{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T05:45:44Z","timestamp":1790833544186,"version":"4.1.0"},"reference-count":90,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,2,1]],"date-time":"2020-02-01T00:00:00Z","timestamp":1580515200000},"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>Trajectory data allow the study of the behavior of moving objects, from humans to animals. Wireless communication, mobile devices, and technologies such as Global Positioning System (GPS) have contributed to the growth of the trajectory research field. With the considerable growth in the volume of trajectory data, storing such data into Spatial Database Management Systems (SDBMS) has become challenging. Hence, Spatial Big Data emerges as a data management technology for indexing, storing, and retrieving large volumes of spatio-temporal data. A Data Warehouse (DW) is one of the premier Big Data analysis and complex query processing infrastructures. Trajectory Data Warehouses (TDW) emerge as a DW dedicated to trajectory data analysis. A list and discussions on problems that use TDW and forward directions for the works in this field are the primary goals of this survey. This article collected state-of-the-art on Big Data trajectory analytics. Understanding how the research in trajectory data are being conducted, what main techniques have been used, and how they can be embedded in an Online Analytical Processing (OLAP) architecture can enhance the efficiency and development of decision-making systems that deal with trajectory data.<\/jats:p>","DOI":"10.3390\/ijgi9020088","type":"journal-article","created":{"date-parts":[[2020,2,3]],"date-time":"2020-02-03T11:28:31Z","timestamp":1580729311000},"page":"88","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["A Survey on Big Data for Trajectory Analytics"],"prefix":"10.3390","volume":"9","author":[{"given":"Dami\u00e3o","family":"Ribeiro de Almeida","sequence":"first","affiliation":[{"name":"Campina Grande, Department of Computer Science, Federal University of Campina Grande, Para\u00edba 58429-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cl\u00e1udio","family":"de Souza Baptista","sequence":"additional","affiliation":[{"name":"Campina Grande, Department of Computer Science, Federal University of Campina Grande, Para\u00edba 58429-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabio","family":"Gomes de Andrade","sequence":"additional","affiliation":[{"name":"Federal Institute of Para\u00edba, Cajazeiras, Para\u00edba 58900-000, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5957-3805","authenticated-orcid":false,"given":"Amilcar","family":"Soares","sequence":"additional","affiliation":[{"name":"Institute for Big Data Analytics, Dalhousie University, Halifax, NS B3H 1W5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1145\/2743025","article-title":"Trajectory Data Mining: An Overview","volume":"6","author":"Zheng","year":"2015","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Vaisman, A., and Zim\u00e1nyi, E. (2014). Conceptual Data Warehouse Design. Data Warehouse Systems, Springer.","DOI":"10.1007\/978-3-642-54655-6"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1111\/tgis.12011","article-title":"Constant\u2014A Conceptual Data Model for Semantic Trajectories of Moving Objects","volume":"18","author":"Bogorny","year":"2014","journal-title":"Trans. GIS"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kolovson, C.P., Neimat, M.A., and Potamianos, S. (1993). Interoperability of Spatial and Attribute Data Managers: A Case Study, Springer.","DOI":"10.1007\/3-540-56869-7_14"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s10707-012-0158-7","article-title":"A Generic Data Model for Moving Objects","volume":"17","author":"Xu","year":"2013","journal-title":"GeoInformatica"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.bdr.2015.01.006","article-title":"Significance and Challenges of Big Data Research","volume":"2","author":"Jin","year":"2015","journal-title":"Big Data Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1016\/j.future.2018.04.053","article-title":"Big Data for Internet of Things: A Survey","volume":"87","author":"Ge","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Shekhar, S., Gunturi, V., Evans, M.R., and Yang, K. (2012, January 20). 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_9","doi-asserted-by":"crossref","unstructured":"B\u00e9dard, Y., Rivest, S., and Proulx, M.J. (2007). Spatial Online Analytical Processing (SOLAP): Concepts, Architectures, and Solutions from a Geomatics Engineering Perspective. Data Warehouses and OLAP: Concepts, Architectures and Solutions, IGI Global.","DOI":"10.4018\/987-1-59904-364-7.ch013"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1145\/2501654.2501656","article-title":"Semantic Trajectories Modeling and Analysis","volume":"45","author":"Parent","year":"2013","journal-title":"ACM Comput. Surv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"58295","DOI":"10.1109\/ACCESS.2018.2873779","article-title":"Big Trajectory Data: A Survey of Applications and Services","volume":"6","author":"Kong","year":"2018","journal-title":"IEEE Access"},{"key":"ref_12","unstructured":"Bian, J., Tian, D., Tang, Y., and Tao, D. (2018). A Survey on Trajectory Clustering Analysis. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2056","DOI":"10.1109\/ACCESS.2016.2553681","article-title":"A Survey on Trajectory Data Mining: Techniques and Applications","volume":"4","author":"Feng","year":"2016","journal-title":"IEEE Access"},{"key":"ref_14","first-page":"1","article-title":"A Survey on Trajectory Data Warehouse","volume":"28","author":"Alsahfi","year":"2019","journal-title":"Spat. Inf. Res."},{"key":"ref_15","unstructured":"Fileto, R., Raffaet\u00e0, A., Roncato, A., Sacenti, J.A., May, C., and Klein, D. (, January November). A Semantic Model for Movement Data Warehouses. Proceedings of the 17th International Workshop on Data Warehousing and OLAP, Shanghai, China."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Nardini, F.M., Orlando, S., Perego, R., Raffaet\u00e0, A., Renso, C., and Silvestri, C. (2018). Analysing Trajectories of Mobile Users: From Data Warehouses to Recommender Systems. A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years, Springer.","DOI":"10.1007\/978-3-319-61893-7_24"},{"key":"ref_17","unstructured":"Andrienko, N.V., and Andrienko, G.L. (2020, January 31). Visual Analytics of Movement: A Rich Palette of Techniques to Enable Understanding 2013. Available online: https:\/\/www.cambridge.org\/core\/books\/mobility-data\/visual-analytics-of-movement-a-rich-palette-of-techniques-to-enable-understanding\/D8CF79BD836291437ED501B4965498B8."},{"key":"ref_18","unstructured":"Etemad, M., J\u00fanior, A.S., Hoseyni, A., Rose, J., and Matwin, S. (2020, January 31). A Trajectory Segmentation Algorithm Based on Interpolation-based Change Detection Strategies. EDBT\/ICDT Workshops. Available online: http:\/\/ceur-ws.org\/Vol-2322\/BMDA_4.pdf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1080\/13658816.2014.938078","article-title":"GRASP-UTS: An Algorithm for Unsupervised Trajectory Segmentation","volume":"29","author":"Moreno","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_20","unstructured":"Junior, A.S., Times, V.C., Renso, C., Matwin, S., and Cabral, L.A. (2018, January 28). A Semi-Supervised Approach for the Semantic Segmentation of Trajectories. Proceedings of the 2018 19th IEEE International Conference on Mobile Data Management (MDM), Aalborg, Denmark."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s10708-007-9111-y","article-title":"Citizens as Sensors: The World of Volunteered Geography","volume":"69","author":"Goodchild","year":"2007","journal-title":"GeoJournal"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Granell, C., Schade, S., and Hobona, G. (2011). Linked Data: Connecting Spatial Data Infrastructures and Volunteered Geographic Information. Geospatial Web Services: Advances in Information Interoperability, IGI Global.","DOI":"10.4018\/978-1-60960-192-8.ch009"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Renso, C., Spaccapietra, S., and Zim\u00e1nyi, E. (2013). Mobility Data, Cambridge University Press.","DOI":"10.1017\/CBO9781139128926"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zheng, Y., and Xie, X. (2010, January 21). Learning Location Correlation from GPS Trajectories. Proceedings of the 2010 Eleventh International Conference on Mobile Data Management, Kansas City, MO, USA.","DOI":"10.1109\/MDM.2010.42"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Krumm, J., and Horvitz, E. (2006). Predestination: Inferring Destinations from Partial Trajectories, Springer.","DOI":"10.1007\/11853565_15"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yan, Z., Chakraborty, D., Parent, C., Spaccapietra, S., and Aberer, K. (2011, January 21). SeMiTri: A Framework for Semantic Annotation of Heterogeneous Trajectories. Proceedings of the 14th International Conference on Extending Database Technology, Uppsala, Sweden.","DOI":"10.1145\/1951365.1951398"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.datak.2007.10.008","article-title":"A conceptual View on Trajectories","volume":"65","author":"Spaccapietra","year":"2008","journal-title":"Data Knowl. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Spaccapietra, S., and Parent, C. (2011). Adding Meaning to Your Steps, Springer.","DOI":"10.1007\/978-3-642-24606-7_2"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1145\/2782759.2782762","article-title":"The Low Hanging Fruit is Gone: Achievements and Challenges of Computational Movement Analysis","volume":"7","author":"Laube","year":"2015","journal-title":"SIGSPATIAL Spec."},{"key":"ref_30","unstructured":"Nabo, R.G., Fileto, R., Nanni, M., and Renso, C. (2014, January 29). Annotating Trajectories by Fusing them with Social Media Users Posts. Proceedings of the XV Brazilian Symposium on Geoinformatics (GeoInfo), Campos do Jord\u00e3o, Brazil."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wagner, R., de Macedo, J.A.F., Raffaet\u00e0, A., Renso, C., Roncato, A., and Trasarti, R. (2013). Mob-Warehouse: A Semantic Approach for Mobility Analysis with a Trajectory Data Warehouse, Springer.","DOI":"10.1007\/978-3-319-14139-8_15"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.datak.2015.07.010","article-title":"The Baquara2 Knowledge-Based Framework for Semantic Enrichment and Analysis of Movement Data","volume":"98","author":"Fileto","year":"2015","journal-title":"Data Knowl. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1111\/tgis.12526","article-title":"MASTER: A Multiple Aspect View on Trajectories","volume":"23","author":"Mello","year":"2019","journal-title":"Trans. GIS"},{"key":"ref_34","unstructured":"Malinowski, E., and Zimanyi, E. (2008). Advanced Data Warehouse Design\u2014From Conventional to Spatial and Temporal Applications, Springer. Data-Centric Systems and Applications."},{"key":"ref_35","first-page":"571","article-title":"Using semantic web technologies for exploratory OLAP: A survey","volume":"27","author":"Romero","year":"2014","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_36","first-page":"61","article-title":"Trajectory Data Warehouses: Proposal of Design and Application to Exploit Data","volume":"9","author":"Braz","year":"2007","journal-title":"GeoInfo"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"211","DOI":"10.5626\/JCSE.2007.1.2.211","article-title":"Trajectory Data Warehouses: Design and Implementation Issues","volume":"1","author":"Orlando","year":"2007","journal-title":"J. Comput. Sci. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Marketos, G., Frentzos, E., Ntoutsi, I., Pelekis, N., Raffaet\u00e0, A., and Theodoridis, Y. (2008, January 13). Building Real-World Trajectory Warehouses. Proceedings of the Seventh ACM International Workshop on Data Engineering for Wireless and Mobile Access, Vancouver, BC, Canada.","DOI":"10.1145\/1626536.1626539"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Leonardi, L., Marketos, G., Frentzos, E., Giatrakos, N., Orlando, S., Pelekis, N., Raffaet\u00e0, A., Roncato, A., Silvestri, C., and Theodoridis, Y. T-warehouse: Visual Olap Analysis on Trajectory Data. Proceedings of the 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010), Long Beach, CA, USA.","DOI":"10.1109\/ICDE.2010.5447749"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/s10707-013-0181-3","article-title":"A General Framework for Trajectory Data Warehousing and Visual OLAP","volume":"18","author":"Leonardi","year":"2014","journal-title":"GeoInformatica"},{"key":"ref_41","unstructured":"Silva, M.C.T., Times, V.C., de Mac\u00eado, J.A., and Renso, C. (2015, January 19). SWOT: A Conceptual Data Warehouse Model for Semantic Trajectories. Proceedings of the ACM Eighteenth International Workshop on Data Warehousing and OLAP, Melbourne, VIC, Australia."},{"key":"ref_42","unstructured":"Bao, J., Li, R., Yi, X., and Zheng, Y. (, January October). Managing Massive Trajectories on the Cloud. Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Burlingame, CA, USA."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Gali\u0107, Z. (2016). Spatio-Temporal Data Streams and Big Data Paradigm. Spatio-Temporal Data Streams, Springer.","DOI":"10.1007\/978-1-4939-6575-5"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Manaa, M., and Akaichi, J. (2016). Ontology-Based Trajectory Data Warehouse Conceptual Model, Springer.","DOI":"10.1007\/978-3-319-43946-4_22"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MCG.2017.3621221","article-title":"ANALYTiC: An Active Learning System for Trajectory Classification","volume":"37","author":"Renso","year":"2017","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Jin, C., Mao, J., Yang, X., and Zhou, A. (2017). Trajspark: A Scalable and Efficient in-memory Management System for Big Trajectory Data, Springer.","DOI":"10.1007\/978-3-319-63579-8_2"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1007\/s10707-018-0325-6","article-title":"St-hadoop: A Mapreduce Framework for Spatio-Temporal Data","volume":"22","author":"Alarabi","year":"2018","journal-title":"GeoInformatica"},{"key":"ref_48","unstructured":"Dividino, R., Soares, A., Matwin, S., Isenor, A.W., Webb, S., and Brousseau, M. (2018). Semantic Integration of Real-Time Heterogeneous Data Streams for Ocean-Related Decision Making. Big Data Artif. Intell. Mil. Decis. Mak. STO."},{"key":"ref_49","unstructured":"Nikitopoulos, P., Vlachou, A., Doulkeridis, C., and Vouros, G.A. (2018, January 26). DiStRDF: Distributed Spatio-temporal RDF Queries on Spark. Proceedings of the EDBT\/ICDT Workshops, Vienna, Austria."},{"key":"ref_50","unstructured":"Soares, A., Rose, J., Etemad, M., Renso, C., and Matwin, S. (2019, January 26). VISTA: A Visual Analytics Platform for Semantic Annotation of Trajectories. Proceedings of the 22nd International Conference on Extending Database Technology (EDBT), Lisbon, Portugal."},{"key":"ref_51","unstructured":"Georgiou, H., Karagiorgou, S., Kontoulis, Y., Pelekis, N., Petrou, P., Scarlatti, D., and Theodoridis, Y. (2018). Moving Objects Analytics: Survey on Future Location & Trajectory Prediction Methods. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zheng, Y., and Zhou, X. (2011). Computing with Spatial Trajectories, Springer Science & Business Media.","DOI":"10.1007\/978-1-4614-1629-6"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Meratnia, N., and Rolf, A. (2004). Spatiotemporal Compression Techniques for Moving Point Objects, Springer.","DOI":"10.1007\/978-3-540-24741-8_44"},{"key":"ref_54","unstructured":"Potamias, M., Patroumpas, K., and Sellis, T. (2006, January 3\u20135). Sampling trajectory streams with spatiotemporal criteria. Proceedings of the 18th International Conference on Scientific and Statistical Database Management (SSDBM\u201906), Vienna, Austria."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.bdr.2015.01.003","article-title":"Geospatial Big Data: Challenges and Opportunities","volume":"2","author":"Lee","year":"2015","journal-title":"Big Data Res."},{"key":"ref_56","unstructured":"Burrough, P.A., McDonnell, R., McDonnell, R.A., and Lloyd, C.D. (2015). Principles of Geographical Information Systems, Oxford University Press."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1080\/02693798708927790","article-title":"Requirements and Principles for the Implementation and Construction of Large-Scale Geographic Information Systems","volume":"1","author":"Smith","year":"1987","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/s10707-016-0264-z","article-title":"Distributed Processing of Big Mobility Data as Spatio-Temporal Data Streams","volume":"21","year":"2017","journal-title":"Geoinformatica"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1145\/1107499.1107502","article-title":"From Databases to Dataspaces: A New Abstraction for Information Management","volume":"34","author":"Franklin","year":"2005","journal-title":"ACM Sigmod Rec."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1516","DOI":"10.14778\/1454159.1454217","article-title":"A First, Tutorial on Dataspaces","volume":"1","author":"Franklin","year":"2008","journal-title":"Proc. VLDB Endow."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Halevy, A., Franklin, M., and Maier, D. (2006, January 27). Principles of Dataspace Systems. Proceedings of the Twenty-fifth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, Chicago, IL, USA.","DOI":"10.1145\/1142351.1142352"},{"key":"ref_62","unstructured":"International Organization for Standardization (2002). ISO 19108 Geographic Information\u2014Temporal Schema, International Organization for Standardization. ISO\/TC 211, I."},{"key":"ref_63","unstructured":"International Organization for Standardization (2003). ISO 19107 Geographic Information\u2014Spatial Schema, International Organization for Standardization. ISO\/TC 211, I."},{"key":"ref_64","first-page":"28","article-title":"Apache flink: Stream and Batch Processing in a Single Engine","volume":"36","author":"Carbone","year":"2015","journal-title":"Bull. IEEE Comput. Soc. Tech. Comm. Data Eng."},{"key":"ref_65","unstructured":"Marz, N., and Warren, J. (2015). Big Data: Principles and Best Practices of Scalable Real-Time Data Systems, Manning Publications Co."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Lenka, R.K., Barik, R.K., Gupta, N., Ali, S.M., Rath, A., and Dubey, H. (2016, January 14\u201317). Comparative Analysis of SpatialHadoop and GeoSpark for Geospatial Big Data Analytics. Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), Greater Noida, India.","DOI":"10.1109\/IC3I.2016.7918013"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Marcu, O.C., Costan, A., Antoniu, G., and P\u00e9rez-Hern\u00e1ndez, M.S. (2012, January 24\u201328). Spark Versus Flink: Understanding Performance in Big Data Analytics Frameworks. Proceedings of the 2016 IEEE International Conference on Cluster Computing (CLUSTER), Beijing, China.","DOI":"10.1109\/CLUSTER.2016.22"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Pelekis, N., Theodoridis, Y., Vosinakis, S., and Panayiotopoulos, T. (2006). Hermes\u2014A Framework for Location-Based Data Management, Springer.","DOI":"10.1007\/11687238_75"},{"key":"ref_69","first-page":"184","article-title":"Workload-Aware RDF Partitioning and SPARQL Query Caching for Massive RDF Graphs Stored in NoSQL Databases","volume":"32","author":"Santana","year":"2017","journal-title":"SBBD"},{"key":"ref_70","first-page":"012024","article-title":"A Novel Visual Interface to Foster Innovation in Mechanical Engineering and Protect from Patent Infringement","volume":"1004","author":"Sorce","year":"2018","journal-title":"J. Phys."},{"key":"ref_71","unstructured":"Newson, P., and Krumm, J. (, January November). Hidden Markov Map Matching through Noise and Sparseness. Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, WA, USA."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Giannotti, F., and Pedreschi, D. (2008). Mobility, Data Mining and Privacy: Geographic Knowledge Discovery, Springer Science & Business Media.","DOI":"10.1007\/978-3-540-75177-9"},{"key":"ref_73","unstructured":"Giannotti, F., Nanni, M., Pedreschi, D., and Renso, C. (2020, January 31). GeoPKDD Geographic Privacy-aware Knowledge Discovery 2009. Available online: https:\/\/pdfs.semanticscholar.org\/f6c8\/d0b66289c78b62e7877cbf60f1f09f1ba72e.pdf."},{"key":"ref_74","unstructured":"Luj\u00e1n-Mora, S., and Trujillo, J. (2003, January 8). A Comprehensive Method for Data Warehouse Design. Proceedings of the 5th International Workshop on Design and Management of Data Warehouses, DMDW\u201903, Berlin, Germany."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1145\/96602.96604","article-title":"Federated Database Systems for Managing Distributed, Heterogeneous, and Autonomous Databases","volume":"22","author":"Sheth","year":"1990","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_76","unstructured":"Rigaux, P., Scholl, M., and Voisard, A. (2001). Spatial Databases: With Application to GIS, Elsevier."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Ponniah, P. (2010). Data Warehousing Fundamentals for IT Professionals, John Wiley & Sons.","DOI":"10.1002\/9780470604137"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"G\u00f3mez, L., Kuijpers, B., Moelans, B., and Vaisman, A. (2013). A State-of-the-art in Spatio-Temporal Data Warehousing, OLAP and Mining. Data Mining: Concepts, Methodologies, Tools, and Applications, IGI Global.","DOI":"10.4018\/978-1-4666-2455-9.ch104"},{"key":"ref_79","unstructured":"Brakatsoulas, S., Pfoser, D., Salas, R., and Wenk, C. (, January August). On Map-Matching Vehicle Tracking Data. Proceedings of the 31st International Conference on Very Large Data Bases, VLDB Endowment, Trondheim Norway."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Ibragimov, D., Hose, K., Pedersen, T.B., and Zim\u00e1nyi, E. (2014). Towards Exploratory OLAP over Linked Open Data\u2014A Case Study. Enabling Real-Time Business Intelligence, Springer.","DOI":"10.1007\/978-3-662-46839-5_8"},{"key":"ref_81","unstructured":"Etcheverry, L., and Vaisman, A.A. (2012, January 12). QB4OLAP: A New Vocabulary for OLAP Cubes on the Semantic Web. Proceedings of the Third International Conference on Consuming Linked Data, Boston, MA, USA."},{"key":"ref_82","first-page":"74","article-title":"Analytics for the Internet of Things: A survey","volume":"51","author":"Siow","year":"2018","journal-title":"ACM Comput. Surv."},{"key":"ref_83","unstructured":"Leite, D.F.B., de Souza Baptista, C., de Oliveira, M.G., Acioli Filho, J.A.M., and da Silva, T.E. (2016, January 2). ExpOLAP: Towards Exploratory OLAP. Proceedings of the 2016 IEEE\/ACS 13th International Conference of Computer Systems and Applications (AICCSA), Agadir, Morocco."},{"key":"ref_84","unstructured":"Rivest, S., B\u00e9dard, Y., Proulx, M.J., and Nadeau, M. (, January October). SOLAP: A New Type of User Interface to Support Spatio-Temporal Multi-dimensional Data Exploration and Analysis. Proceedings of the ISPRS Joint Workshop on Spatial, Temporal and Multi-Dimensional Data Modelling and Analysis, Quebec, QC, Canada."},{"key":"ref_85","first-page":"391","article-title":"An exploratory SOLAP tool for linked open data","volume":"31","author":"Leite","year":"2019","journal-title":"Int. J. Bus. Inf. Syst."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.datak.2018.01.003","article-title":"A Branch and Bound Strategy for Fast Trajectory Similarity Measuring","volume":"115","author":"Furtado","year":"2018","journal-title":"Data Knowl. Eng."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Keskin, S., and Yazici, A. (2018). Modelling and Designing Spatial and Temporal Big Data for Analytics, Springer.","DOI":"10.1007\/978-3-030-00840-6_12"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1007\/s11280-017-0487-4","article-title":"LoTAD: Long-Term Traffic Anomaly Detection Based on Crowdsourced Bus Trajectory Data","volume":"21","author":"Kong","year":"2018","journal-title":"World Wide Web"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Andrienko, N., Andrienko, G., Fuchs, G., and Jankowski, P. (2015). Visual Analytics Methodology for Scalable and Privacy-Respectful Discovery of Place Semantics from Episodic Mobility Data, Springer.","DOI":"10.1007\/978-3-319-23461-8_25"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Kong, L., He, L., Liu, X.Y., Gu, Y., Wu, M.Y., and Liu, X. (2015, January 29). Privacy-Preserving Compressive Sensing for Crowdsensing Based Trajectory Recovery. Proceedings of the 2015 IEEE 35th International Conference on Distributed Computing Systems, Columbus, OH, USA.","DOI":"10.1109\/ICDCS.2015.12"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/2\/88\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:53:45Z","timestamp":1760172825000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/2\/88"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,1]]},"references-count":90,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["ijgi9020088"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9020088","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,1]]}}}