Abstract
Given two data sets of user preferences and product attributes in addition to a set of query products, the aggregate reverse rank (ARR) query returns top-k users who regard the given query products as the highest aggregate rank than other users.
In this paper, we reveal two limitations of the state-of-the-art solution to ARR query; that is, (a) It has poor efficiency when the distribution of the query set is dispersive. (b) It processes a lot of user data. To address these limitations, we develop a cluster-and-process method and a sophisticated indexing strategy. From the theoretical analysis of the results and experimental comparisons, we conclude that our proposals have superior performance.
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Notes
- 1.
Without loss of generality, we assume that the minimum values are preferable.
References
Dong, Y., Chen, H., Furuse, K., Kitagawa, H.: Aggregate reverse rank queries. In: Hartmann, S., Ma, H. (eds.) DEXA 2016. LNCS, vol. 9828, pp. 87–101. Springer, Cham (2016). doi:10.1007/978-3-319-44406-2_8
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Acknowledgement
This research was partly supported by the program “Research and Development on Real World Big Data Integration and Analysis” of RIKEN, Japan.
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Dong, Y., Chen, H., Furuse, K., Kitagawa, H. (2017). Efficient Processing of Aggregate Reverse Rank Queries. In: Benslimane, D., Damiani, E., Grosky, W., Hameurlain, A., Sheth, A., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2017. Lecture Notes in Computer Science(), vol 10438. Springer, Cham. https://doi.org/10.1007/978-3-319-64468-4_12
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DOI: https://doi.org/10.1007/978-3-319-64468-4_12
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