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A MapReduce approach for spatial co-location pattern mining via ordered-clique-growth

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Abstract

Spatial co-location pattern is a subset of spatial features whose instances are frequently located together in geography. Mining co-location patterns are particularly valuable for discovering spatial dependencies. Traditional co-location pattern mining algorithms are computationally expensive with rapidly increasing of data volume. In this paper, we explore a novel iterative framework based on parallel ordered-clique-growth for co-location pattern mining. The ordered clique extension can re-use previously processed information and be executed in parallel, and hence speed up the identification of co-location instances. Based on the iterative framework, a MapReduce algorithm is designed to search for prevalent co-location patterns in a level-wise manner, namely PCPM_OC. To narrow the search space of ordered cliques, two pruning techniques are suggested for filtering invalid clique instances as much as possible. The completeness and correctness of PCPM_OC are proven and we also discuss its complexity in this paper. Moreover, we compare PCPM_OC with two advanced MapReduce based co-location pattern mining algorithms on multiple perspectives. At last, substantial experiments are conducted on synthetic and real-world spatial datasets to study the performance of PCPM_OC. Experimental results demonstrate that PCPM_OC has a significant improvement in efficiency and shows better scalability on massive spatial data.

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References

  1. Shekhar, S., Huang, Y.: Discovering spatial co-location patterns: a summary of results. In: 7th International Symposium on Advances in Spatial and Temporal Databases (SSTD), pp. 236–256 (2001)

  2. Yoo, J.S., Shekhar, S.: A joinless approach for mining spatial colocation patterns. IEEE Trans. Knowl. Data Eng. 18(10), 1323–1337 (2006)

    Article  Google Scholar 

  3. Huang, Y., Shekhar, S., Xiong, H.: Discovering colocation patterns from spatial data Sets: a general approach. IEEE Trans. Knowl. Data Eng. 16(12), 1472–1485 (2004)

    Article  Google Scholar 

  4. Xiong, H., Shekhar, S., Huang, Y., Kumar, V., Ma, X., Yoo, J.S.: A framework for discovering co-location patterns in data sets with extended spatial objects. In: SIAM International Conference on Data Mining, pp. 1–13 (2004)

  5. Mohammad, A., Farhad, S., Robert, W.: A generic regional spatio-temporal co-occurrence pattern mining model: a case study for air pollution. J. Geogr. Syst. 17(3), 249–274 (2015)

    Article  Google Scholar 

  6. Fang, Y., Wang, L., Hu, T., Wang, X.: DFCPM: a dominant feature co-location pattern miner. In: APWEB/WAIM, pp. 456–460 (2018)

  7. Wang L., Bao X., Cao, L.: Interactive probabilistic post-mining of user-preferred spatial co-location patterns. In: IEEE International Conference on Data Engineering (ICDE), pp. 1256–1259 (2018)

  8. Yang, P., Zhang, T., Wang, L.: TSRS: trip service recommended system based on summarized co-location patterns. In: APWEB/WAIM, pp. 451–455 (2018)

  9. Yu, W.: Spatial co-location pattern mining for location-based services in road networks. Expert Syst. Appl. 46, 324–335 (2016)

    Article  Google Scholar 

  10. Li, J., Adilmagambetov, A., Jabbar, M.S.M., Osornio-Vargas, A., Wine, O.: On discovering co-location patterns in datasets: a casestudy of pollutants and child cancers. Geoinformatica 20(4), 651–692 (2016)

    Article  Google Scholar 

  11. Lu, J., Wang, L., Fang, Y., Zhao, J.: Mining strong symbiotic patterns hidden in spatial prevalent co-location patterns. Knowl. Based Syst. 146, 190–202 (2018)

    Article  Google Scholar 

  12. Lu, J., Wang, L., Fang, Y., Li, M.: Mining competitive pairs hidden in co-location patterns from dynamic spatial databases. In: Pacific Asia Knowledge Discovery and Data Mining (PAKDD), pp. 467–480 (2017)

  13. Yao, X., Chen, L., Peng, L., Chi, T.: A co-location pattern-mining algorithm with a density-weighted distance thresholding consideration. Inf. Sci. 396, 144–161 (2017)

    Article  Google Scholar 

  14. Wang, L., Bao, X., Zhou, L.: Redundancy reduction for prevalent co-location patterns. IEEE Trans. Knowl. Data Eng. 30(1), 142–155 (2018)

    Article  Google Scholar 

  15. Wang, L., Bao, X., Chen, H., Cao, L.: Effective lossless condensed representation and discovery of spatial co-location patterns. Inf. Sci. 436–437, 197–213 (2018)

    Article  MathSciNet  Google Scholar 

  16. Yang, P., Wang, L., Wang, X.: A parallel spatial co-location pattern mining approach based on ordered clique growth. In: International Conference on Database Systems for Advanced Applications (DASFAA), pp. 734–742 (2018)

  17. Andrzejewski, W., Boinski, P.: Efficient spatial co-location pattern mining on multiple GPUs. Expert Syst. Appl. 93, 465–483 (2018)

    Article  Google Scholar 

  18. Fang, Y., Wang, L., Wang, X., Zhou, L.: Mining co-location patterns with dominant features. In: International Conference on Web Information Systems Engineering (WISE), pp. 183–198 (2017)

  19. Fang, Y., Wang, L., Hu, T.: Spatial co-location pattern mining based on density peaks clustering and fuzzy theory. In: APWEB/WAIM, pp. 298–305 (2018)

  20. Ouyang, Z., Wang, L., Wu, P.: Spatial co-location pattern discovery from fuzzy objects. Int. J. Artif. Intell. Tools 26, 1750003 (2017). https://doi.org/10.1142/S0218213017500038

    Article  Google Scholar 

  21. Chan, H.K., Long, C., Yan, D., Wong, R.C. : Fraction-score: a new support measure for co-location pattern mining. In: IEEE International Conference on Data Engineering (ICDE), pp. 1514–1525 (2019)

  22. Wang, L., Bao, Y., Lu, J., Yip, J.: A new join-less approach for co-location pattern mining. In: 8th IEEE International Conference on Computer and Information Technology (CIT), pp. 197–202 (2008)

  23. Wang, L., Zhou, L., Lu, J., Yip, J.: An order-clique-based approach for mining maximal co-locations. Inf. Sci. 179(19), 3370–3382 (2009)

    Article  Google Scholar 

  24. Lin, Z., Lim, S.J.: Fast spatial co-location mining without cliqueness checking. In: International Conference on Information and Knowledge Management (CIKM), pp. 1461–1462 (2008)

  25. Yoo, J.S., Shekhar, S.: A partial join approach for mining co-location patterns. In: The 12th Annual ACM International Workshop on Geographic Information Systems, pp. 241–249 (2004)

  26. Yao, X., Peng, L., Yang, L., Chi, T.: A fast space-saving algorithm for maximal co-location pattern mining. Expert Syst. Appl. 63, 310–323 (2016)

    Article  Google Scholar 

  27. Xiao, X., Xie, X., Luo, Q., Ma, W.: Density based co-location pattern discovery. In: 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 1–10 (2008)

  28. Kim, S., K., Kim, Y., Kim, U.: Maximal cliques generating algorithm for spatial co-location pattern mining. In: Secure and Trust Computing, Data Management and Applications (STA), pp. 241–250 (2011)

  29. Yoo, J.S., Boulware, D., Kimmey, D.: A parallel spatial co-location mining algorithm based on MapReduce. In: IEEE International Congress on Big Data, pp. 25–31 (2014)

  30. Yang, P., Wang, L., Wang, X., Fang, Y.: A parallel joinless algorithm for co-location pattern mining based on group-dependent shard. In: International Conference on Web Information Systems Engineering (WISE), pp. 240–250 (2018)

  31. Zheng, B., Zheng, K., Jensen, C.S., Nguyen, Q.V.H., Su, H., Li, G., Zhou, X.: Answering why-not group spatial keyword queries. IEEE Trans. Knowl. Data Eng. (2019). https://doi.org/10.1109/TKDE.2018.2879819

    Article  Google Scholar 

  32. Zheng, B., Su, H., Hua, W., Zheng, K., Zhou, X., Li, G.: Efficient clue-based route search on road networks. IEEE Trans. Knowl. Data Eng. 29(9), 1846–1859 (2017)

    Article  Google Scholar 

  33. Zhao, Y., Shang, S., Wang, Y., Zheng, B., Nguyen, Q.V.H., Zheng, K.: REST: a reference-based framework for spatio-temporal trajectory compression. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 2797–2806 (2018)

  34. Zheng, K., Zhao, Y., Lian, D., Zheng, B., Liu, G., Zhou, X.: Reference-based framework for spatio-temporal trajectory compression and query processing. IEEE Trans. Knowl. Data Eng. (2019). https://doi.org/10.1109/TKDE.2019.2914449

    Article  Google Scholar 

  35. Zheng, B., Zheng, K., Xiao, X., Su, H., Yin, H., Zhou, X., Li, G.: Keyword-aware continuous kNN query on road networks. In: IEEE International Conference on Data Engineering (ICDE), pp. 871–882 (2016)

  36. Liu, J., Lemus, N.M., Pacitti, E., Porto, F., Valduriez, P.: Parallel computation of PDFs on big spatial data using spark. Distrib. Parallel Databases (2019). https://doi.org/10.1007/s10619-019-07260-3

    Article  Google Scholar 

  37. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: International Conference on Very Large Data Bases (VLDB), pp. 487–499 (1994)

  38. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ACM SIGMOD International Conference on Management of Data, pp. 1–12 (2000)

  39. Barua, S., Sander, J.: Mining statistically significant co-location and segregation patterns. IEEE Trans. Knowl. Data Eng. 26(5), 1185–1199 (2014)

    Article  Google Scholar 

  40. Cai, J., Liu, Q., Deng, M., Tang, J., He, Z.: Adaptive detection of statistically significant regional spatial co-location patterns. Comput. Environ. Urban Syst. 68, 53–63 (2018)

    Article  Google Scholar 

  41. Yao, X., Chen, L., Wen, C., Peng, L., Yang, L., Chi, T., Wang, X., Yu, W.: A spatial co-location mining algorithm that includes adaptive proximity improvements and distant instance references. Int. J. Geogr. Inf. Sci. 3, 1–26 (2018)

    Google Scholar 

  42. Andrzejewski, W., Boinski, P.: Parallel GPU-based plane-sweep algorithm for construction of iCPI-trees. J. Database Manage. 26(3), 1–20 (2015)

    Article  Google Scholar 

  43. Garaeva, A., Makhmutova, F., Anikin, I., Sattler, K.U.: A framework for co-location patterns mining in big spatial data. In: IEEE International Conference on Soft Computing & Measurements, pp. 477–480 (2017)

  44. Li, H., Wang, Y., Zhan, D., Zhang, M., Chang, E.: PFP: parallel FP-growth for query recommendation. In: ACM Conference on Recommender Systems, pp. 107–114 (2008)

  45. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. In: OSDI, pp. 137–150 (2004)

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (61472346, 61662086, 61762090), and the Project of Innovative Research Team of Yunnan Province (2018HC019).

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  1. School of Information Science and Engineering, Yunnan University, Kunming, 650091, China

    Peizhong Yang, Lizhen Wang & Xiaoxuan Wang

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  1. Peizhong Yang
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Correspondence to Lizhen Wang.

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Yang, P., Wang, L. & Wang, X. A MapReduce approach for spatial co-location pattern mining via ordered-clique-growth. Distrib Parallel Databases 38, 531–560 (2020). https://doi.org/10.1007/s10619-019-07278-7

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