Abstract
This work addresses a novel variant of the Maximum Range-Sum (MaxRS) query for settings in which spatial point objects occur dynamically and, upon occurrence, their significance (i.e., weight) decays over time. The objective of the original MaxRS query is to find a location to place (the centroid of) a fixed-size spatial rectangle so that the sum of the weights of the point objects in its interior is maximized. The unique aspect of the problem studied in this paper, which we call DDW-MaxRS (Dynamic and Decaying Weights MaxRS), is that the placement of its solution can vary over time due to the joint impact of the arrival of new objects and the change of the corresponding weights of the existing objects over time. To improve the efficiency of the DDW-MaxRS problem processing, we propose a memory-efficient approximate algorithmic solution that will naturally infuse uncertainty in the answer. We formally analyze the error bounds’ properties and provide experimental results to quantify the effectiveness of the proposed approach.
Research supported by the NSF SWIFT grant 2030249.
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Notes
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Implementation and the datasets used are publicly available at https://github.com/Ashraf-T/DDW-MaxRS.
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References
Agarwal, P.K., Kumar, N., Sintos, S., Suri, S.: Range-max queries on uncertain data. J. Comput. Syst. Sci. 94, 118–134 (2018)
Amagata, D., Hara, T.: Monitoring MaxRS in spatial data streams. In: EDBT, pp. 317–328 (2016)
Boulos, M.N.K., et al.: Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples. Int. J. Health Geograph. 10 (2011)
Chen, Z., Liu, Y., Wong, R.C., Xiong, J., Cheng, X., Chen, P.: Rotating MaxRS queries. Inf. Sci. 305 (2015)
Choi, D.W., Chung, C.W., Tao, Y.: A scalable algorithm for maximizing range sum in spatial databases. Proc. VLDB Endow. 5(11), 1088–1099 (2012)
Choi, D., Chung, C., Tao, Y.: Maximizing range sum in external memory. ACM Trans. Database Syst. 39(3), 21:1–21:44 (2014)
Eldawy, A., Mokbel, M.F.: The era of big spatial data: a survey. Found. Trends Databases 6(3–4), 163–273 (2016)
Güting, R.H., Schneider, M.: Moving Objects Databases. Morgan Kaufmann, San Francisco (2005)
Hussain, M.M., Mostafiz, M.I., Mahmud, S.M.F., Trajcevski, G., Ali, M.E.: Conditional MaxRS query for evolving spatial data. Front. Big Data 3, 20 (2020)
Imai, H., Asano, T.: Finding the connected components and a maximum clique of an intersection graph of rectangles in the plane. J. Algorithms 4(4), 310–323 (1983)
Koutsopoulos, I.: Optimal incentive-driven design of participatory sensing systems. In: Proceedings of the IEEE INFOCOM (2013)
Liu, Q., Lian, X., Chen, L.: Probabilistic maximum range-sum queries on spatial database. In: Proceedings of the 27th ACM SIGSPATIAL (2019)
LLC, S.: Home Burglay awareness and prevention (2013). http://www.jsu.edu/police/docs/Schoolsafety.pdf
Mishra, A.: Data science: The key tool cities need to reduce carbon emissions (2020)
Nandy, S.C., Bhattacharya, B.B.: A unified algorithm for finding maximum and minimum object enclosing rectangles and cuboids. Comput. Math. Appl. 29(8), 45–61 (1995)
Phan, T., Jung, H., Youn, H.Y., Kim, U.: Efficient evaluation of maximizing range sum queries in a road network. IEICE Trans. Inf. Syst. 99-D(5), 1326–1336 (2016)
Prieto Curiel, R., Collignon Delmar, S., Bishop, S.R.: Measuring the distribution of crime and its concentration. J. Quant. Criminol. 34 (2018)
Silva, T.H., et al.: Urban computing leveraging location-based social network data: a survey. ACM Comput. Surv. 52(1), 17:1–17:39 (2019)
Singru, N., Lumain, R., Roldan, C.: Reducing Carbon Emissions from Transport Projects - ADB Strategy 2020 (2010)
Siow, E., Tiropanis, T., Hall, W.: Analytics for the internet of things: a survey. ACM Comput. Surv. 51(4), 74:1–74:36 (2018)
Subramanian, R., He, Q., Pascual, M.: Quantifying asymptomatic infection and transmission of COVID-19 in New York city using observed cases, serology, and testing capacity. In: Proceedings of the National Academy of Sciences of the United States of America, vol. 118 (2021)
Tao, Y., Hu, X., Choi, D.W., Chung, C.W.: Approximate MaxRS in spatial databases. In: 39th International Conference on Very Large Data Bases 2013, VLDB 2013. vol. 6, pp. 1546–1557 (2013)
Wan, L., Ng, W.K., Dang, X.H., Yu, P.S., Zhang, K.: Density-based clustering of data streams at multiple resolutions. ACM Trans. Knowl. Discov. Data (TKDD) 3(3), 1–28 (2009)
Yang, D., Zhang, D., Zheng, V.W., Yu, Z.: Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNS. IEEE Trans. Syst. Man Cybern. Syst. 45(1), 129–142 (2015)
Zhang, J., Zhu, M., Papadias, D., Tao, Y., Lee, D.L.: Location-based spatial queries. In: Proceedings of the 2003 ACM SIGMOD, pp. 443–454. ACM (2003)
Zhou, X., Wang, W., Xu, J.: General purpose index-based method for efficient MaxRS query. In: Hartmann, S., Ma, H. (eds.) DEXA 2016. LNCS, vol. 9827, pp. 20–36. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44403-1_2
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Tahmasbi, A., Trajcevski, G. (2022). Maximum Range-Sum for Dynamically Occurring Objects with Decaying Weights. In: Chiusano, S., Cerquitelli, T., Wrembel, R. (eds) Advances in Databases and Information Systems. ADBIS 2022. Lecture Notes in Computer Science, vol 13389. Springer, Cham. https://doi.org/10.1007/978-3-031-15740-0_18
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