{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T12:18:53Z","timestamp":1766578733958,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T00:00:00Z","timestamp":1652572800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Development Project of Henan Province","award":["192102210276","KLGIS2021A01"],"award-info":[{"award-number":["192102210276","KLGIS2021A01"]}]},{"name":"Open Fund of Key Laboratory of Geographic Information Science (Ministry of Education)","award":["192102210276","KLGIS2021A01"],"award-info":[{"award-number":["192102210276","KLGIS2021A01"]}]},{"name":"East China Normal University","award":["192102210276","KLGIS2021A01"],"award-info":[{"award-number":["192102210276","KLGIS2021A01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>As a basic method of spatial data operation, spatial keyword query can provide meaningful information to meet user demands by searching spatial textual datasets. How to accurately understand users\u2019 intentions and efficiently retrieve results from spatial textual big data are always the focus of research. Spatial textual big data and their complex correlation between textual features not only enrich the connotation of spatial objects but also bring difficulties to the efficient recognition and retrieval of similar spatial objects. Because there are a lot of many-to-many relationships between massive spatial objects and textual features, most of the existing research results that employ tree-like and table-like structures to index spatial data and textual data are inefficient in retrieving similar spatial objects. In this paper, firstly, we define spatial textual concept (STC) as a group of spatial objects with the same textual keywords in a limited spatial region in order to present the many-to-many relationships between spatial objects and textual features. Then we attempt to introduce the concept lattice model to maintain a group of related STCs and propose a hybrid tree-like spatial index structure, the lattice-tree, for spatial textual big data. Lattice-tree employs R-tree to index the spatial location of objects, and it embeds a concept lattice structure into specific tree nodes to organize the STC set from a large number of textual keywords of objects and their relationships. Based on this, we also propose a novel spatial keyword query, named Top-k spatial concept query (TkSCQ), to answer STC and retrieve similar spatial objects with multiple textual features. The empirical study is carried out on two spatial textual big data sets from Yelp and Amap. Experiments on the lattice-tree verify its feasibility and demonstrate that it is efficient to embed the concept lattice structure into tree nodes of 3 to 5 levels. Experiments on TkSCQ evaluate lattice from results, keywords, data volume, and so on, and two baseline index structures based on IR-tree and Fp-tree, named the inverted-tree and Fpindex-tree, are developed to compare with the lattice-tree on data sets from Yelp and Amap. Experimental results demonstrate that the Lattice-tree has the better retrieval efficiency in most cases, especially in the case of large amounts of data queries, where the retrieval performance of the lattice-tree is much better than the inverted-tree and Fpindex-tree.<\/jats:p>","DOI":"10.3390\/ijgi11050312","type":"journal-article","created":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T09:48:22Z","timestamp":1652608102000},"page":"312","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Spatial Concept Query Based on Lattice-Tree"],"prefix":"10.3390","volume":"11","author":[{"given":"Aopeng","family":"Xu","sequence":"first","affiliation":[{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China"},{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"International Education College, Henan University, Zhengzhou 450046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaqing","family":"Ma","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475004, China"},{"name":"Henan Technology Innovation Center of Spatio-Temporal Big Data, Henan University, Zhengzhou 450046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zixiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Zhengzhou 450046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6497-104X","authenticated-orcid":false,"given":"Tao","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Henan Technology Innovation Center of Spatio-Temporal Big Data, Henan University, Zhengzhou 450046, China"},{"name":"Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Zhengzhou 450046, China"},{"name":"Key Laboratory of Geographical Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"337","DOI":"10.14778\/1687627.1687666","article-title":"Efficient retrieval of the top-k most relevant spatial web objects","volume":"2","author":"Cong","year":"2009","journal-title":"Proc. VLDB Endow."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1109\/TKDE.2010.149","article-title":"IR-Tree: An Efficient Index for Geographic Document Search","volume":"23","author":"Li","year":"2011","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1706","DOI":"10.1109\/TKDE.2016.2530060","article-title":"Inverted Linear Quadtree: Efficient Top k Spatial Keyword Search","volume":"28","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.pmcj.2017.09.009","article-title":"A single quadtree-based algorithm for top-k spatial keyword query","volume":"42","author":"Hong","year":"2017","journal-title":"Pervasive Mob. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1007\/11535331_13","article-title":"Spatio-textual indexing for geographical search on the web","volume":"3633","author":"Vaid","year":"2005","journal-title":"Int. Symp. Spat. Temporal Databases"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1966","DOI":"10.14778\/2367502.2367549","article-title":"DISKs: A system for distributed spatial group keyword search on road networks","volume":"5","author":"Luo","year":"2012","journal-title":"Proc. VLDB Endow."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1109\/TITS.2015.2477837","article-title":"Efficient Collective Spatial Keyword Query Processing on Road Networks","volume":"17","author":"Gao","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.ipl.2016.10.008","article-title":"Group-based collective keyword querying in road networks","volume":"118","author":"Su","year":"2017","journal-title":"Inf. Processing Lett."},{"key":"ref_9","first-page":"416","article-title":"Evaluating Skyline Queries on Spatial Web Objects","volume":"7447","author":"Regalado","year":"2012","journal-title":"Database Expert Syst. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1007\/s10707-015-0243-9","article-title":"Skyline for geo-textual data","volume":"20","author":"Li","year":"2016","journal-title":"GeoInformatica"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1109\/TKDE.2015.2465374","article-title":"Textually relevant spatial skylines","volume":"28","author":"Shi","year":"2016","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2601","DOI":"10.1109\/TKDE.2017.2742956","article-title":"Time-Aware Boolean Spatial Keyword Queries","volume":"29","author":"Chen","year":"2017","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mehta, P., Skoutas, D., and Voisard, A. (2015, January 1\u20134). Spatio-temporal keyword queries for moving objects. Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. Association for Computing Machinery, New York, NY, USA.","DOI":"10.1145\/2820783.2820845"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Nepomnyachiy, S., Gelley, B., Jiang, W., and Minkus, T. (2014, January 1\u20138). What, where, and when: Keyword search with spatio-temporal ranges. Proceedings of the 8th Workshop on Geographic Information Retrieval, Dallas, TX, USA.","DOI":"10.1145\/2675354.2675358"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, D., Tan, K.L., and Tung, A.K.H. (2013, January 18\u201322). Scalable top-k spatial keyword search. Proceedings of the 16th International Conference on Extending Database Technology, Genoa, Italy.","DOI":"10.1145\/2452376.2452419"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Christoforaki, M., He, J., Dimopoulos, C., Markowetz, A., and Suel, T. (2011, January 24\u201328). Text vs. space: Efficient geo-search query processing. Proceedings of the 20th ACM International Conference on Information and Knowledge Management, Glasgow, UK.","DOI":"10.1145\/2063576.2063641"},{"key":"ref_17","unstructured":"Felipe, I.D., Hristidis, V., and Rishe, N. (2008, January 7\u201312). Keyword Search on Spatial Databases. Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, Cancun, Mexico."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, D., Chee, Y.M., Mondal, A., Tung, A.K.H., and Kitsuregawa, M. (April, January 29). Keyword Search in Spatial Databases: Towards Searching by Document. Proceedings of the 2009 IEEE 25th International Conference on Data Engineering, Shanghai, China.","DOI":"10.1109\/ICDE.2009.77"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1889","DOI":"10.1109\/TKDE.2011.172","article-title":"Joint Top-K Spatial Keyword Query Processing","volume":"24","author":"Wu","year":"2012","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.neucom.2020.02.129","article-title":"CISK: An interactive framework for conceptual inference based spatial keyword query","volume":"428","author":"Xu","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_21","first-page":"445","article-title":"Restructuring lattice theory: An approach based on hierarchies of concepts","volume":"Volume 83","author":"Rival","year":"1982","journal-title":"NATO Advanced Study Institutes Series"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1080\/02693799308901953","article-title":"Modelling spatial relations and operations with partially ordered sets","volume":"7","author":"Kainz","year":"1993","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"ref_23","unstructured":"Bian, F., Li, J., Zhang, W., Hu, R., Wang, J., Li, L., Wu, W., Liu, W., Wang, H., and Zhang, H. (2007, January 21\u201325). A Research about Spatial Association Rule Mining Based on Concept Lattice. Proceedings of the 2007 International Conference on Wireless Communications, Networking and Mobile Computing, Shanghai, China."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tripathy, A., Mishra, L., and Patra, P.K. (2010, January 19\u201320). A multi dimensional design framework for querying spatial data using concept lattice. Proceedings of the 2010 IEEE 2nd International Advance Computing Conference, Patiala, India.","DOI":"10.1109\/IADCC.2010.5422922"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/335191.335372","article-title":"Mining Frequent Patterns without Candidate Generation","volume":"29","author":"Han","year":"2000","journal-title":"ACM SIGMOD Record"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"373","DOI":"10.14778\/1920841.1920891","article-title":"Retrieving top-k prestige-based relevant spatial web objects","volume":"3","author":"Cao","year":"2010","journal-title":"Proc. VLDB Endow."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.ins.2016.10.033","article-title":"Level-aware collective spatial keyword queries","volume":"378","author":"Zhang","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Fang, Y., Cheng, R., Cong, G., Mamoulis, N., and Li, Y. (2018, January 16\u201319). On Spatial Pattern Matching. Proceedings of the 2018 IEEE 34th International Conference on Data Engineering, Paris, France.","DOI":"10.1109\/ICDE.2018.00035"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1007\/978-3-319-22363-6_23","article-title":"Geo-Social Keyword Search","volume":"9239","author":"Ahuja","year":"2015","journal-title":"Adv. Spat. Temporal Databases. SSTD"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jiang, J., Lu, H., Yang, B., and Cui, B. (2015, January 13\u201317). Finding top-k local users in geo-tagged social media data. Proceedings of the 2015 IEEE 31st International Conference on Data Engineering, Seoul, Korea.","DOI":"10.1109\/ICDE.2015.7113290"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wu, D., Li, Y., Choi, B., and Xu, J. (2014, January 14\u201318). Social-Aware Top-k Spatial Keyword Search. Proceedings of the 2014 IEEE 15th International Conference on Mobile Data Management, Brisbane, QLD, Australia.","DOI":"10.1109\/MDM.2014.35"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Shekhar, S., Gunturi, V., Evans, M.R., and Yang, K.S. (2012, January 1\u20136). Spatial big-data challenges intersecting mobility and cloud computing. Proceedings of the Eleventh ACM International Workshop on Data Engineering for Wireless and Mobile Access, Scottsdale, AZ, USA.","DOI":"10.1145\/2258056.2258058"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s10586-015-0512-2","article-title":"Geographical information system parallelization for spatial big data processing: A review","volume":"19","author":"Zhao","year":"2016","journal-title":"Clust. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"G\u00f6bel, R., Henrich, A., Niemann, R., and Blank, D. (2009, January 2\u20136). A hybrid index structure for geo-textual searches. Proceedings of the 18th ACM Conference on Information and Knowledge Management, Hong Kong, China.","DOI":"10.1145\/1645953.1646188"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1007\/s00778-012-0271-0","article-title":"A framework for efficient spatial web object retrieval","volume":"21","author":"Wu","year":"2012","journal-title":"VLDB J."},{"key":"ref_36","first-page":"450","article-title":"Hybrid Indexing and Seamless Ranking of Spatial and Textual Features of Web Documents","volume":"6261","author":"Khodaei","year":"2010","journal-title":"Database Expert Syst. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chen, Y.Y., Suel, T., and Markowetz, A. (2006, January 27\u201329). Efficient query processing in geographic web search engines. Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, Chicago, IL, USA.","DOI":"10.1145\/1142473.1142505"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4327","DOI":"10.1007\/s00500-020-05444-z","article-title":"Periodic pattern mining from spatio-temporal database using novel global pollination artificial fish swarm optimizer-based clustering and modified FP tree","volume":"25","author":"Upadhyay","year":"2021","journal-title":"Soft Comput."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, J., Kong, X., and Philip, S.Y. (2013, January 7\u201310). Predicting Social Links for New Users across Aligned Heterogeneous Social Networks. Proceedings of the 2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA.","DOI":"10.1109\/ICDM.2013.134"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1140\/epjds\/s13688-016-0087-z","article-title":"A multilayer approach to multiplexity and link prediction in online geo-social networks","volume":"5","author":"Hristova","year":"2016","journal-title":"EPJ Data Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10707-019-00372-z","article-title":"S2R-tree: A pivot-based indexing structure for semantic-aware spatial keyword search","volume":"24","author":"Chen","year":"2020","journal-title":"Geoinformatica"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/BF00058654","article-title":"A Lattice Conceptual Clustering System and Its Application to Browsing Retrieval","volume":"24","author":"Carpineto","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/TKDE.2006.18","article-title":"A basic mathematical framework for conceptual graphs","volume":"18","author":"Nguyen","year":"2006","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1109\/TCBB.2015.2443805","article-title":"Using Formal Concept Analysis to Identify Negative Correlations in Gene Expression Data","volume":"13","author":"Tu","year":"2016","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1109\/JSYST.2015.2457244","article-title":"Using Concept Lattice for Personalized Recommendation System Design","volume":"11","author":"Zou","year":"2017","journal-title":"IEEE Syst. J."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1109\/TSE.2007.70723","article-title":"Applying Concept Analysis to User-Session-Based Testing of Web Applications","volume":"33","author":"Sampath","year":"2007","journal-title":"IEEE Trans. Softw. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Guttman, A. (1984, January 18\u201321). R-trees: A dynamic index structure for spatial searching. Proceedings of the 1984 ACM SIGMOD International Conference on Management of Data, Boston, MA, USA.","DOI":"10.1145\/602264.602266"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/5\/312\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:10:56Z","timestamp":1760137856000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/5\/312"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,15]]},"references-count":47,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["ijgi11050312"],"URL":"https:\/\/doi.org\/10.3390\/ijgi11050312","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2022,5,15]]}}}