity flow over paths defined by the activities chosen. Despite its great industrial importance, the LSFRP has received little attention in the literature. We introduce a novel mathematical model of the LSFRP with cargo flows based on a carefully constructed graph and evaluate it on real world data from our industrial collaborator.

Published
2012
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34. Proceedings of the 2010 International Conference on Data Mining

Author

Stahlbock, Robert, Crone, Sven F., Abou-Nasr, Mahmoud, ARABNIA, Hamid R., Kourentzes, Nikolaos, Lenca, Philippe, Lippe, Wolfram-Manfred, Weiss, Gary M., Institute of Information Systems (Department of Business Administration, University of Hamburg) (IIS), Lancaster Research Centre for Forecasting (Lancaster University Management School, Department of Management Science) (LRCF), Research and Innovation Center, Ford (Ford Motor Company), University of Georgia [USA], Lab-STICC_TB_CID_DECIDE, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Institut für Informatik (Münster University), Fordham University [New York], Institute of Information Systems (Department of Business Administration, University of Hamburg) ( IIS ), Lancaster Research Centre for Forecasting (Lancaster University Management School, Department of Management Science) ( LRCF ), Department of Computer Science (University of Georgia), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance ( Lab-STICC ), École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ) -École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ), Département Logique des Usages, Sciences sociales et Sciences de l'Information ( LUSSI ), Université européenne de Bretagne ( UEB ) -Télécom Bretagne-Institut Mines-Télécom [Paris], and Department of Computer & Information Sciences (Fordham University)

Subjects
[ INFO.INFO-DB ] Computer Science [cs]/Databases [cs.DB], [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [ INFO.INFO-NE ] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI], Data mining, [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML], ComputingMilieux_MISCELLANEOUS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience

Published
2010

38. Modelling functional outliers for high frequency time series forecasting with neural networks:an empirical evaluation for electricity load data

Author

Stahlbock, Robert, Kourentzes, Nikolaos, Stahlbock, Robert, and Kourentzes, Nikolaos

Abstract

This paper discusses and empirically evaluates alternative methodologies in modeling functional outliers for high frequency time series forecasting. In spite of several modeling and forecasting methodologies that have been proposed, there have been limited advancements in monitoring and automatically identifying outlying patterns and even less in modeling those for such times series. This is a significant gap considering the difficulty and the cost associated with manual exploration and treatment of such data, due to the vast number of observations. This study proposes and assesses the performance of different modeling methodologies focusing on two key aspects, the accuracy that the outliers are modeled and the impact of each methodology on modeling normal observations. The evaluated methodologies model functional outliers using binary, integer or trigonometric dummy variables, outlier profiles or isolate them into new time series and forecast them separately. Neural networks are employed to produce the forecasts, taking advantage of their flexible nature to accommodate the different methodologies and their superior performance in high frequency time series forecasting. Hourly electricity load data from the UK are used to empirically evaluate the performance of the different methodologies.

Published
2011

39. Inference for Neural Network Predictive Models with Impulse Interventions

Author

Stahlbock, Robert, Crone, Sven F., Abou-Nasr, Mahmoud, Arabnia, Hamid R., Kourentzes, Nikolaos, Lenca, Philippe, Lippe, Wolfram-Manfred, Weiss, Gary M., Crone, Sven, Stahlbock, Robert, Crone, Sven F., Abou-Nasr, Mahmoud, Arabnia, Hamid R., Kourentzes, Nikolaos, Lenca, Philippe, Lippe, Wolfram-Manfred, Weiss, Gary M., and Crone, Sven

Abstract

Neural Networks (NN) have demonstrated remarkable time series fitting and prediction abilities, outperforming in several applications other methods and particularly linear models, such as dynamic linear regression. However, due to their nature, NNs are not easy to interpret and are often considered as black box models. The importance of each independent variable is hard to estimate and therefore test whether they have significant explanatory power and hence be included in the model or not. This task is very important for several applications, where the effect of each variable has to be identified, such as marketing modelling and analysis, where the effectiveness of different marketing instruments has to be estimated, commonly modelled as impulse interventions. Statistical inference in these cases is sought, hindering the use of NNs. This paper proposes a framework to allow statistical inference of impulse interventions modelled with NNs. The effects of interventions are estimated and tested for statistical significance. Using a Monte Carlo simulation the power of the proposed test is compared with dynamic linear regression models. The power is found to be higher and the estimation of the simulated effects is more accurate. Based on this framework strategies to code multiple impulses with NNs are discussed.

Published
2010

40. Forecasting seasonal time series with multilayer perceptrons - an empirical evaluation of input vector specifications for deterministic seasonality

Author

Stahlbock, Robert, Crone, Sven F., Lessmann, Stefan, Crone, S., Kourentzes, N., Stahlbock, Robert, Crone, Sven F., Lessmann, Stefan, Crone, S., and Kourentzes, N.

Abstract

Research in forecasting with Neural Networks (NN) has provided contradictory evidence on their ability to model seasonal time series. Several empirical studies have concluded that time series should be deseasonalised prior to modelling, with contradictory evidence pointing to adequate selection of input vectors. However, the nature of seasonality itself has not been considered: econometric theory suggests that deterministic seasonality and stochastic seasonality need to be modelled differently, with only the latter requiring deseasonalisation. As prior research has failed to take the conditions of the underlying seasonality into consideration, this study explores how deterministic seasonality should be best modelled with NN to achieve accurate and robust forecasts. We consider different forms of modelling seasonality as autoregressive lags, through different encodings of explanatory variables and deseasonalisation. We evaluate the results regarding empirical accuracy and the parsimony of the input vector in order to limit the degrees of freedom, develop robust models and simplify the training of NN. Our findings are consistent with econometric literature, i.e. that no deseasonalisation is required for deterministic seasonality, and contributes to current research in NNs by identifying a more parsimonious coding of seasonality based on seasonal indices.

Published
2009

43. Proceedings of the 2005 International Conference on Data Mining, DMIN'05:Foreword

Author

Abachi, Hamid, Arabnia, Hamid R., Asif, Nadim, Badie, K., Chung, Ping Tsai, Crone, Sven F., Daimi, Kevin, Demetriou, Georgios A., Guo, Gongde, Hashemi, Ray R., He, Zengyou, Holmes, Dawn E., Ji, Xiang, Joshua, Rose, Kato, Hatsuhiko, Kitts, Brendan, Li, Kuan Ching, Mahmoudi, M. T., Mun, Youngsong, Puttagunta, Vasundhara, Rocha, Joao, Rodriguez, Abraham R., Saraee, Mohammed, Scime, Anthony, Seriai, Abdelhak Djamel, Stahlbock, Robert, Sun, Junping, Swiniarski, Roman W., Vasikarla, Shantaram, Wu, Yi Fang Brook, Zhang, Chaoyang, Zhang, Xiaolong, Abachi, Hamid, Arabnia, Hamid R., Asif, Nadim, Badie, K., Chung, Ping Tsai, Crone, Sven F., Daimi, Kevin, Demetriou, Georgios A., Guo, Gongde, Hashemi, Ray R., He, Zengyou, Holmes, Dawn E., Ji, Xiang, Joshua, Rose, Kato, Hatsuhiko, Kitts, Brendan, Li, Kuan Ching, Mahmoudi, M. T., Mun, Youngsong, Puttagunta, Vasundhara, Rocha, Joao, Rodriguez, Abraham R., Saraee, Mohammed, Scime, Anthony, Seriai, Abdelhak Djamel, Stahlbock, Robert, Sun, Junping, Swiniarski, Roman W., Vasikarla, Shantaram, Wu, Yi Fang Brook, Zhang, Chaoyang, and Zhang, Xiaolong

Published
2005

49. Genetically Constructed Kernels for Support Vector Machines.

Author

Haasis, Hans-Dietrich, Kopfer, Herbert, Schönberger, Jörn, Lessmann, Stefan, Stahlbock, Robert, and Crone, Sven

Abstract

Data mining for customer relationship management involves the task of binary classification, e.g. to distinguish between customers who are likely to respond to direct mail and those who are not. The support vector machine (SVM) is a powerful learning technique for this kind of problem. To obtain good classification results the selection of an appropriate kernel function is crucial for SVM. Recently, the evolutionary construction of kernels by means of meta-heuristics has been proposed to automate model selection. In this paper we consider genetic algorithms (GA) to generate SVM kernels in a data driven manner and investigate the potential of such hybrid algorithms with regard to classification accuracy, generalisation ability of the resulting classifier and computational efficiency. We contribute to the literature by: (1) extending current approaches for evolutionary constructed kernels; (2) investigating their adequacy in a real world business scenario; (3) considering runtime issues together with measures of classification effectiveness in a mutual framework. [ABSTRACT FROM AUTHOR]

Published
2006
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