Name
Abstract | ||
|---|---|---|
With the advent of Industry 4.0, many companies aim at analyzing historically collected or operative transaction data. Despite the availability of large amounts of data, particular missing values can introduce bias or preclude the use of specific data analytics methods. Historically, a lot of research into missing data comes from the social sciences, especially with respect to survey data, whereas little research work deals with industrial missing data. In this paper, we (1) describe challenges that occur with missing data in the context of industrial data analytics, and (2) present an approach for handling missing data in industrial databases, which has been applied at voestalpine Stahl GmbH. In addition, we have evaluated different methods to impute missing values in our application data. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICDIM.2018.8846984 | 2018 Thirteenth International Conference on Digital Information Management (ICDIM) |
Keywords | Field | DocType |
Missing Data,Missing Values,Industrial Data Analytics,Data Quality,Multiple Imputation,Data Preparation | Survey data collection,Data analysis,Information retrieval,Computer science,Distributed database,Missing data,Transaction data | Conference |
ISBN | Citations | PageRank |
978-1-5386-5245-9 | 2 | 0.37 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lisa Ehrlinger | 1 | 2 | 1.38 |
| Thomas Grubinger | 2 | 9 | 1.52 |
| Bence Varga | 3 | 2 | 0.37 |
| Mario Pichler | 4 | 3 | 0.75 |
| T Natschläger | 5 | 1199 | 102.98 |
| Jürgen Zeindl | 6 | 2 | 0.37 |