Fink, Andreas

Kliewer, Natalia

Schryen, Guido

English

AIS Electronic Library (AISeL)

CALL FOR PAPERSCall for Papers Issue 1/2020High Performance Business ComputingGuido Schryen • Natalia Kliewer • Andreas FinkPublished online: 10 July 2018 Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 20181 Special IssueHigh Performance Business Computing (HPBC) is theapplication of models, methodologies, tools and technolo-gies of High Performance Computing (HPC) (e.g., Hagerand Wellein 2010) to business problems. While its neigh-bor discipline, High Performance Technical Computing,has already found entrance into a variety of scientific dis-ciplines, including meteorology, engineering, bio sciences,physics, chemistry and mathematics, the fields of man-agement science (MS) and information systems research(ISR) have largely neglected to start exploiting the poten-tials that HPC offer for business problems and addressingthe need of modern business computing for high computingpower and capability to deal with huge volumes of data.The scientific development and practical deployment ofHPBC is useful, if not even necessary, for essentially threereasons: First, the fields of MS and ISR become increasinglycomputational with the advent of large volumes of data and thepervasion of computing resources in business, scientific andsocial environments. Expectations and business opportunitiesthat result from these developments include the potentials ofreal-time decision making (e.g., for IT security infrastructureprotection andfinancial stock trading), solving computationallyhard optimization problems (e.g., in production and logistics)(e.g., Gendron and Crainic 1994; Aldasoro et al. 2017), andanalyzing huge volumes of data acquired from sensors, mobilephones, and social networks (e.g., for social network analysis,fraud detection and business analytics) (e.g., Wang et al. 2016;Zhang et al. 2016). The analysis of such data has already startedattracting new machine learning approaches. Second, HPCinfrastructures, tools, libraries and services are becomingcommodity goods offered by computing centers of universities,public cloud providers (e.g., as part of AmazonWeb services),and open source communities (e.g., Apache Hadoop frame-work). Third, the end of exponential growth in single processorcore performance, which has been a key driver of the economicand technology-advancement cycles over the past decades,marks the end of the dominance of the singlemicroprocessor incomputing, and the era of sequential computing must give wayto a new era in which parallelism is at the forefront (Fuller andMillet 2011). However, sequential programs (as models andmethodologies in general) will not benefit from increases inprocessor performance that stem from the use of parallel pro-cessing capabilities.This special issue accounts for the aforementionedchallenges of HPBC by calling for quantitative andcomputational studies in the areas of business opti-mization, data and business analytics, and machinelearning. Of particular interest are studies related to thedevelopment and computational validation of parallel ordistributed (1) (exact or heuristic) algorithms for opti-mization problems, (2) data analysis algorithms for busi-ness analytics, and (3) machine learning algorithms forbusiness analytics.Prof. Dr. G. Schryen (&)Universita¨t Paderborn, Warburger Straße 100, 33098 Paderborn,Germanye-mail: guido.schryen@upb.deURL: https://wiwi.uni-paderborn.de/dep3/schryen/Prof. Dr. N. KliewerFreie Universita¨t Berlin, Garystr. 21, 14195 Berlin, Germanye-mail: natalia.kliewer@fu-berlin.deURL: http://www.wiwiss.fu-berlin.de/kliewerProf. Dr. A. FinkInstitut fu¨r Informatik, Helmut-Schmidt-Universita¨t Hamburg,Holstenhofweg 85, 22043 Hamburg, Germanye-mail: andreas.fink@hsu-hamburg.deURL: https://www.hsu-hh.de/ifi/team/fink123Bus Inf Syst Eng 60(5):439–440 (2018)https://doi.org/10.1007/s12599-018-0549-x2 SubmissionAuthors are asked to submit their papers online under thecategory ‘‘BISE High Performance Business Computing’’by 2019-03-01 via Springer’s submission system EditorialManager (http://www.editorialmanager.com/buis/). Allpapers must follow the typing and formatting instructionsfor Business & Information Systems Engineering (BISE)available at http://www.bise-journal.com/?page_id=18. Inparticular, manuscripts should not exceed 50,000 charac-ters (discounting 5000 characters for each figure/table).Submitted papers will undergo a double-blind reviewprocess and be refereed by at least three domain expertsaccording to quality, originality, relevance, and scientificrigor.3 SchedulePaper submission due: 1 March 2019Notification of authors: 2 May 2019Revisions due: 1 July 2019Notification of authors: 15 August 2019Completion of a second revision (if needed): 19 September 2019Anticipated publication date: February 2020ReferencesAldasoro U, Escudero LF, Merino M, Perez G (2017) A parallelbranch-and-fix coordination based matheuristic algorithm forsolving large sized multistage stochastic mixed 0–1 problems.Eur J Oper Res 258:590–606Fuller SH, Millet LI (2011) The future of computing performance:game over or next level? In: Fuller SH, Millett LI (eds) Nationalresearch council of the national academies. National AcademiesPress, Washington, D.C.Gendron B, Crainic TG (1994) Parallel branch-and-bound algorithms:survey and synthesis. Oper Res 42(6):1042–1066Hager G, Wellein G (2010) Introduction to high performancecomputing for scientists and engineers. CRC Press, Boca RatonWang Q, Li B, Wang P, Yang J (2016) Using TB-sized data tounderstand multi-device advertising. In: Proceedings of theinternational conference on information systems, DublinZhang K, Bhattacharyya S, Ram S (2016) Large-scale networkanalysis for online social brand advertising. MIS Q40(4):849–868123440 G. Schryen et al.: Call for Papers Issue 1/2020, Bus Inf Syst Eng 60(5):439–440 (2018)

Call for Papers Issue 1/2020 - High Performance Business Computing

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Call for Papers Issue 1/2020 - High Performance Business Computing

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