Therefore, most of the problems caused by separating data production process from preparation of the data are overcome.
Therefore, the problems, caused by separating spatial data production process from preparation of this data, are overcome as far as possible.
For an overview of the data production process and the main types of possible analyses see, e.g. Rey (2016).
Expressing the uncertainty associated with these measurements or reducing the uncertainty in the data production process is a critical first step to achieving more definitive measures and communication.
Evaluating the quality of scientific information used in regulatory decision making requires that judgments be made about data production processes, data reporting, analysis, and interpretation methods and data applicability relative to the goals of the decision-making activity in question.
For the first time, data about production processes, sales, customer interactions, and more were recorded, aggregated, and analyzed.
Since the data of production processes cannot be precisely measured in some cases, the uncertain theory has played an important role in DEA.
Based on the open production data, the detailed production process, characteristics and future prospects are re-constructed and numerically discussed, so as to provide a general view of the production behaviors and potential prediction in this region.
Global land cover (GLC) data production and verification process is very complicated, time consuming and labor intensive, requiring huge amount of imagery data and ancillary data and involving many people, often from different geographic locations.
Furthermore, three key enabling technologies are discussed, i.e., interconnection and interoperability among different devices, industrial big data analysis for production process management and control, and intelligent decision-making based on knowledge acquisition and learning methodology.
The link between the decision and the true implicit system that generates the data (measurement system, production process, category of samples, etc). is a representation of this uncertainty as a probability distribution.
