{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T05:26:14Z","timestamp":1772429174654,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2016,9,29]],"date-time":"2016-09-29T00:00:00Z","timestamp":1475107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>With the rapid development of mobile data acquisition technology, the volume of available spatial data is growing at an increasingly fast pace. The real-time processing of big spatial data has become a research frontier in the field of Geographic Information Systems (GIS). To cope with these highly dynamic data, we aim to reduce the time complexity of data updating by modifying the traditional spatial index. However, existing algorithms and data structures are based on single work nodes, which are incapable of handling the required high numbers and update rates of moving objects. In this paper, we present a distributed spatial index based on Apache Storm, an open-source distributed real-time computation system. Using this approach, we compare the range and K-nearest neighbor (KNN) query efficiency of four spatial indexes on a single dataset and introduce a method of performing spatial joins between two moving datasets. In particular, we build a secondary distributed index for spatial join queries based on the grid-partition index. Finally, a series of experiments are presented to explore the factors that affect the performance of the distributed index and to demonstrate the feasibility of the proposed distributed index based on Storm. As a real-world application, this approach has been integrated into an information system that provides real-time traffic decision support.<\/jats:p>","DOI":"10.3390\/ijgi5100178","type":"journal-article","created":{"date-parts":[[2016,9,29]],"date-time":"2016-09-29T09:54:50Z","timestamp":1475142890000},"page":"178","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Real-Time Spatial Queries for Moving Objects Using Storm Topology"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1475-8480","authenticated-orcid":false,"given":"Feng","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China"},{"name":"Zhejiang Provincial Key Laboratory of Geographic Information Science, 148 Tianmushan Road, Hangzhou 310028, China"}]},{"given":"Ye","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China"}]},{"given":"Dengping","family":"Xu","sequence":"additional","affiliation":[{"name":"Academy of Forest Inventory and Planning, State Forestry Administration, Beijing 100714, China"}]},{"given":"Zhenhong","family":"Du","sequence":"additional","affiliation":[{"name":"Zhejiang Provincial Key Laboratory of Geographic Information Science, 148 Tianmushan Road, Hangzhou 310028, China"}]},{"given":"Yingzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Public Order, Zhejiang Police College, 555 Binwen Road, Hangzhou 310053, China"}]},{"given":"Renyi","family":"Liu","sequence":"additional","affiliation":[{"name":"Zhejiang Provincial Key Laboratory of Geographic Information Science, 148 Tianmushan Road, Hangzhou 310028, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8838-9476","authenticated-orcid":false,"given":"Xinyue","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Geography, Kent State University, Kent, OH 44240, USA"}]}],"member":"1968","published-online":{"date-parts":[[2016,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1109\/TVCG.2015.2467771","article-title":"TrajGraph: A graph-based visual analytics approach to studying urban network centralities using taxi trajectory data","volume":"22","author":"Huang","year":"2016","journal-title":"Vis. Comput. Graph."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1007\/s10586-015-0428-x","article-title":"Improving the performance of GIS polygon overlay computation with MapReduce for spatial big data processing","volume":"18","author":"Wang","year":"2015","journal-title":"Clust. Comput."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"You, S.J., and Zhang, L.G. (2015, January 13\u201317). Large-scale spatial join query processing in cloud. Proceedings of the IEEE International Conference on Data Engineering Workshops, Seoul, Korea.","DOI":"10.1109\/ICDEW.2015.7129541"},{"key":"ref_4","unstructured":"Fast Data: The Next Step after Big Data. Available online: http:\/\/www.infoworld.com\/article\/2608040."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Stojanovi\u0107, D.N., and Turanjanin, J. (2015, January 14\u201317). Processing big trajectory and Twitter data streams using Apache STORM. Proceedings of the 12th International Conference on Telecommunication in Modern Satellite, Cable and Broadcasting Services (TELSIKS), Ni\u0161, Serbia.","DOI":"10.1109\/TELSKS.2015.7357792"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhao, S., Chandrashekar, M., and Lee, Y. (2015, January 24\u201327). Real-time network anomaly detection system using machine learning. Proceedings of the 11th International Conference on the Design of Reliable Communication Networks, Kansas City, MO, USA.","DOI":"10.1109\/DRCN.2015.7149025"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1145\/1138394.1138396","article-title":"Maintenance of K-nn and spatial join queries on continuously moving points","volume":"31","author":"Iwerks","year":"2006","journal-title":"ACM Trans. Database Syst."},{"key":"ref_8","first-page":"165","article-title":"An efficient scalable spatial data search for location-aware mobile services","volume":"31","author":"Park","year":"2015","journal-title":"J. Inf. Sci. Eng."},{"key":"ref_9","unstructured":"Kwon, D., and Lee, S. (2002, January 8\u201310). Indexing the current positions of moving objects using the lazy update R-tree. Proceedings of the Third International Conference on Mobile Data Management, Singapore, Singapore."},{"key":"ref_10","unstructured":"Pfoser, D., Jensen, C.S., and Theodoridis, Y. (2000, January 10\u201314). Novel approaches to the indexing of moving object trajectories. Proceedings of the 26th VLDB Conference, Cairo, Egypt."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1007\/s10707-014-0218-2","article-title":"The TM-RTree an index on generic moving objects for range queries","volume":"19","author":"Xu","year":"2015","journal-title":"Geoinformatica"},{"key":"ref_12","unstructured":"Tao, Y., Papadias, D., and Sun, J. (2003, January 9\u201312). The TPR-tree: An optimized spatio-temporal access method for predictive queries. Proceedings of the 29th International Conference on Very Large Data Bases, Berlin, Germany."},{"key":"ref_13","unstructured":"Tao, Y., and Papadiasa, D. (2000). MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries Dept, Hong Kong University."},{"key":"ref_14","unstructured":"Jensen, C.S., Lin, D., and Ooi, B.C. (September, January 31). Query and update efficient B \u00b1 Tree based indexing of moving objects. Proceedings of the 30th VLDB Conference, Toronto, ON, Canada."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"\u0160altenis, S., Jense, C.S., and Leutenegger, S.T. (2000, January 16\u201318). Indexing the positions of continuously moving objects. Proceedings of the ACM SIGMOD International Conference on Management of Data, New York, NY, USA.","DOI":"10.1145\/342009.335427"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1007\/s11390-008-9185-0","article-title":"Adaptive indexing of moving objects with highly variable update frequencies","volume":"23","author":"Chen","year":"2008","journal-title":"J. Comput. Sci. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wu, W., and Tan, K. (2007, January 9\u201311). ISEE: Efficient continuous K-nearest-neighbor monitoring over moving objects. Proceedings of the 19th International Conference on Scientific and Statistical Database Management, Banff, AB, Canada.","DOI":"10.1109\/SSDBM.2007.37"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"\u0160idlauskas, D., Ross, K.A., and Jensen, C.S. (2011, January 24\u201326). Thread-level parallel indexing of update intensive moving-object workloads. Proceedings of the 12th International Symposium on Spatial and Temporal Databases, Minneapolis, MN, USA.","DOI":"10.1007\/978-3-642-22922-0_12"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPDS.2014.2311811","article-title":"Parallel processing of dynamic continuous queries over streaming data flows","volume":"82","author":"Deng","year":"2015","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1080\/02693799608902109","article-title":"Strategies for real-time spatial analysis using massively parallel SIMD computers: An application to urban traffic flow analysis","volume":"10","author":"Xiong","year":"1996","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"\u0160idlauskas, D., \u0160altenis, S., and Jensen, C.S. (2009, January 4\u20136). Trees or grids? Indexing moving objects in main memory. Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, WA, USA.","DOI":"10.1145\/1653771.1653805"},{"key":"ref_22","unstructured":"Lee, M.L., Hsu, W., and Jense, C.S. (2003, January 12\u201313). Supporting frequent updates in R-trees: A bottom-up approach. Proceedings of the 29th International Conference on Very Large Data Bases, Berlin, Germany."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1007\/s00778-014-0353-2","article-title":"Processing of extreme moving-object update and query workloads in main memory","volume":"23","author":"Jensen","year":"2014","journal-title":"VLDB J."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"You, S., Zhang, J., and Le, G. (2015, January 16\u201319). Spatial join query processing in cloud: Analyzing design choices and performance comparisons. Proceedings of the International Conference on Parallel Processing Workshops, Beijing, China.","DOI":"10.1109\/ICPPW.2015.41"},{"key":"ref_25","unstructured":"Zhang, S., Han, J., and Liu, Z. (September, January 31). SJMR: Parallelizing spatial join with MapReduce on clusters. Proceedings of the IEEE International Conference on Cluster Computing & Workshops, New Orleans, LA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lu, W., Shen, Y., and Chen, S. (2012). Efficient processing of k nearest neighbor joins using MapReduce. Proc. VLDB Endow.","DOI":"10.14778\/2336664.2336674"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Akdogan, A., Demiryurek, U., Banaeikashani, F., and Shahabi, C. (2010, January 15\u201319). Voronoi-based geospatial query processing with MapReduce. Proceedings of the IEEE Second International Conference on Cloud Computing Technology & Science, Indianapolis, Indiana, IN, USA.","DOI":"10.1109\/CloudCom.2010.92"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhong, Y.Q., Han, J.Z., and Zhang, T.Y. (2012, January 21\u201325). Towards parallel spatial query processing for big spatial data. Proceedings of the Parallel & Distributed Processing Symposium Workshops & PhD Forum, Shanghai, China.","DOI":"10.1109\/IPDPSW.2012.245"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Aji, A., Wang, F., and Vo, H. (2013). Hadoop-GIS: A high performance spatial data warehousing system over MapReduce. Proc. VLDB Endow.","DOI":"10.14778\/2536222.2536227"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Eldawy, A., and Mokbel, M.F. (2013). A demonstration of SpatialHadoop: An efficient MapReduce framework for spatial data. Proc. VLDB Endow.","DOI":"10.14778\/2536274.2536283"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yu, J., Wu, J., and Sarwat, M. (2016, January 16\u201325). A demonstration of GeoSpark: A cluster computing framework for processing big spatial data. Proceedings of the IEEE International Conference on Data Engineering, Helsinki, Finland.","DOI":"10.1109\/ICDE.2016.7498357"},{"key":"ref_32","unstructured":"Baig, F., Mehrotra, M., and Wang, F. (2015). VLDB Workshops, Big-O(Q) and DMAH."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xie, D., Li, F., and Li, G. (July, January 26). Simba: Efficient in memory spatial analytics. Proceedings of the 2016 International Conference on Management of Data, San Francisco, CA, USA.","DOI":"10.1145\/2882903.2915237"},{"key":"ref_34","unstructured":"Allen, S.T., Jankowski, M., and Pathirana, P. (2015). Storm Applied: Strategies for Real-Time Event Processing, Manning Publications."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"MouRatidis, K., Papadias, D., and Hadjieleftheriou, M. (2005, January 13\u201316). Conceptual partitioning: An efficient method for continuous nearest neighbor monitoring. Proceedings of the ACM SIGMOD International Conference on Management of Data, New York, USA.","DOI":"10.1145\/1066157.1066230"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1007\/s10707-011-0122-y","article-title":"Movies: Indexing moving objects by shooting index images","volume":"15","author":"Dittrich","year":"2011","journal-title":"Geoinformatica"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1145\/356789.356797","article-title":"Data structures for range searching","volume":"11","author":"Bentley","year":"1979","journal-title":"ACM Comput. Surv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1109\/TKDE.2010.171","article-title":"Processing of continuous location-based range queries on moving objects in road networks","volume":"23","author":"Wang","year":"2011","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Tauheed, F., Heinis, T., and Ailamaki, A. (2015, January 9\u201316). Thermal-join: A scalable spatial join for dynamic workloads. Proceedings of the ACM SIGMOD International Conference on Management of Data, Melbourne, Australia.","DOI":"10.1145\/2723372.2749434"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Corral, A., Torres, M., and Vassilakopoulos, M. (2008, January 5\u20139). Predictive join processing between regions and moving object. Proceedings of the 12th East European Conference, Pori, Finland.","DOI":"10.1007\/978-3-540-85713-6_5"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1007\/s00778-014-0358-x","article-title":"Real-time continuous intersection joins over large sets of moving objects using graphic processing units","volume":"23","author":"Ward","year":"2014","journal-title":"VLDB J."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1023\/B:DAPD.0000013068.25976.88","article-title":"Main memory evaluation of monitoring queries over moving objects","volume":"15","author":"Kalashnikov","year":"2004","journal-title":"Distrib. Parallel Databases"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/978-3-540-24741-8_6","article-title":"MobiEyes: Distributed processing of continuously moving queries on moving objects in a mobile system","volume":"Volume 2992","author":"Gedik","year":"2004","journal-title":"Advances in Database Technology\u2014EDBT 2004"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1007\/s00778-011-0259-1","article-title":"A highly optimized algorithm for continuous intersection join queries over moving objects","volume":"21","author":"Zhang","year":"2012","journal-title":"VLDB J."},{"key":"ref_45","unstructured":"Mokbel, M.F., Xiong, X., and Aref, W.G. (September, January 29). PLACE: A query processor for handling real-time spatio-temporal data streams. Proceedings of the 30th International Conference on Very Large Data Bases, Toronto, ON, Canada."},{"key":"ref_46","unstructured":"Xiong, X.P., Mokbel, M.F., and Aref, W.G. (2005, January 5\u20138). SEA-CNN: Scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases. Proceedings of the 21st International Conference on Data Engineering, Tokyo, Japan."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1023\/A:1015231126594","article-title":"A framework for generating network-based moving objects","volume":"6","author":"Brinkhoff","year":"2002","journal-title":"Geoinformatica"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/5\/10\/178\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:32:05Z","timestamp":1760211125000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/5\/10\/178"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,9,29]]},"references-count":47,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2016,10]]}},"alternative-id":["ijgi5100178"],"URL":"https:\/\/doi.org\/10.3390\/ijgi5100178","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,9,29]]}}}