Abstract
In a data-driven era, scientific discovery by querying integrated heterogeneous data is becoming a popular approach. However, most current search engines retrieve data using SQL, which only addresses part of the common data processing use cases in data discovery for scientific research. Besides, the expressive power of SQL and other popular languages can’t describe some simple but useful second order logic queries. Therefore, we first proposed an abstract data processing model which contains four components: data set, data source, data model, and analysis tool. Then, we introduced a unified data model and SimbaQL query language to describe the steps of data processing. At last, we studied two cases by describing the data processing using SimbaQL, and it turns out that SimbaQL can describe these tasks properly.
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Li, Y., Shen, Z., Li, J. (2019). SimbaQL: A Query Language for Multi-source Heterogeneous Data. In: Li, J., Meng, X., Zhang, Y., Cui, W., Du, Z. (eds) Big Scientific Data Management. BigSDM 2018. Lecture Notes in Computer Science(), vol 11473. Springer, Cham. https://doi.org/10.1007/978-3-030-28061-1_27
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DOI: https://doi.org/10.1007/978-3-030-28061-1_27
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