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Social media studies often collect data retrospectively to analyze public opinion. Social media data may decay over time and such decay may prevent the collection of the complete dataset. As a result, the collected dataset may differ from the complete data ...
Recent years have seen an exponential increase in the amount of data available in all sciences and application domains. Macroecology is part of this "Big Data" trend, with a strong rise in the volume of data that we are using for our research. Here, we sum ...
Data cleaning has become an indispensable part of data analysis due to the increasing amount of dirty data. Data scientists spend most of their time preparing dirty data before it can be used for data analysis. Existing solutions that attempt to automate t ...
Core to many scientific and analytics applications are spatial data capturing the position or shape of objects in space, and time series recording the values of a process over time. Effective analysis of such data requires a shift from confirmatory pipelin ...
Data cleaning is a time-consuming process that depends on the data analysis that users perform. Existing solutions treat data cleaning as a separate offline process that takes place before analysis begins. Applying data cleaning before analysis assumes a p ...
In the past two decades, the use of ontologies has been proven to be an effective tool for enriching existing information systems in the digital data modelling domain and exploiting those assets for semantic interoperability. With the rise of Industry 4.0, ...
The 1987 Hirschigrabe landslide was triggered by a rain intensive period and formed a natural dam to the Zulg river. The natural dam did not break and as such, only the uncertainties concerning the security of some houses within the surrounding settlement ...
A crucial building block of responsible artificial intelligence is responsible data governance, including data collection. Its importance is also underlined in the latest EU regulations. The data should be of high quality, foremost correct and representati ...
Data cleaning is a time-consuming process that depends on the data analysis that users perform. Existing solutions treat data cleaning as a separate offline process that takes place before analysis begins. Applying data cleaning before analysis assumes a p ...
Data are essential to urban building energy models and yet, obtaining sufficient and accurate building data at a large-scale is challenging. Previous studies have highlighted that the data impact on urban case studies has not been sufficiently discussed. T ...
