A scalable spatial skyline evaluation system utilizing parallel independent region groups
- 64 Downloads
Abstract
This research presents two parallel solutions to efficiently address spatial skyline queries. First, we propose a novel concept called independent regions for parallelizing the process of spatial skyline evaluation. Spatial skyline candidates in an independent region do not depend on any data point in other independent regions. Then, we propose a GPU-based solution. We use multi-level independent region group-based parallel filter to support efficient multi-threading spatial skyline non-candidate elimination. Beyond that, we propose comparable region to accelerate non-candidate elimination in each independent region. Secondly, we propose a MapReduce-based solution. We generate the convex hull of query points in the first MapReduce phase. In the second phase, we calculate independent regions based on the input data points and the convex hull of the query points. With the independent regions, spatial skylines are evaluated in parallel in the third phase, in which data points are partitioned by their associated independent regions in map functions, and spatial skyline candidates are calculated by reduce functions. The results of the spatial skyline queries are the union of outputs from the reduce functions. Our experimental results show that GPU multi-threading scheme is very efficient on small-scale input datasets. On the contrary, MapReduce scheme performs very well on large-scale input datasets.
Keywords
Spatial skyline query MapReduce Parallel computation GPUNotes
Acknowledgements
This research has been funded in part by the National Science Foundation grants IIS-1618669 (III) and ACI-1642133 (CICI).
References
- 1.Börzsönyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: ICDE, pp. 421–430 (2001)Google Scholar
- 2.Tan, K-L., Eng, P.-K., Ooi, B.C.: Efficient progressive skyline computation. In: VLDB, pp. 301–310 (2001)Google Scholar
- 3.Kossmann, D., Ramsak, F., Rost, S.: Shooting stars in the sky: an online algorithm for skyline queries. In: VLDB, pp. 275–286 (2002)Google Scholar
- 4.Papadias, D., Tao, Y., Fu, G., Seeger, B.: Progressive skyline computation in database systems. ACM Trans. Database Syst. 30(1), 41–82 (2005)CrossRefGoogle Scholar
- 5.Chomicki, J., Godfrey, P., Gryz, J., Liang, D.: Skyline with presorting. In: ICDE, pp. 717–719 (2003)Google Scholar
- 6.Zhang, S., Mamoulis, N., Cheung, D.W.: Scalable skyline computation using object-based space partitioning. In: SIGMOD Conference, pp. 483–494 (2009)Google Scholar
- 7.Sarma, A.D., Lall, A., Nanongkai, D., Xu, J.: Randomized multi-pass streaming skyline algorithms. PVLDB 2(1), 85–96 (2009)Google Scholar
- 8.Huang, Z., Jensen, C.S., Lu, H., Ooi, B.C.: Skyline queries against mobile lightweight devices in MANETs. In: ICDE, p. 66 (2006)Google Scholar
- 9.Sharifzadeh, M., Shahabi, C.: The spatial skyline queries. In: VLDB, pp. 751–762 (2006)Google Scholar
- 10.Son, W., Lee, M.-W., Ahn, H.-K., Hwang, S.-W.: Spatial skyline queries: an efficient geometric algorithm. In: SSTD, pp. 247–264 (2009)Google Scholar
- 11.Hose, K., Vlachou, A.: A survey of skyline processing in highly distributed environments. VLDB J. 21(3), 359–384 (2012)CrossRefGoogle Scholar
- 12.Choi, W., Liu, L., Yu, B.: Multi-criteria decision making with skyline computation. In: Information Reuse and Integration (IRI), 2012 IEEE 13th International Conference, pp. 316–323. IEEE (2012)Google Scholar
- 13.Bøgh, K.S.. Assent, I., Magnani, M.: Efficient GPU-based skyline computation. In: Proceedings of the Ninth International Workshop on Data Management on New Hardware, p. 5. ACM (2013)Google Scholar
- 14.Lee, J., Hwang, S.-W.: Scalable skyline computation using a balanced pivot selection technique. Inf. Syst. 39, 1–21 (2014)CrossRefGoogle Scholar
- 15.Mullesgaard, K., Pedersen, J.L., Lu, H., Zhou, Y.: Efficient skyline computation in MapReduce. In: EDBT (2014)Google Scholar
- 16.Park, Y., Min, J.-K., Shim, K.: Parallel computation of skyline and reverse skyline queries using mapreduce. PVLDB 6(14), 2002–2013 (2013)Google Scholar
- 17.Zhang, J., Jiang, X., Ku, W.-S., Qin, X.: Efficient parallel skyline evaluation using MapReduce. IEEE Trans. Parallel Distrib. Syst. 27(7), 1996–2009 (2016)CrossRefGoogle Scholar
- 18.Zhang, C., Li, F., Jestes, J.: Efficient parallel kNN joins for large data in MapReduce. In: EDBT, pp. 38–49 (2012)Google Scholar
- 19.Vernica, R., Carey, M.J., Li, C.: Efficient parallel set-similarity joins using MapReduce. In: SIGMOD Conference, pp. 495–506 (2010)Google Scholar
- 20.Okcan, A., Riedewald, M.: Processing theta-joins using MapReduce. In: SIGMOD Conference, pp. 949–960 (2011)Google Scholar
- 21.Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
- 22.Bøgh, K.S., Chester, S., Assent, I.: Work-efficient parallel skyline computation for the GPU. Proc. VLDB Endow. 8(9), 962–973 (2015)CrossRefGoogle Scholar
- 23.Chazelle, B.: An optimal convex hull algorithm in any fixed dimension. Discrete Comput. Geom. 10(1), 377–409 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
- 24.Eldawy, A., Li, Y., Mokbel, M.F., Janardan, R.: CG\_Hadoop: Computational geometry in MapReduce. In: SIGSPATIAL, pp. 294–303 (2013)Google Scholar
- 25.de Berg, M., Cheong, O., van Kreveld, M., Overmars, M.: Computational Geometry: Algorithms and Applications, 3rd edn. Springer, New York (2008)CrossRefzbMATHGoogle Scholar
- 26.Apache, H.: http://hadoop.apache.org. Accessed 26 Apr 2016
- 27.Chester, S., Šidlauskas, D., Assent, I., Bøgh, K.S: Scalable parallelization of skyline computation for multi-core processors. In: 2015 IEEE 31st International Conference on Data Engineering, pp. 1083–1094. IEEE (2015)Google Scholar
- 28.Lee, S.-Y., Wu, C.-J.: Characterizing the latency hiding ability of GPUS. In: Performance Analysis of Systems and Software (ISPASS), 2014 IEEE International Symposium, pp. 145–146. IEEE (2014)Google Scholar
- 29.Balke, W.-T., Güntzer, U., Zheng, J. X.: Efficient distributed skylining for web information systems. In: EDBT, pp. 256–273 (2004)Google Scholar
- 30.Wu, P., Zhang, C., Feng, Y., Zhao, B.Y., Agrawal, D., El Abbadi, A.: Parallelizing skyline queries for scalable distribution. In: EDBT, pp. 112–130 (2006)Google Scholar
- 31.Cosgaya-Lozano, A., Rau-Chaplin, A., Zeh, N.: Parallel computation of skyline queries. In: HPCS, p. 12 (2007)Google Scholar
- 32.Afrati, F.N., Koutris, P., Suciu, D., Ullman, J.D.: Parallel skyline queries. In: ICDT, pp. 274–284 (2012)Google Scholar
- 33.Rocha-Junior, J.B., Vlachou, A., Doulkeridis, C., Nørvåg, K.: AGiDS: a grid-based strategy for distributed skyline query processing. In: Globe, pp. 12–23 (2009)Google Scholar
- 34.Vlachou, A., Doulkeridis, C., Kotidis, Y.: Angle-based space partitioning for efficient parallel skyline computation. In: SIGMOD Conference, pp. 227–238 (2008)Google Scholar
- 35.Köhler, H., Yang, J., Zhou, X.: Efficient parallel skyline processing using hyperplane projections. In: SIGMOD Conference, pp. 85–96 (2011)Google Scholar
- 36.Han, X., Li, J., Yang, D., Wang, J.: Efficient skyline computation on big data. IEEE Trans. Knowl. Data Eng. 25(11), 2521–2535 (2013)CrossRefGoogle Scholar
- 37.Zhang, B., Zhou, S., Guan, J.: Adapting skyline computation to the Mapreduce framework: algorithms and experiments. In: DASFAA Workshops, pp. 403–414 (2011)Google Scholar
- 38.Chen, L., Hwang, K., Wu, J.: Mapreduce skyline query processing with a new angular partitioning approach. In: IPDPS Workshops, pp. 2262–2270 (2012)Google Scholar
- 39.Yoon, S., Shahabi, C.: Distributed spatial skyline query processing in wireless sensor networks. In: Proceedings of the IPSN, San Francisco, CA, USA, pp. 13–16 (2009)Google Scholar
- 40.Wang, Y., Song, B., Wang, J., Zhang, L., Wang, L.: Geometry-based distributed spatial skyline queries in wireless sensor networks. Sensors 16(4), 454 (2016)CrossRefGoogle Scholar