1. ZHAW digitalcollection
  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3214
Full metadata record
DC FieldValueLanguage
dc.contributor.authorStockinger, Kurt-
dc.contributor.authorBundi, Nils Andri-
dc.contributor.authorHeitz, Jonas-
dc.contributor.authorBreymann, Wolfgang-
dc.date.accessioned2019-06-19T09:23:26Z-
dc.date.available2019-06-19T09:23:26Z-
dc.date.issued2019-
dc.identifier.issn2196-1115de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/17286-
dc.description.abstractLarge financial organizations have hundreds of millions of financial contracts on their balance sheets. Moreover, highly volatile financial markets and heterogeneous data sets within and across banks world-wide make near real-time financial analytics very challenging and their handling thus requires cutting edge financial algorithms. However, due to a lack of data modeling standards, current financial risk algorithms are typically inconsistent and non-scalable. In this paper, we present a novel implementation of a real-world use case for performing large-scale financial analytics leveraging Big Data technology. We first provide detailed background information on the financial underpinnings of our framework along with the major financial calculations. Afterwards we analyze the performance of different parallel implementations in Apache Spark based on existing computation kernels that apply the ACTUS data and algorithmic standard for financial contract modeling. The major contribution is a detailed discussion of the design trade-offs between applying user-defined functions on existing computation kernels vs. partially re-writing the kernel in SQL and thus taking advantage of the underlying SQL query optimizer. Our performance evaluation demonstrates almost linear scalability for the best design choice.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofJournal of Big Datade_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectFinancial analyticsde_CH
dc.subjectQuery processingde_CH
dc.subjectPerformance evaluationde_CH
dc.subjectUser-defined functionde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.subject.ddc332: Finanzwirtschaftde_CH
dc.titleScalable architecture for big data financial analytics : user-defined functions vs. SQLde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.21256/zhaw-3214-
dc.identifier.doi10.1186/s40537-019-0209-0de_CH
zhaw.funding.euNode_CH
zhaw.issue46de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume6de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedInformation Engineeringde_CH
zhaw.funding.zhawLarge Scale Data-Driven Financial Risk Modellingde_CH
Appears in collections:Publikationen School of Engineering

Files in This Item:
File Description SizeFormat 
BigDataFinancialAnalyticsUDFvsSQL_JournalBigData.pdf2.16 MBAdobe PDFThumbnail
View/Open
Show simple item record
Stockinger, K., Bundi, N. A., Heitz, J., & Breymann, W. (2019). Scalable architecture for big data financial analytics : user-defined functions vs. SQL. Journal of Big Data, 6(46). https://doi.org/10.21256/zhaw-3214
Stockinger, K. et al. (2019) ‘Scalable architecture for big data financial analytics : user-defined functions vs. SQL’, Journal of Big Data, 6(46). Available at: https://doi.org/10.21256/zhaw-3214.
K. Stockinger, N. A. Bundi, J. Heitz, and W. Breymann, “Scalable architecture for big data financial analytics : user-defined functions vs. SQL,” Journal of Big Data, vol. 6, no. 46, 2019, doi: 10.21256/zhaw-3214.
STOCKINGER, Kurt, Nils Andri BUNDI, Jonas HEITZ und Wolfgang BREYMANN, 2019. Scalable architecture for big data financial analytics : user-defined functions vs. SQL. Journal of Big Data. 2019. Bd. 6, Nr. 46. DOI 10.21256/zhaw-3214
Stockinger, Kurt, Nils Andri Bundi, Jonas Heitz, and Wolfgang Breymann. 2019. “Scalable Architecture for Big Data Financial Analytics : User-Defined Functions vs. SQL.” Journal of Big Data 6 (46). https://doi.org/10.21256/zhaw-3214.
Stockinger, Kurt, et al. “Scalable Architecture for Big Data Financial Analytics : User-Defined Functions vs. SQL.” Journal of Big Data, vol. 6, no. 46, 2019, https://doi.org/10.21256/zhaw-3214.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.