| Publication type: | Book part |
| Type of review: | Editorial review |
| Title: | Data warehousing and exploratory analysis for market monitoring |
| Authors: | Geiger, Melanie Stockinger, Kurt |
| et. al: | No |
| DOI: | 10.1007/978-3-030-11821-1_18 |
| Published in: | Applied data science : lessons learned for the data-driven business |
| Page(s): | 333 |
| Pages to: | 351 |
| Issue Date: | 2019 |
| Publisher / Ed. Institution: | Springer |
| Publisher / Ed. Institution: | Cham |
| ISBN: | 978-3-030-11820-4 978-3-030-11821-1 |
| Language: | English |
| Subjects: | Data warehousing; Machine learning; Query processing; Database |
| Subject (DDC): | 005: Computer programming, programs and data |
| Abstract: | With 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/17803 |
| Fulltext version: | Published version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Computer Science (InIT) |
| Published as part of the ZHAW project: | Market Monitoring |
| Appears in collections: | Publikationen School of Engineering |
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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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