Abstract
Statistical database systems are designed to answer queries on summarized data (or macro data), while queries on raw records are not allowed in such database systems. As macro data can offer aggregate information about the database, it is also an effective way to use statistical queries to provide analytical results in semantic databases. However, traditional statistical databases are proposed for security protection, i.e., hiding the raw records from user queries. Few studies are toward query optimizations on aggregate queries in statistical databases. In this paper, we propose a new process-in-memory (PIM) based processing scheme called agile query for accelerating queries in statistical databases. We present two new designs in the agile query. First, we propose an in-memory index to cache aggregate operators (e.g., sum, min, max, count, and average) in the main memory. The aggregate queries that hit in the in-memory index can be evaluated in the memory and no I/O operation will be incurred. Second, we propose to incrementally update the in-memory operator index so that we can ensure the consistency between the cached data and the original data records. We implement the agile query processing framework on top of MySQL and conduct experiments over various sizes of datasets to compare our design with the traditional method in MySQL. The results show that our proposal achieves up to 9 times higher throughput than MySQL under the skewed Zipf query set, and averagely gets about 2 times higher throughput under the random and uniform distributed queries.
Keywords
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Gupta, A.: Statistical data management. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of Database Systems, 2nd edn. Springer, Boston (2018)
Rafanelli, M., Shoshani, A.: Storm: a statistical object representation model. In: Michalewicz, Z. (ed.) SSDBM 1990. LNCS, vol. 420, pp. 14–29. Springer, Heidelberg (1990). https://doi.org/10.1007/3-540-52342-1_18
Shoshani, A.: OLAP and statistical databases: similarities and differences. In: PODS, pp. 185–196 (1997)
Brankovic, L., Giggins, H.: Statistical database security. In: Petković, M., Jonker, W. (eds.) Security, Privacy, and Trust in Modern. Data Management Data-Centric Systems and Applications, pp. 167–181. Springer, Heidelberg (2007)
Shoshani, A., Olken, F., Wong, H.K.T.: Characteristics of scientific databases. In: VLDB, pp. 147–160 (1984)
Shoshani A. Statistical databases: characteristics, problems, and some solutions. In: VLDB, pp. 208–222 (1982)
Shoshani, A., Wong, H.K.T.: Statistical and scientific database issues. IEEE Trans. Software Eng. 10, 1040–1047 (1985)
Lu, H., Vaidya, J., et al.: Statistical database auditing without query denial threat. INFORMS J. Comput. 27(1), 20–34 (2015)
Ryan, J.: A brief survey on the contribution of Mirka Miller to the security of statistical databases. Math. Comput. Sci. 12(3), 255–262 (2018)
Domingo-Ferrer, J.: Inference control in statistical databases. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of Database Systems, 2nd edn. Springer, Boston (2018)
Aggarwal, C.C., Yu, P.S.: A condensation approach to privacy preserving data mining. In: Bertino, E., et al. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 183–199. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24741-8_12
Skinner, G., Chang, E., et al.: Shield privacy Hippocratic security method for virtual community. In: IECON, pp. 472–479 (2004)
Baranczyk, S., Konik, R., et al.: Forecasting query access plan obsolescence: U.S. Patent 9, 990, 396 (2018)
Xike, X., Xingjun, H., Torben, P., Peiquan, J., Jinchuan, C.: OLAP over probabilistic data cubes I: Aggregating, materializing, and querying. In: ICDE, pp. 799–810 (2016)
Cormode, G., Korn, F., Muthukrishnan, S., Srivastava, D.: Summarizing two-dimensional data with skyline-based statistical descriptors. In: Ludäscher, B., Mamoulis, N. (eds.) SSDBM 2008. LNCS, vol. 5069, pp. 42–60. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69497-7_6
Gemulla, R., Rösch, P., Lehner, W.: Linked bernoulli synopses: sampling along foreign keys. In: Ludäscher, B., Mamoulis, N. (eds.) SSDBM 2008. LNCS, vol. 5069, pp. 6–23. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69497-7_4
Singh, S., Mayfield, C., Shah, R., Prabhakar, S., Hambrusch, S.: Query selectivity estimation for uncertain data. In: Ludäscher, B., Mamoulis, N. (eds.) SSDBM 2008. LNCS, vol. 5069, pp. 61–78. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69497-7_7
Wang, F., Agrawal, G., Jin, R.: Query planning for searching inter-dependent deep-web databases. In: Ludäscher, B., Mamoulis, N. (eds.) SSDBM 2008. LNCS, vol. 5069, pp. 24–41. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69497-7_5
Zhi, L., Peiquan, J., Xuan, S., et al.: CCF-LRU: a new buffer replacement algorithm for flash memory. IEEE Trans. Consum. Electron. 55(3), 1351–1359 (2009)
Peiquan, J., Xike, X., Na, W., Lihua, Y.: Optimizing R-tree for flash memory. Expert Syst. Appl. 42(10), 4676–4686 (2015)
Peiquan, J., Yi, O., Theo, H., Zhi, L.: AD-LRU: an efficient buffer replacement algorithm for flash-based databases. Data Knowl. Eng. 72, 83–102 (2012)
Chen, K., Jin, P., Yue, L.: A novel page replacement algorithm for the hybrid memory architecture involving PCM and DRAM. In: Hsu, C.-H., Shi, X., Salapura, V. (eds.) NPC 2014. LNCS, vol. 8707, pp. 108–119. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44917-2_10
Wu, Z., Jin, P., Yang, C., Yue, L.: APP-LRU: a new page replacement method for PCM/DRAM-based hybrid memory systems. In: Hsu, C.-H., Shi, X., Salapura, V. (eds.) NPC 2014. LNCS, vol. 8707, pp. 84–95. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44917-2_8
Talati, N., Ali, A., et al.: Practical challenges in delivering the promises of real processing-in-memory machines. In: DATE, pp. 1628–1633 (2018)
Ahn, J., Yoo, S., Mutlu, O., et al.: PIM-enabled instructions: a low-overhead, locality-aware processing-in-memory architecture. In: ISCA, pp. 336–348 (2015)
Acknowledgements
This work is partially supported by the National Key Research and Development Program of China (2018YFB0704404) and the National Science Foundation of China (61672479).
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Lu, S., Jin, P., Mu, L., Wan, S. (2019). Agile Query Processing in Statistical Databases: A Process-In-Memory Approach. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_64
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