Bein, Wolfgang W.

Pickl, Stefan Wolfgang

Tao, Fei

English

AIS Electronic Library (AISeL)

EDITORIALData Analytics and Optimization for Decision SupportWolfgang Bein • Stefan Pickl • Fei TaoPublished online: 10 April 2019 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019The aim of this special issue ‘‘Data Analytics and Opti-mization for Decision Support’’ is to present recentadvances at the intersection of data analytics and opti-mization. With the integration and pervasive applicationsof a new generation of information technology at anindustrial scale – driven by such advances as big data,cloud computing, mobile internet, Internet of Things andmachine learning – inevitably massive amounts of data aregenerated. Traditional decision methods, by comparison,use much less data.In this new setting, ‘‘data-driven optimization’’ as afresh approach for industrial optimization has been pro-posed. But the transformation of traditional mathematicaloptimization to a data-based optimization model is achallenging undertaking, in terms of collection and man-agement of relevant data, or the extraction and utilizationof useful information from ‘‘big data’’.With contributions from worldwide researchers, a totalof 17 papers were received. All papers underwent peerreview, and five papers were further revised and thenaccepted for publication. Broadly speaking the papers havein common that data mining techniques have been appliedin analysis, evaluation and optimization in the support ofdecision making and data-based intelligence.In the paper ‘‘Adaptive State Space Partitioning forDynamic Decision Processes’’, the authors propose anapproach which can adapt an architecture during approxi-mation for anticipatory decision making.The paper ‘‘Optimizing Data Stream Representation: AnExtensive Survey on Stream Clustering Algorithms’’explores, summarizes and categorizes a total of more than50 stream clustering algorithms and identifies core researchthreads over the past decades.The third paper, ‘‘An Adaptive Scheduling Algorithmfor Dynamic Jobs in Flexible Job Shop Scheduling Prob-lem’’ develops a clever algorithm to tackle makespanminimization for dynamic job shop scheduling. The pro-posed adaptive Heterogeneous Earliest Finish Time algo-rithm is shown to be significantly more efficient than whathas been traditionally available.In the fourth paper ‘‘Performance Analysis andEnhancement of Deep Convolutional Neural Network:Application to Gearbox Condition Monitoring’’, a com-prehensive neural network based framework for machinerycondition monitoring is presented.The contribution of the paper ‘‘Prognostic ModelDevelopment with Missing Labels – A Condition-BasedMaintenance Approach Using Machine Learning’’ is anovel approach to overcome the situation of missing labels.It is demonstrated how these techniques could be used toretrieve information that was only latently available in vastamounts of maintenance-related data.The special issue is complemented by an interview withGeneral Dr. Vad. It is discussed what changes does digi-tization bring about, especially in view of security,geopolitical, and strategic issues. Vad describes in detailthe challenges of digitization with regard to politicalProf. Dr. W. BeinDepartment of Computer Science, University of Nevada,Las Vegas, USAe-mail: wolfgang.bein@unlv.eduProf. Dr. S. Pickl (&)Department of Computer Science, Universität der BundeswehrMünchen, Munich, Germanye-mail: stefan.pickl@unibw.deProf. Dr. F. TaoSchool of Automation Science and Electrical Engineering,Beihang University, Beijing, Chinae-mail: ftao@buaa.edu.cn123Bus Inf Syst Eng 61(3):255–256 (2019)https://doi.org/10.1007/s12599-019-00591-6sovereignty and democracy. He states that security policyand strategic action take place ‘‘in the dark of incalcula-bility’’ – as Clausewitz once put it: This means that weneed more than only cognitive, rational intelligence. It alsohas a lot to do with empathy, emotionality and socialintelligence, which largely elude measurability… a newfield of intelligence analysis will emerge. This is also achallenge for Operations Research.The special issue has been made possible by the strongsupport of Prof. Martin Bichler, the Editor-in-Chief of thisjournal. We would like to express our gratitude to theauthors for their contributions, the referees for ensuring thequality of the accepted papers, and the staff of the EditorialOffice and Production Department for their help in final-izing this special issue.123256 W. Bein et al.: Data Analytics and Optimization, Bus Inf Syst Eng 61(3):255–256 (2019)

Data Analytics and Optimization for Decision Support

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Data Analytics and Optimization for Decision Support

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