This type of data will likely not have the same properties as real data but can still be useful for some purposes, such as debugging an R data analysis program, or some types of performance testing of software applications.įor some use cases, having high utility will matter quite a bit. For example, the analyst can make a simplifying assumption that the variables have normal distributions and “medium” correlations among them, and create data that way. Of course, the use of background knowledge works only when the analyst truly understands the phenomenon of interest.Īs a final example, when a process is new or not well understood by the analyst, and there is no real historical data to use, then an analyst can make some simple assumptions about the distributions and correlations among the variables involved in the process. If the analyst’s knowledge of the process is accurate, then the synthetic data will behave in a manner that is consistent with real-world data. In such a case, it is relatively straightforward to create a model and sample from background knowledge to generate synthetic data. It can also be knowledge of the statistical distribution of human traffic in a store based on years of experience. Simulations can be, for instance, gaming engines that create simulated (and synthetic) images of scenes or objects, or they can be simulation engines that generate shopper data with particular characteristics (say, age and gender) for people who walk past a store at different times of the day.īackground knowledge can be, for example, knowledge of how a financial market behaves that comes from textbook descriptions or the movements of stock prices under various historical conditions. These existing models can be statistical models of a process (developed through surveys or other data collection mechanisms) or they can be simulations. It is created by using existing models or the analyst’s background knowledge. The second type of synthetic data is not generated from real data. The first type is generated from actual/real datasets, the second type does not use real data, and the third type is a hybrid of these two. Using machine learning, it is possible to create realistic pictures of people who do not exist in the real world. Furthermore, images, videos, audio, and virtual environments are types of data that can be synthesized. Data can also be unstructured text, such as doctors’ notes, transcripts of conversations or online interactions by email or chat. For example, data can be structured data, as one would see in a relational database. We refer to the process of generating synthetic data as synthesis.ĭata in this context can mean different things. The degree to which a synthetic dataset is an accurate proxy for real data is a measure of utility. This means that if an analyst works with a synthetic dataset, they should get analysis results similar to what they would get with real data. The generated data was validated by comparing it against real industrial pipelines data.At a conceptual level, synthetic data is not real data, but data that has been generated from real data and that has the same statistical properties as the real data. All pipelines formation components properties were studied and the relationships among them were identified. Real industrial pipelines data was used to build the industrial pipelines data generatorĪn industrial pipelines project database was analysed. The generator produces industrial pipelines data similar to real pipelines. Real industrial pipelines data are used in developing the industrial pipelines data generator. Industrial pipelines data generator is developed using Markov Chain model. Construction Engineering and Operational Researchĭataset generation for construction engineering optimization problems
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