Abstract
There are different types of join operation dealing with different issues in database research. However, existing join operations cannot meet the increasing demands of the real world. In this paper, we define a new join operation, the preference join (p-join), which introduces the concepts of the personal preference and the satisfaction operator on various data types. We present a general join algorithm (Nested Loop) to deal with the p-join, and we also propose an advanced algorithm called MFV for p-join. To improve the MFV algorithm, two enhanced mapping methods are employed. A large number of experiments on both real-world and synthetic data sets are conducted. The experimental results demonstrate the effectiveness, efficiency and scalability of our methods, and show the advanced algorithms have advantages over the general algorithms.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (No. 61373023, No. 61170064).
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Wang, C., Wang, C., Wang, H., Chen, J., Ye, X. (2016). Preference Join on Heterogeneous Data. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9932. Springer, Cham. https://doi.org/10.1007/978-3-319-45817-5_25
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DOI: https://doi.org/10.1007/978-3-319-45817-5_25
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