Big spatial vector data management: a review

References

• Aji, A. , Wang, F. , Vo, H. , Lee, R. , Liu, Q. , Zhang, X. , & Saltz, J. (2013). Hadoop-GIS: A high performance spatial data warehousing system over MapReduce. Proceedings of the VLDB Endowment , 6 (11), 1009–1020.10.14778/2536222
   Google Scholar
• Al-Badarneh, A. , & Al-Alaj, A. (2011). A spatial index structure using dynamic recursive space partitioning. Paper presented at the International Conference on Innovations in Information Technology, Abu Dhabi, United Arab Emirates, April 25–27.
   Google Scholar
• Alarabi, L. , & Mokbel, M. F. (2017). A demonstration of ST-hadoop: A MapReduce framework for big spatio-temporal data. Proceedings of the VLDB Endowment , 10 (12), 1961–1964. doi:10.14778/3137765.3137819
   Web of Science ®Google Scholar
• Alelaiwi, A. (2017). A collaborative resource management for big IoT data processing in Cloud. Cluster Computing-the Journal of Networks Software Tools and Applications , 20 (2), 1791–1799. doi:10.1007/s10586-017-0839-y
   Web of Science ®Google Scholar
• Ali, S. M. , Gupta, N. , Nayak, G. K. , & Lenka, R. K. (2016). Big data visualization: Tools and challenges. Paper presented at the 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noida, India, December 14–17.
   Google Scholar
• Aly, A. M. , Mahmood, A. R. , Hassan, M. S. , Aref, W. G. , Ouzzani, M. , Elmeleegy, H. , & Qadah, T. (2015). AQWA: Adaptive query workload aware partitioning of big spatial data. Proceedings of the VLDB Endowment , 8 (13), 2062–2073. doi:10.14778/2831360.2831361
   Web of Science ®Google Scholar
• ArcGIS . (2017). GIS tools for Hadoop. ESRI. Retrieved September 25, from https://blogs.esri.com/esri/arcgis/2013/03/25/gis-tools-for-hadoop/
   Google Scholar
• Bellur, U. (2014). On parallelizing large spatial queries using map-reduce. Paper presented at the 13th International Symposium on Web and Wireless Geographical Information Systems, Seoul, South Korea, May 29–30.
   Google Scholar
• Boltcheva, D. , & Lévy, B. (2017). Surface reconstruction by computing restricted Voronoi cells in parallel. Computer-Aided Design , 90 , 123–134. doi:10.1016/j.cad.2017.05.011
   Web of Science ®Google Scholar
• Bradley, P. S. (2013). Implications of big data analytics on population health management. Big Data , 1 (3), 152–159. doi:10.1089/big.2013.0019
   PubMed Web of Science ®Google Scholar
• Cervone, G. , Sava, E. , Huang, Q. , Schnebele, E. , Harrison, J. , & Waters, N. (2016). Using Twitter for tasking remote-sensing data collection and damage assessment: 2013 Boulder flood case study. International Journal of Remote Sensing , 37 (1), 100–124. doi:10.1080/01431161.2015.1117684
   Web of Science ®Google Scholar
• Chen, B. , Yuan, H. , Li, Q. , Shaw, S. , Lam, W. H. K. , & Chen, X. (2015). Spatiotemporal data model for network time geographic analysis in the era of big data. International Journal of Geographical Information Science , 30 (6), 1041–1071. doi:10.1080/13658816.2015.1104317
   Web of Science ®Google Scholar
• Chen, J. , Chen, Y. , Du, X. , Li, C. , Lu, J. , Zhao, S. , & Zhou, X. (2013). Big data challenge: A data management perspective. Frontiers of Computer Science , 7 (2), 157–164. doi:10.1007/s11704-013-3903-7
   Web of Science ®Google Scholar
• Chi, M. , Plaza, A. , Benediktsson, J. A. , Sun, Z. , Shen, J. , & Zhu, Y. (2016). Big data for remote sensing: Challenges and opportunities. Proceedings of the IEEE , 104 (11), 2207–2219. doi:10.1109/Jproc.2016.2598228
   Web of Science ®Google Scholar
• Ding, Z. , Chen, Z. , & Yang, Q. (2014). IoT-SVKSearch: A real-time multimodal search engine mechanism for the internet of things. International Journal of Communication Systems , 27 (6), 871–897. doi:10.1002/dac.2647
   Web of Science ®Google Scholar
• Eldawy, A. , Alarabi, L. , & Mokbel, M. F. (2015). Spatial partitioning techniques in SpatialHadoop. Proceedings of the VLDB Endowment , 8 (12), 1602–1605. doi:10.14778/2824032.2824057
   Web of Science ®Google Scholar
• Eldawy, A. , Elganainy, M. , Bakeer, A. , Abdelmotaleb, A. , & Mokbel, M. F. (2015). Sphinx: Distributed execution of interactive SQL queries on big spatial data. Paper presented at the 23rd ACM Sigspatial International Conference on Advances In Geographic Information Systems, Seattle, WA, November 3–6.
   Google Scholar
• Eldawy, A. , Li, Y. , Mokbel, M. F. , & Janardan, R. (2013). CG-Hadoop: Computational geometry in MapReduce. Paper presented at the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Orlando, FL, USA, November 5–8.
   Google Scholar
• Eldawy, A. , & Mokbel, M. F. (2013). A demonstration of spatialhadoop: An efficient mapreduce framework for spatial data. Proceedings of the VLDB Endowment , 6 (12), 1230–1233. doi:10.14778/2536274.2536283
   Google Scholar
• Eldawy, A. , & Mokbel, M. F. (2014). Pigeon: A spatial MapReduce language. Paper presented at the 30th IEEE International Conference on Data Engineering, Chicago, IL, USA, March 31–April 04.
   Google Scholar
• Eldawy, A. , & Mokbel, M. F. (2015a). The era of big spatial data. Paper presented at the 31st IEEE International Conference on Data Engineering Workshops, Seoul, South Korea, April 13–17.
   Google Scholar
• Eldawy, A. , & Mokbel, M. F. (2015b). SpatialHadoop: A MapReduce framework for spatial data. Paper presented at the 31st IEEE International Conference on Data Engineering, Seoul, South Korea, April 13–17.
   Google Scholar
• Eldawy, A. , Mokbel, M. F. , Alharthi, S. , Alzaidy, A. , Tarek, K. , & Ghani, S. (2015). SHAHED: A MapReduce-based system for querying and visualizing spatio-temporal satellite data. Paper presented at the 31st IEEE International Conference on Data Engineering, Seoul, South Korea, April 13–17.
   Google Scholar
• Eldawy, A. , Mokbel, M. F. , & Jonathan, C. (2016). HadoopViz: A MapReduce framework for extensible visualization of big spatial data. Paper presented at the 32nd IEEE International Conference on Data Engineering, Helsinki, Finland, May 16–20.
   Google Scholar
• Fan, J. , Ji, M. , Gu, G. , & Sun, Y. (2014). Optimization approaches to mpi and area merging-based parallel buffer algorithm. Boletim de Ciências Geodésicas , 20 (2), 237–256. doi:10.1590/s1982-21702014000200015
   Web of Science ®Google Scholar
• Feng, J. , Tang, Z. , Wei, M. , & Xu, L. (2014). HQ-Tree: A distributed spatial index based on Hadoop. China Communications , 11 (7), 128–141. doi:10.1109/CC.2014.6895392
   Web of Science ®Google Scholar
• Fries, S. , Boden, B. , Stepien, G. , & Seidl, T. (2014). Phidj: Parallel similarity self-join for high-dimensional vector data with mapreduce. Paper presented at the 30th IEEE International Conference on Data Engineering, Chicago, IL, USA, March 31–April 04.
   Google Scholar
• Goodchild, M. F. (2013). The quality of big (geo)data. Dialogues in Human Geography , 3 (3), 280–284. doi:10.1177/2043820613513392
   Google Scholar
• Guo, H. (2017). Big Earth data: A new frontier in Earth and information sciences. Big Earth Data , 1 (1–2), 4–20. doi:10.1080/20964471.2017.1403062
   Google Scholar
• Guo, H. , Wang, L. , & Liang, D. (2016). Big Earth Data from space: A new engine for Earth science. Science Bulletin , 61 (7), 505–513. doi:10.1007/s11434-016-1041-y
   Web of Science ®Google Scholar
• Guo, M. , Guan, Q. , Xie, Z. , Wu, L. , Luo, X. , & Huang, Y. (2015). A spatially adaptive decomposition approach for parallel vector data visualization of polylines and polygons. International Journal of Geographical Information Science , 29 , 1419–1440.10.1080/13658816.2015.1032294
   Web of Science ®Google Scholar
• Hameurlain, A. , & Morvan, F. (2016). Big Data management in the cloud: Evolution or crossroad?. Paper presented at the 12th International Scientific Conference on Beyond Databases, Architectures and Structures (BDAS), Ustron, Poland, May 31–June 03.
   Google Scholar
• Huang, F. , Wen, C. , Luo, H. , Cheng, M. , Wang, C. , & Li, J. (2016). Local quality assessment of point clouds for indoor mobile mapping. Neurocomputing , 196 , 59–69. doi:10.1016/j.neucom.2016.02.033
   Web of Science ®Google Scholar
• Hughes, J. N. , Annex, A. , Eichelberger, C. N. , Fox, A. , Hulbert, A. , & Ronquest, M. (2015). GeoMesa: A distributed architecture for spatio-temporal fusion. Paper presented at the Geospatial Informatics, Fusion, and Motion Video Analytics V, Baltimore, MD, USA, April 20–21.
   Google Scholar
• Jhummarwala, A. , Mazin, A. , & Potdar, M. B. (2016). Geospatial Hadoop (GS-Hadoop) an efficient mapreduce based engine for distributed processing of shapefiles. Paper presented at the 2nd International Conference on Advances in Computing, Communication, & Automation, Bareilly, India, September 30–October 1.
   Google Scholar
• Kanchi, S. , Sandilya, S. , Ramkrishna, S. , Manjrekar, S. , & Vhadgar, A. (2015). Challenges and solutions in big data management – an overview. Paper presented at the 3rd International Conference on Future Internet Of Things And Cloud (Ficloud) And International Conference on Open And Big (Obd), Rome, Italy, August 24–26.
   Google Scholar
• Karim, A. , Siddiqa, A. , Safdar, Z. , Razzaq, M. , Gillani, S. A. , Tahir, H. , … Imran, M. (2017). Big data management in participatory sensing: Issues, trends and future directions. Future Generation Computer Systems . doi:10.1016/j.future.2017.10.007
   Google Scholar
• Keim, D. , Qu, H. , & Ma, K.-L. (2013). Big-Data visualization. IEEE Computer Graphics and Applications , 33 (4), 20–21. doi:10.1109/MCG.2013.54
   PubMed Web of Science ®Google Scholar
• Lee, J.-G. , & Kang, M. (2015). Geospatial big data: Challenges and opportunities. Big Data Research , 2 (2), 74–81. doi:10.1016/j.bdr.2015.01.003
   Web of Science ®Google Scholar
• Li, D. (2016). Towards geo-spatial information science in big data era. Cehui Xuebao/Acta Geodaetica et Cartographica Sinica , 45 (4), 379–384. doi:10.11947/j.AGCS.2016.20160057
   Google Scholar
• Li, G. , & Huang, Z. (2017). Data infrastructure for remote sensing big data: Integration, management and on-demand service. Jisuanji Yanjiu yu Fazhan/Computer Research and Development , 54 (2), 267–283. doi:10.7544/issn1000-1239.2017.20160837
   Google Scholar
• Li, J. , Xu, Z. , Jiang, Y. , & Zhang, R. (2014). The overview of big data storage and management. Paper presented at the 13th IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI-CC) , London, UK. August 18–20.
   Google Scholar
• Li, L. , Hu, W. , Zhu, H. , Li, Y. , & Zhang, H. (2017). Tiled vector data model for the geographical features of symbolized maps. Plos One , 12 (5), 1–26. doi:10.1371/journal.pone.0176387
   Web of Science ®Google Scholar
• Li, Q. , & Li, D. (2014). Big data GIS. Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University , 39 (6), 641–646. doi:10.13203/j.whugis20140150
   Google Scholar
• Li, X. , & Zheng, W. (2013). Parallel spatial index algorithm based on hilbert partition. Paper presented at the 5th International Conference on Computational and Information Sciences , Shiyan, Hubei, China. June 21–23.
   Google Scholar
• Lin, W. , Zhou, H. , & Xia, P. (2016). An effective NoSQL-based vector map tile management approach. Isprs International Journal of Geo-Information , 5 (11), 1–25. doi:10.3390/Ijgi5110215
   Web of Science ®Google Scholar
• Liu, J. , Fang, Y. , Guo, C. , & Gao, K. (2014). Research progress in location big data analysis and processing. Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University , 39 (4), 379–385. doi:10.13203/j.whugis20140210
   Google Scholar
• Liu, Y. , Liu, X. , Gao, S. , Gong, L. , Kang, C. , Zhi, Y. , … Shi, L. (2015). Social sensing: A new approach to understanding our socioeconomic environments. Annals of the Association of American Geographers , 105 (3), 512–530. doi:10.1080/00045608.2015.1018773
   Web of Science ®Google Scholar
• Lu, F. , & Zhang, H. (2014). Big data and generalized GIS. Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University , 39 (6), 645–654. doi:10.13203/j.whugis20140148
   Google Scholar
• Lu, G. , Yuan, L. , & Yu, Z. (2017). Surveying and mapping geographical information from the perspective of geography. Cehui Xuebao/Acta Geodaetica et Cartographica Sinica , 46 (10), 1549–1556. doi:10.11947/j.AGCS.2017.20170338
   Google Scholar
• Lu, J. , & Guting, R. H. (2014). Parallel SECONDO: A practical system for large-scale processing of moving objects. Paper presented at the 30th IEEE International Conference on Data Engineering , Chicago, IL, USA. March 31–April 04.
   Google Scholar
• Lu, P , Chen, G. , Ooi, B. C. , Vo, H. T. , & Wu, S. (2014). ScalaGiST: Scalable generalized search trees for mapreduce systems [innovative systems paper]. Proceedings of the VLDB Endowment , 7 (14), 1797–1808. doi:10.14778/2733085.2733087
   Google Scholar
• Ma, Y. , Wu, H. , Wang, L. , Huang, B. , Ranjan, R. , Zomaya, A. , & Jie, W. (2015). Remote sensing big data computing: Challenges and opportunities. Future Generation Computer Systems , 51 , 47–60. doi:10.1016/j.future.2014.10.029
   Web of Science ®Google Scholar
• Magdy, A. , Mokbel, M. F. , Elnikety, S. , Nath, S. , & He, Y. (2016). Venus: Scalable real-time spatial queries on microblogs with adaptive load shedding. IEEE Transactions on Knowledge & Data Engineering , 28 (2), 356–370. doi:10.1109/TKDE.2015.2493531
   Web of Science ®Google Scholar
• Manyika, J. , Chui, M. , Brown, B. , Bughin, J. , Dobbs, R. , Roxburgh, C. , & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. Analytics .
   Google Scholar
• Mapbox . (2018). Awesome implementations of the vector tile . Author. Retrieved January 5, from. https://github.com/mapbox/awesome-vector-tiles
   Google Scholar
• Mcafee, A. , & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review , 90 (10), 60–66.
   PubMed Web of Science ®Google Scholar
• McCoy, M. D. (2017). Geospatial big data and archaeology: Prospects and problems too great to ignore. Journal of Archaeological Science , 84 , 74–94. doi:10.1016/j.jas.2017.06.003
   Web of Science ®Google Scholar
• Miller, H. J. , & Goodchild, M. F. (2015). Data-driven geography. GeoJournal , 80 (4), 449–461. doi:10.1007/s10708-014-9602-6
   Web of Science ®Google Scholar
• Mulyono, S. , & Fanany, M. I. (2015). Remote sensing big data utilization for paddy growth stages detection. Paper presented at the IEEE International Conference on Aerospace Electronics and Remote Sensing Technology, Bali, Indonesia, December 3–5.
   Google Scholar
• Nishimura, S. , Das, S. , Agrawal, D. , & El Abbadi, A. (2011). MD-HBase: A scalable multi-dimensional data infrastructure for location aware services. Paper presented at the 12th IEEE International Conference on Mobile Data Management, Lulea, Sweden, June 6–9.
   Google Scholar
• Nobari, S. , Tauheed, F. , Heinis, T. , Karras, P. , Bressan, S. , & Ailamaki, A. (2013). TOUCH: In-memory spatial join by hierarchical data-oriented partitioning. Paper presented at the Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, New York, NY, USA, June 22–27.
   Google Scholar
• Pekturk, M. K. , & Unal, M. (2017). A review on real-time big data analysis in remote sensing applications. Paper presented at the 25th Signal Processing and Communications Applications Conference (SIU), Antalya, Turkey, May 15–18.
   Google Scholar
• Puri, S. , & Prasad, S. K. (2014). Output-sensitive parallel algorithm for polygon clipping. Paper presented at the 43rd International Conference on Parallel Processing (ICPP), Minneapolis, MN, USA, September 9–12.
   Google Scholar
• Puri, S. , & Prasad, S. K. (2015). A parallel algorithm for clipping polygons with improved bounds and a distributed overlay processing system using MPI. Paper presented at the 15th IEEE ACM International Symposium on Cluster Cloud and Grid Computing, Shenzhen, China, May 4–7.
   Google Scholar
• Purss, M. B. J. , Gibb, R. , Samavati, F. , Peterson, P. , & Ben, J. (2016). The Ogc (R) discrete global grid system core standard: A framework for rapid geospatial integration. Paper presented at the 36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, July 10–15.
   Google Scholar
• Ray, S. , Simion, B. , Brown, A. D. , & Johnson, R. (2013). A parallel spatial data analysis infrastructure for the cloud. Paper presented at the Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Orlando, FL, USA, November 5–8.
   Google Scholar
• Schoier, G. , & Borruso, G. (2015). On the problem of clustering spatial big data. Paper presented at the 15th International Conference on Computational Science and its Applications, Banff, AB, Canada, June 22–25.
   Google Scholar
• Scitovski, R. , & Scitovski, S. (2013). A fast partitioning algorithm and its application to earthquake investigation. Computers & Geosciences , 59 , 124–131. doi:10.1016/j.cageo.2013.06.010
   Web of Science ®Google Scholar
• Scitovski, S. (2018). A density-based clustering algorithm for earthquake zoning. Computers & Geosciences , 110 , 90–95. doi:10.1016/j.cageo.2017.08.014
   Web of Science ®Google Scholar
• Shekhar, S. , Evans, M. R. , Gunturi, V. , Yang, Y. , & Cugler, D. C. (2014). Benchmarking spatial big data. Paper presented at the 2nd Workshop on Specifying Big Data Benchmarks, Pune, India, December 17–18.
   Google Scholar
• Shen, D. , Wong, D. W. , Camelli, F. , & Liu, Y. (2013). An ArcScene plug-in for volumetric data conversion, modeling and spatial analysis. Computers & Geosciences , 61 , 104–115. doi:10.1016/j.cageo.2013.08.004
   Web of Science ®Google Scholar
• Siddiqa, A. , Hashem, I. A. T. , Yaqoob, I. , Marjani, M. , Shamshirband, S. , Gani, A. , & Nasaruddin, F. (2016). A survey of big data management: Taxonomy and state-of-the-art. Journal of Network and Computer Applications , 71 , 151–166. doi:10.1016/j.jnca.2016.04.008
   Web of Science ®Google Scholar
• Stantic, B. , & Pokorný, J. (2014). Opportunities in big data management and processing. Frontiers in Artificial Intelligence & Applications , 270 , 15–26. doi:10.3233/978-1-61499-458-9-15
   Google Scholar
• Starinshak, D. P. , Owen, J. M. , & Johnson, J. N. (2014). A new parallel algorithm for constructing Voronoi tessellations from distributed input data. Computer Physics Communications , 185 (12), 3204–3214. doi:10.1016/j.cpc.2014.08.020
   Web of Science ®Google Scholar
• Storey, V. C. , & Song, I.-Y. (2017). Big data technologies and management: What conceptual modeling can do. Data & Knowledge Engineering , 108 , 50–67. doi:10.1016/j.datak.2017.01.001
   Web of Science ®Google Scholar
• Sui, D. , Ye, X. , & Gan, T. (2014). Open GIS for big data: Opportunities and impediments. Progress in Geography , 33 (6), 723–737. doi:10.11820/dlkxjz.2014.06.001
   Google Scholar
• Sun, Z. , Chen, F. , Chi, M. , & Zhu, Y. (2015). A spark-based big data platform for massive remote sensing data processing. Paper presented at the 2nd International Conference on Data Science (ICDS), Univ Technol Sydney, Sydney, Austrilia, August 8–9.
   Google Scholar
• Taleb, I. , Dssouli, R. , & Serhani, M. A. (2015). Big data pre-processing: A quality framework. Paper presented at the IEEE International Congress on Big Data, New York, NY, USA, June 27–July 2.
   Google Scholar
• Tan, H. , Luo, W. , & Ni, L. M. (2012). CloST: A hadoop-based storage system for big spatio-temporal data analytics. Paper presented at the Proceedings of the 21st ACM international conference on Information and knowledge management, Maui, Hawaii, USA, October 29–November 2.
   Google Scholar
• Tang, W. , & Feng, W. (2017). Parallel map projection of vector-based big spatial data: Coupling cloud computing with graphics processing units. Computers, Environment and Urban Systems , 61 , 187–197. doi:10.1016/j.compenvurbsys.2014.01.001
   Web of Science ®Google Scholar
• Tong, X. , Ben, J. , Liu, Y. , & Zhang, Y. (2013). Modeling and expression of vector data in the hexagonal discrete global grid system. Isprs Webmgs 2013 & Dmgis 2013 Topics: Global Spatial Grid & Cloud-Based Services 40-4-W2 , 15–25. doi:10.5194/isprsarchives-XL-4-W2-15-2013
   Google Scholar
• Tsou, M.-H. (2015). Research challenges and opportunities in mapping social media and Big Data. Cartography and Geographic Information Science , 42 (Suppl 1), 70–74. doi:10.1080/15230406.2015.1059251
   Google Scholar
• Wang, H. , Guan, X. , & Wu, H. (2017). A hybrid parallel spatial interpolation algorithm for massive LiDAR point clouds on heterogeneous CPU-GPU systems. ISPRS International Journal of Geo-Information , 6 (11), 363. doi:10.3390/ijgi6110363
   Web of Science ®Google Scholar
• Wang, J. (2017). Cartography in the age of spatio-temporal big data. Cehui Xuebao/Acta Geodaetica et Cartographica Sinica , 46 (10), 1226–1237. doi:10.11947/j.AGCS.2017.20170308
   Google Scholar
• Wang, L. , Chen, B. , & Liu, Y. (2013). Distributed storage and index of vector spatial data based on HBase. Paper presented at the 21st International Conference on Geoinformatics, Kaifeng, China, June 20–22.
   Google Scholar
• Wang, L. , Geng, H. , Liu, P. , Lu, K. , Kolodziej, J. , Ranjan, R. , & Zomaya, A. Y. (2015). Particle Swarm optimization based dictionary learning for remote sensing big data. Knowledge-Based Systems , 79 , 43–50. doi:10.1016/j.knosys.2014.10.004
   Web of Science ®Google Scholar
• Wang, L. , Ma, Y. , Yan, J. , Chang, V. , & Zomaya, A. Y. (2018). pipsCloud: High performance cloud computing for remote sensing big data management and processing. Future Generation Computer Systems , 78 , 353–368. doi:10.1016/j.future.2016.06.009
   Web of Science ®Google Scholar
• Wang, Y. , Liu, Z. , Liao, H. , & Li, C. (2015). Improving the performance of GIS polygon overlay computation with MapReduce for spatial big data processing. Cluster Computing-the Journal of Networks Software Tools and Applications , 18 (2), 507–516. doi:10.1007/s10586-015-0428-x
   Web of Science ®Google Scholar
• Wei, H. , Du, Y. , Liang, F. , Zhou, C. , Liu, Z. , Yi, J. , … Wu, D. (2015). A k-d tree-based algorithm to parallelize Kriging interpolation of big spatial data. GIScience & Remote Sensing , 52 (1), 40–57. doi:10.1080/15481603.2014.1002379
   Web of Science ®Google Scholar
• Whitman, R. T. , Park, M. B. , Ambrose, S. M. , & Hoel, E. G. (2014). Spatial indexing and analytics on hadoop. Paper presented at the Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, New York, NY, USA, November 4–7.
   Google Scholar
• Wojda, P. , & Brouyère, S. (2013). An object-oriented hydrogeological data model for groundwater projects. Environmental Modelling & Software , 43 , 109–123. doi:10.1016/j.envsoft.2013.01.015
   Web of Science ®Google Scholar
• Wu, Z. , Chai, Y. , Dang, A. , Gong, J. , Gao, S. , Yue, Y. , … Liu, Y. (2015). Geography interact with big data: Dialogue and reflection. Geographical Research , 12 (34), 2207–2221. doi:10.11821/dlyj201512001
   Google Scholar
• Xie, D. , Li, F. , Yao, B. , Li, G. , Chen, Z. , Zhou, L. , & Guo, M. (2016). Simba: Spatial in-memory big data analysis. Paper presented at the 24th ACM SIGSPATIAL International Conference on Advances In Geographic Information Systems, San Francisco, CA, USA, October 31–November 3.
   Google Scholar
• Xu, L. , Jiang, C. , Wang, J. , Yuan, J. , & Ren, Y. (2014). Information security in big data: Privacy and data mining. IEEE Access , 2 , 1149–1176. doi:10.1109/access.2014.2362522
   Web of Science ®Google Scholar
• Yang, C. , Huang, Q. , Li, Z. , Liu, K. , & Hu, F. (2016). Big DATA and cloud computing: Innovation opportunities and challenges. International Journal of Digital Earth , 10 (1), 13–53. doi:10.1080/17538947.2016.1239771
   Web of Science ®Google Scholar
• Yang, C. , Raskin, R. , Goodchild, M. , & Gahegan, M. (2010). Geospatial cyberinfrastructure: Past, present and future. Computers, Environment and Urban Systems , 34 (4), 264–277. doi:10.1016/j.compenvurbsys.2010.04.001
   Web of Science ®Google Scholar
• Yang, C. , Yan, X. , & Nebert, D. (2013). Redefining the possibility of digital Earth and geosciences with spatial cloud computing. International Journal of Digital Earth , 6 (4), 297–312. doi:10.1080/17538947.2013.769783
   Web of Science ®Google Scholar
• Yang, C. , Yu, M. , Hu, F. , Jiang, Y. , & Li, Y. (2017). Utilizing cloud computing to address big geospatial data challenges. Computers, Environment and Urban Systems , 61 , 120–128. doi:10.1016/j.compenvurbsys.2016.10.010
   Web of Science ®Google Scholar
• Yao, X. , Mokbel, M. F. , Alarabi, L. , Eldawy, A. , Yang, J. , Yun, W. , … Zhu, D. (2017). Spatial coding-based approach for partitioning big spatial data in Hadoop. Computers & Geosciences , 106 , 60–67. doi:10.1016/j.cageo.2017.05.014
   Web of Science ®Google Scholar
• Yao, X. , Yang, J. , Li, L. , Yun, W. , Zhao, Z. , Ye, S. , & Zhu, D. (2017). LandQv1: A GIS cluster-based management information system for arable land quality big data. Paper presented at the 6th International Conference on Agro-Geoinformatics, Fairfax, VA, USA, August 7–10.
   Google Scholar
• Yao, X. , Zhu, D. , Ye, S. , Zhang, N. , & Li, L. (2014). Spatial interpolation methods study based on geostatistics for the grasshopper population. Sensor Letters , 12 , 645–650. doi:10.1166/sl.2014.3095
   Google Scholar
• Yao, X. , Zhu, D. , Yun, W. , Peng, F. , & Li, L. (2017). A WebGIS-based decision support system for locust prevention and control in China. Computers and Electronics in Agriculture , 140 , 148–158. doi:10.1016/j.compag.2017.06.001
   Web of Science ®Google Scholar
• Ye, S. , Yan, T. , Yue, Y. , Lin, W. , Li, L. , Yao, X. , … Zhu, D. (2016). Developing a reversible rapid coordinate transformation model for the cylindrical projection. Computers & Geosciences , 89 , 44–56. doi:10.1016/j.cageo.2016.01.007
   Web of Science ®Google Scholar
• You, S. , Zhang, J. , & Gruenwald, L. (2015a). Large-scale spatial join query processing in cloud. Paper presented at the 31st IEEE International Conference on Data Engineering Workshops, Seoul, South Korea, April 13–17.
   Google Scholar
• You, S. , Zhang, J. , & Gruenwald, L. (2015b). Spatial join query processing in cloud: Analyzing design choices and performance comparisons. Paper presented at the 44th International Conference on Parallel Processing Workshops, Beijing, China, September 1–4.
   Google Scholar
• Yu, E. G. , Di, L. , Rahman, M. S. , Lin, L. , Zhang, C. , Hu, L. , … Yang, G. (2017). Performance improvement on a Web Geospatial service for the remote sensing flood-induced crop loss assessment web application using vector tiling. Paper presented at the 6th International Conference on Agro-Geoinformatics, Fairfax, VA, USA, August 7–10.
   Google Scholar
• Yu, J. , Wu, J. , & Sarwat, M. (2015). GeoSpark: A cluster computing framework for processing large-scale spatial data. Paper presented at the Proceedings of the 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, Washington, November 3–6.
   Google Scholar
• Zhang, G. (2017). Construction of the college education quality management system based on big data and its evaluation. Agro Food Industry Hi-Tech , 28 (1), 3124–3127.
   Google Scholar
• Zhang, J. , & You, S. (2012). Speeding up large-scale point-in-polygon test based spatial join on GPUs. Paper presented at the Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, Redondo Beach, CA, USA, November 6.
   Google Scholar
• Zhang, X. (2014). Spatial analysis in the era of big data. Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University , 39 (6), 655–659. doi:10.13203/j.whugis20140143
   Google Scholar
• Zhao, L. , Chen, L. , Ranjan, R. , Choo, K.-K. R. , & He, J. (2015). Geographical information system parallelization for spatial big data processing: A review. Cluster Computing , 19 (1), 139–152. doi:10.1007/s10586-015-0512-2
   Web of Science ®Google Scholar
• Zheng, K. , & Fu, Y. (2013). Research on vector spatial data storage schema based on Hadoop platform. International Journal of Database Theory and Application , 6 (5), 85–94. doi:10.14257/ijdta.2013.6.5.08
   Google Scholar
• Zheng, Y. (2015). Introduction to urban computing. Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University , 40 (1), 1–13. doi:10.13203/j.whugis20140718
   Google Scholar
• Zheng, Y. (2017). Urban computing: Enabling urban intelligence with big data. Frontiers of Computer Science , 11 (1), 1–3. doi:10.1007/s11704-016-6907-2
   Web of Science ®Google Scholar
• Zhong, R. , Li, G. , Tan, K.-L. , Zhou, L. , & Gong, Z. (2015). G-Tree: An efficient and scalable index for spatial search on road networks. IEEE Transactions on Knowledge and Data Engineering , 27 (8), 2175–2189. doi:10.1109/tkde.2015.2399306
   Web of Science ®Google Scholar
• Zhong, Y. , Fang, J. , & Zhao, X. (2013). VegaIndexer: A distributed composite index scheme for big spatio-temporal sensor data on cloud. Paper presented at the 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS, Melbourne, VIC, Australia, July 21–26.
   Google Scholar
• Zhong, Y. , Han, J. , Zhang, T. , Li, Z. , Fang, J. , & Chen, G. (2012). Towards parallel spatial query processing for big spatial data. Paper presented at the 26th IEEE International Parallel and Distributed Processing Symposium Workshops, Shanghai, China, May 21–25.
   Google Scholar
• Zhou, Y. , Zhou, C. , Ma, T. , Han, J , Xu, T. , & Ji, M. (2015). A double-index and data divide-conquer based parallel point-polygon overlay method. Geography and Geo-Information Science , 31 (02), 1–6. doi:10.3969/j.issn.1672-0504.2015.02.001
   Google Scholar
• Zhu, X. , Huo, J. , & Qiu, Q. (2015). A novel methodology for parallel spatial overlay over vector data-A case study with shape file. Paper presented at the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, July 26–31.
   Google Scholar
• Zhuang, Y. , Fong, S. , Yuan, M. , Sung, Y. , Cho, K. , & Wong, R. K. (2017). Location-based big data analytics for guessing the next Foursquare check-ins. The Journal of Supercomputing , 73 (7), 3112–3127. doi:10.1007/s11227-016-1925-2
   Web of Science ®Google Scholar