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  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
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dc.contributor.authorGeiger, Melanie-
dc.contributor.authorStockinger, Kurt-
dc.date.accessioned2019-07-31T13:02:07Z-
dc.date.available2019-07-31T13:02:07Z-
dc.date.issued2019-
dc.identifier.isbn978-3-030-11820-4de_CH
dc.identifier.isbn978-3-030-11821-1de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/17803-
dc.description.abstractWith the growing trend of digitalization, many companies plan to use machine learning to improve their business processes or to provide new data-driven services. These companies often collect data from different locations with sometimes conflicting context. However, before machine learning can be applied, heterogeneous datasets often need to be integrated, harmonized, and cleaned. In other words, a data warehouse is often the foundation for subsequent analytics tasks. In this chapter, we first provide an overview on best practices of building a data warehouse. In particular, we describe the advantages and disadvantage of the major types of data warehouse architectures based on Inmon and Kimball. Afterwards, we describe a use case on building an e-commerce application where the users of this platform are provided with information about healthy products as well as products with sustainable production. Unlike traditional e-commerce applications, where users need to log into the system and thus leave personalized traces when they search for specific products or even buy them afterwards, our application allows full anonymity of the users in case they do not want to log into the system. However, analyzing anonymous user interactions is a much harder problem than analyzing named users. The idea is to apply modern data warehousing, big data technologies, as well as machine learning algorithms to discover patterns in the user behavior and to make recommendations for designing new products.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofApplied data science : lessons learned for the data-driven businessde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectData warehousingde_CH
dc.subjectMachine learningde_CH
dc.subjectQuery processingde_CH
dc.subjectDatabasede_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.titleData warehousing and exploratory analysis for market monitoringde_CH
dc.typeBuchbeitragde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
zhaw.publisher.placeChamde_CH
dc.identifier.doi10.1007/978-3-030-11821-1_18de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end351de_CH
zhaw.pages.start333de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewEditorial reviewde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedInformation Engineeringde_CH
zhaw.funding.zhawMarket Monitoringde_CH
zhaw.author.additionalNode_CH
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Geiger, M., & Stockinger, K. (2019). Data warehousing and exploratory analysis for market monitoring. In Applied data science : lessons learned for the data-driven business (pp. 333–351). Springer. https://doi.org/10.1007/978-3-030-11821-1_18
Geiger, M. and Stockinger, K. (2019) ‘Data warehousing and exploratory analysis for market monitoring’, in Applied data science : lessons learned for the data-driven business. Cham: Springer, pp. 333–351. Available at: https://doi.org/10.1007/978-3-030-11821-1_18.
M. Geiger and K. Stockinger, “Data warehousing and exploratory analysis for market monitoring,” in Applied data science : lessons learned for the data-driven business, Cham: Springer, 2019, pp. 333–351. doi: 10.1007/978-3-030-11821-1_18.
GEIGER, Melanie und Kurt STOCKINGER, 2019. Data warehousing and exploratory analysis for market monitoring. In: Applied data science : lessons learned for the data-driven business. Cham: Springer. S. 333–351. ISBN 978-3-030-11820-4
Geiger, Melanie, and Kurt Stockinger. 2019. “Data Warehousing and Exploratory Analysis for Market Monitoring.” In Applied Data Science : Lessons Learned for the Data-Driven Business, 333–51. Cham: Springer. https://doi.org/10.1007/978-3-030-11821-1_18.
Geiger, Melanie, and Kurt Stockinger. “Data Warehousing and Exploratory Analysis for Market Monitoring.” Applied Data Science : Lessons Learned for the Data-Driven Business, Springer, 2019, pp. 333–51, https://doi.org/10.1007/978-3-030-11821-1_18.


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