China 
Africa 
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The three-axis approach to analytics projects
Published in Ondřej Bothe, Ondřej Kubera, David Bednář, Martin Potančok, Ota Novotný, Data Analytics Initiatives, 2022
Ondřej Bothe, Ondřej Kubera, David Bednář, Martin Potančok, Ota Novotný
In the case of b), the answer is not as straightforward as for a). The technical problem will not be as prevalent; we have various options for connecting to data, but a few will cause us real challenges. A situation that will not pose a challenge will be when we have the drivers and connection string with all the necessary authentication in place and approval from the data steward/owner to use this data. At the opposite end of the scale, we have a situation in which we are connecting a new data source with no experience on the project or the data source side because it has not been done before. We are positioned somewhere in between for most analytics projects, which applies to every type – flat file, database, or API. We must always expect something to break, especially if our company ecosystem is extensive and contains many independent components.
Database querying using SQL
Published in Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton, Modern Data Science with R, 2021
Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton
It may be the case that all of the data that you have encountered thus far has been in a application-specific format (e.g., R, Minitab, SPSS, Stata or has taken the form of a single CSV (comma-separated value) file. This file consists of nothing more than rows and columns of data, usually with a header row providing names for each of the columns. Such a file is known as known as a flat file, since it consists of just one flat (e.g., two-dimensional) file. A spreadsheet application—like Excel or Google Sheets —allows a user to open a flat file, edit it, and also provides a slew of features for generating additional columns, formatting cells, etc. In R, the read_csv() command from the readr package converts a flat file database into a data.frame.
Semantic Technologies as Enabler
Published in Sarika Jain, Understanding Semantics-Based Decision Support, 2021
In the 1960s, before the advent of database models, files were used to represent machine-readable information, with records stored one per line in a text file. In the flat-file model (data level), data is stored but cannot be robustly searched and cannot represent connections or nesting. It does not allow working with a dynamic environment or extracting implicit data from the stored data. File-system descriptions could not satisfactorily solve the problem of concurrent access of data. For this reason, the file-based model vanished (approximately forty years ago), because it could not accommodate the storage of real-world entities semantically [Chihoub et al. 2020]. The only really significant surviving remnants of the flat-file database model are CSV files (comma-separated values) and Excel spreadsheets.
Microstructure modelling for metallic additive manufacturing: a review
Published in Virtual and Physical Prototyping, 2020
Joel Heang Kuan Tan, Swee Leong Sing, Wai Yee Yeong
In this review, the focus is on the microstructural evolutions during the AM processes. The results from these models have the potential to be fed and linked to mechanical properties prediction of the AM parts. Recently, Ge et al. used an integrated modelling of process–structure–property relationship in laser AM for duplex titanium alloy. It is found that the formation of larger grain sizes and more α phase are due to higher scanning speed. This higher composition of α phase is then linked to higher mechanical properties (Ge et al. 2019). At current stage, most of the work done has linked the thermal simulations to the residual stresses in AM parts (Ding et al. 2011; Heigel, Michaleris, and Reutzel 2015; Yang et al. 2016; Zhao et al. 2017). The main challenge to link the different models together to achieve the microstructure to mechanical properties relation is the exchange of information between the individual model. Yan et al. have proposed to use a fixed format, flat-file database as a solution (Yan et al. 2018; Yan et al. 2018). However, more efficient approaches for information exchange have to be developed (Smith et al. 2016).