As the fundamental phrase of collecting and analyzing data, data integration is used in many applications, such as data cleaning, bioinformatics and pattern recognition. In big data era, one of the major problems of data integration is to obtain the global schema of data sources since the global schema could be hardly derived from massive data sources directly. In this paper, we attempt to solve such schema integration problem. For different scenarios, we develop batch and incremental schema integration algorithms. We consider the representation difference of attribute names in various data sources and propose ED Join and Semantic Join algorithms to integrate attributes with different representations. Extensive experimental results demonstrate that the proposed algorithms could integrate schemas efficiently and effectively.
CITATION STYLE
Li, T., Guo, H., Yang, D., Li, M., Zheng, B., & Wang, H. (2024). Schema Integration on Massive Data Sources. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 14488 LNCS, pp. 186–206). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-97-0801-7_11
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