Results 241 to 250 of about 6,519,550 (284)
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ACM SIGMOD Record, 2020
Raw data are often messy: they follow different encodings, records are not well structured, values do not adhere to patterns, etc. Such data are in general not fit to be ingested by downstream applications, such as data analytics tools, or even by data management systems.
Mazhar Hameed 0001, Felix Naumann
+5 more sources
Raw data are often messy: they follow different encodings, records are not well structured, values do not adhere to patterns, etc. Such data are in general not fit to be ingested by downstream applications, such as data analytics tools, or even by data management systems.
Mazhar Hameed 0001, Felix Naumann
+5 more sources
2018
The objective of this chapter is to present the rationale for data preparation and practical guidance for conducting the procedures in applied research. It is argued that the scope of data preparation is far greater than the application of now classic diagnostic procedures such as those outlined for the regression family.
Stefano Occhipinti, Caley Tapp
+6 more sources
The objective of this chapter is to present the rationale for data preparation and practical guidance for conducting the procedures in applied research. It is argued that the scope of data preparation is far greater than the application of now classic diagnostic procedures such as those outlined for the regression family.
Stefano Occhipinti, Caley Tapp
+6 more sources
Preparing complex data for warehousing
The 3rd ACS/IEEE International Conference onComputer Systems and Applications, 2005., 2005Summary form only given. In order to prepare complex data for relevant analysis, a data warehousing-based approach is needed. However, a good multidimensional modeling requires efficient preparation of data starting with a data integration phase. We present in this paper two principal steps of the complex data warehousing process.
Tanasescu, Adrian +2 more
openaire +1 more source
2007
Abstract This module’s focus has been on data and, so far, data considerations, data sources, scoring structure, and information sharing have been covered. This chapter covers data preparation, the first practical stage in the scorecard development process.
openaire +2 more sources
Abstract This module’s focus has been on data and, so far, data considerations, data sources, scoring structure, and information sharing have been covered. This chapter covers data preparation, the first practical stage in the scorecard development process.
openaire +2 more sources