Abstract
The process of computing a representative trajectory for a set of raw (or even semantically enriched) trajectories is an attractive solution to minimize several challenges related to trajectory management, like trajectory data integration or trajectory pattern analysis. We identify two main strategies for accomplishing such a process (trajectory data summarization and trajectory data fusion), but we argue that this subject is still an open issue, and we did not find a survey with such a focus. In order to fill this literature gap, this paper presents a survey that analyzes several issues around the two aforementioned strategies, like the type of representative data computed by each approach, the dimensions that are considered by the approach (spatial, temporal, and semantics), the accomplished methods of the proposed processes, and how the process is evaluated. Additionally, we compare these two research areas (trajectory summarization and trajectory fusion) in literature to analyze their relationship. Finally, some open issues related to this subject are also pointed out.
Similar content being viewed by others
Notes
The selection problem consists of selecting the most appropriate elements of a predefined set of elements, i.e., the best ones from a given collection [23].
References
dos Santos Mello R, Bogorny V, Alvares LO, Santana LHZ, Ferrero CA, Frozza AA, Schreiner GA, Renso C (2019) MASTER: A multiple aspect view on trajectories. Trans GIS 23(4):805–822
Richly K (2018) A survey on trajectory data management for hybrid transactional and analytical workloads. In: 2018 IEEE International conference on big data (Big Data), pp 562–569. IEEE, Seattle, United States
Su H, Liu S, Zheng B, Zhou X, Zheng K (2020) A survey of trajectory distance measures and performance evaluation. VLDB J 29(1):3–32
Wang S, Bao Z, Culpepper JS, Cong G (2021) A survey on trajectory data management, analytics, and learning. ACM Comput Surv 54(2)
Feng Z, Zhu Y (2016) A survey on trajectory data mining: Techniques and applications. IEEE Access 4:2056–2067
Georgiou H, Karagiorgou S, Kontoulis Y, Pelekis N, Petrou P, Scarlatti D, Theodoridis Y (2018) Moving objects analytics: Survey on future location & trajectory prediction methods. arXiv: abs/1807.04639
Bian J, Tian D, Tang Y, Tao D (2018) A survey on trajectory clustering analysis. CoRR arXiv: 1802.06971
Leite da Silva C, May Petry L, Bogorny V (2019) A survey and comparison of trajectory classification methods. In: 2019 8th Brazilian conference on intelligent systems (BRACIS), pp 788–793. IEEE, Brazil
Fiore M, Katsikouli P, Zavou E, Cunche M, Fessant F, Hello DL, Aïvodji UM, Olivier B, Quertier T, Stanica R (2019) Privacy of trajectory micro-data : a survey. ArXiv: 1903.12211
Ahmed SA, Dogra DP, Kar S, Roy PP (2019) Trajectory-based surveillance analysis: A survey. IEEE Trans Circuits Syst Video Technol 29(7):1985–1997
Esteban J, Starr A, Willetts R, Hannah P, Bryanston-Cross P (2005) A review of data fusion models and architectures: towards engineering guidelines. Neural Comput Appl 14(4):273–281
Hall DL, Llinas J (1997) An introduction to multisensor data fusion. Proc IEEE 85(1):6–23
Doan A, Halevy A, Ives Z (2012) Principles of Data Integration. Morgan Kaufmann, Burlington, United States
Zhao H, Ram S (2007) Combining schema and instance information for integrating heterogeneous data sources. Data Knowl Eng 61(2):281–303
Dong XL, Srivastava D (2015) Big Data Integration vol 7, pp 1–198. Morgan & Claypool Publishers, Williston, United States
Sazontev V (2018) Methods for big data integration in distributed computation environments. In: XX International conference on data analytics and management in data intensive domains (DAMDID/RCDL 2018), Moscow, Russia, pp 238–244
Ma B, Jiang T, Zhou X, Zhao F, Yang Y (2017) A novel data integration framework based on unified concept model. IEEE Access 5:5713–5722
Taleb I, Serhani MA, Bouhaddioui C, Dssouli R (2021) Big data quality framework: a holistic approach to continuous quality management. J Big Data 8(1):1–41
Hesabi ZR, Tari Z, Goscinski A, Fahad A, Khalil I, Queiroz C (2015) Data summarization techniques for big data–a survey. In: Khan SU, Zomaya AY (eds) Handbook on Data Centers. Springer, New York, United States, pp 1109–1152
Chandola V, Kumar V (2007) Summarization-compressing data into an informative representation. Knowl Inf Syst 12:355–378
Ahmed M (2019) Data summarization: a survey. Knowl Inf Syst 58(2):249–273
Blelloch GE (2013) Introduction to data compression*. Computer Science Department, Carnegie Mellon University, 55
Desu MM (1970) A selection problem. Ann Math Stat 41(5):1596–1603
Nakamura EF, Loureiro AA, Frery AC (2007) Information fusion for wireless sensor networks: Methods, models, and classifications. ACM Comput Surv (CSUR) 39(3):9
Daoui M, Lalam M, Hamrioui S, Djamah B, Nouali D (2012) Circuit of data aggregation on the fly for wsn. Sens Transd 142(7):44
Amigo D, Sánchez Pedroche D, García J, Molina JM (2021) Review and classification of trajectory summarisation algorithms: From compression to segmentation. Int J Distrib Sens Netw 17(10):15501477211050728
Martinez D, Cristobal S, Belkoura S (2018) Smart data fusion: Probabilistic record linkage adapted to merge two trajectories from different sources. Proceedings of the SESAR Innovation Days],(Dec 2018)
Gao C, Zhao Y, Wu R, Yang Q, Shao J (2019) Semantic trajectory compression via multi-resolution synchronization-based clustering. Knowl-Based Syst 174:177–193
Lee J-G, Han J, Whang K-Y (2007) Trajectory clustering: A partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD international conference on management of data. SIGMOD ’07, pp 593–604. Association for Computing Machinery (ACM), New York, United States
Panagiotakis C, Pelekis N, Kopanakis I, Ramasso E, Theodoridis Y (2012) Segmentation and sampling of moving object trajectories based on representativeness. IEEE Trans Knowl Data Eng 24(7):1328–1343
Wang H, Su H, Zheng K, Sadiq S, Zhou X (2013) An effectiveness study on trajectory similarity measures. Proceedings of the twenty-fourth Australasian database conference 137, 13–22. Australian Computer Society, Inc
Buchin K, Buchin M, Van Kreveld M, Löffler M, Silveira RI, Wenk C, Wiratma L (2013) Median trajectories. Algorithmica 66(3):595–614
Berndt DJ, Clifford J (1994) Using dynamic time warping to find patterns in time series. In: Proceedings of the 3rd international conference on knowledge discovery and data mining. AAAIWS’94, pp 359–370. AAAI Press, Seattle, WA
Vlachos M, Kollios G, Gunopulos D (2002) Discovering similar multidimensional trajectories. In: Proceedings 18th international conference on data engineering, pp 673–684. IEEE, San Jose, United States
Chen L, Özsu MT, Oria V (2005) Robust and fast similarity search for moving object trajectories. In: Proceedings of the 2005 ACM SIGMOD international conference on management of data. SIGMOD ’05, pp. 491–502. Association for Computing Machinery (ACM), Baltimore, Maryland
Peixoto DA (2018) A distributed in-memory database system for large-scale spatial-temporal trajectory data. PhD thesis, University of Queensland, Australia. Doctor of Philosophy - School of Information Technology and Electrical Engineering
Buchin M, Kilgus B, Kölzsch A (2019) Group diagrams for representing trajectories. Int J Geogr Inf Sci 34(12):2401–2433
Eiter T, Mannila H (1994) Computing discrete frechet distance. Technical report cd-tr 94/64, Christian Doppler Laboratory for Expert Systems, TU Vienna - Austria
Ying X, Xu Z, Yin WG (2009) Cluster-based congestion outlier detection method on trajectory data. In: 2009 Sixth international conference on fuzzy systems and knowledge discovery, vol. 5, pp. 243–247. IEEE
Frentzos E, Gratsias K, Pelekis N, Theodoridis Y (2007) Algorithms for nearest neighbor search on moving object trajectories. Geoinformatica 11:159–193
Furtado AS, Alvares LOC, Pelekis N, Theodoridis Y, Bogorny V (2018) Unveiling movement uncertainty for robust trajectory similarity analysis. Int J Geogr Inf Sci 32(1):140–168
Furtado AS, Kopanaki D, Alvares LO, Bogorny V (2016) Multidimensional similarity measuring for semantic trajectories. Trans GIS 20(2):280–298
Lehmann AL, Alvares LO, Bogorny V (2019) SMSM: a similarity measure for trajectory stops and moves. Int J Geogr Inf Sci 33(9):1847–1872
Petry LM, Ferrero CA, Alvares LO, Renso C, Bogorny V (2019) Towards semantic-aware multiple-aspect trajectory similarity measuring. Trans GIS 23(5):960–975
Xie P, Li T, Liu J, Du S, Yang X, Zhang J (2020) Urban flow prediction from spatiotemporal data using machine learning: A survey. Inf Fus 59:1–12
de Almeida DR, de Souza Baptista C, de Andrade FG, Soares A (2020) A survey on big data for trajectory analytics. ISPRS Int J Geo-Information 9(2):88
Kong X, Li M, Ma K, Tian K, Wang M, Ning Z, Xia F (2018) Big trajectory data: A survey of applications and services. IEEE Access 6:58295–58306
Ayhan S, Samet H (2015) Diclerge: Divide-cluster-merge framework for clustering aircraft trajectories. In: Proceedings of the 8th ACM SIGSPATIAL international workshop on computational transportation science, pp 7–14
Etienne L, Devogele T, Buchin M, McArdle G (2016) Trajectory box plot: A new pattern to summarize movements. Int J Geogr Inf Sci 30(5):835–853
Borkowski P (2017) The ship movement trajectory prediction algorithm using navigational data fusion. Sensors 17(6):1432
Agarwal PK, Fox K, Munagala K, Nath A, Pan J, Taylor E (2018) Subtrajectory clustering: Models and algorithms. In: Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI symposium on principles of database systems, pp 75–87
Seep J, Vahrenhold J (2019) Inferring semantically enriched representative trajectories. In: Proceedings of the 1st ACM SIGSPATIAL international workshop on computing with multifaceted movement data. MOVE’19, pp 1–4. Association for Computing Machinery, New York, United States
Zheng C, Peng Q, Xu X (2020) Heterogenous multi-source fusion for ship trajectory complement and prediction with sequence modeling. In: 2020 IEEE Fifth international conference on data science in cyberspace (DSC), pp 15–21. IEEE
Rodriguez DF, Ortiz AE (2020) Detecting representative trajectories in moving objects databases from clusters. In: International conference on information technology & systems, pp 141–151. Springer
Li H (2021) Typical trajectory extraction method for ships based on ais data and trajectory clustering. In: 2021 2nd International conference on artificial intelligence and information systems, pp 1–8
Machado VL, Mello RdS, Bogorny V (2022) A method for summarizing trajectories with multiple aspects. In: International conference on database and expert systems applications, pp 433–446. Springer
Ruan S, Li R, Bao J, He T, Zheng Y (2018) Cloudtp: A cloud-based flexible trajectory preprocessing framework. In: 2018 IEEE 34th international conference on data engineering (ICDE), pp 1601–1604. IEEE
Lian J, Zhang L (2018) One-month beijing taxi gps trajectory dataset with taxi ids and vehicle status. In: Proceedings of the first workshop on data acquisition to analysis, pp 3–4
Yang D, Zhang D, Zheng VW, Yu Z (2015) Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs. IEEE Trans Syst Man Cybern: Syst 45(1):129–142
Santipantakis GM, Glenis A, Patroumpas K, Vlachou A, Doulkeridis C, Vouros GA, Pelekis N, Theodoridis Y (2018) Spartan: Semantic integration of big spatio-temporal data from streaming and archival sources. Future Gener Comput Syst 110:540–555
Funding
This work has been partially supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina (FAPESC) by the MATCH Project (Co-financing of H2020 Projects - Grant 2018TR 1266), as well as the European Union’s Horizon 2020 research and innovation programme under GA N. 777695 (EU Project MASTER - Multiple ASpects TrajEctoRy management and analysis). The views and opinions expressed in this article are the authors’ sole responsibility and do not necessarily reflect the views of the European Commission.
Author information
Authors and Affiliations
Contributions
Vanessa Lago Machado and Ronaldo dos Santos Mello wrote the main manuscript text. All authors had reviewed and approved the manuscript and contributed significantly to the paper.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Machado, V.L., Mello, R.d.S., Bogorny, V. et al. A survey on the computation of representative trajectories. Geoinformatica 28, 605–630 (2024). https://doi.org/10.1007/s10707-024-00514-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10707-024-00514-y