{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T13:42:40Z","timestamp":1758462160686,"version":"3.44.0"},"publisher-location":"Cham","reference-count":53,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032057266","type":"print"},{"value":"9783032057273","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T00:00:00Z","timestamp":1758499200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T00:00:00Z","timestamp":1758499200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-05727-3_5","type":"book-chapter","created":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T07:44:14Z","timestamp":1758440654000},"page":"46-55","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Spatio-Temporal Data and\u00a0Molecular Dynamics: Challenges and\u00a0Opportunities (Vision Paper)"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8839-6278","authenticated-orcid":false,"given":"Goce","family":"Trajcevski","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ashfaq","family":"Khokhar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sohail","family":"Murad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cynthia","family":"Jameson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,22]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Adcock, S.A., McCammon, J.A.: Molecular dynamics: survey of methods for simulating the activity of proteins. Chem. Rev. 106(5) (2006)","DOI":"10.1021\/cr040426m"},{"key":"5_CR2","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.bdr.2017.06.005","volume":"9","author":"M Ahmadian","year":"2017","unstructured":"Ahmadian, M., Zhuang, Y., Hase, W.L., Chen, Y.: Data reduction through increased data utilization in chemical dynamics simulations. Big Data Res. 9, 57\u201366 (2017)","journal-title":"Big Data Res."},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Alam, M.M., Torgo, L., Bifet, A.: A survey on spatio-temporal data analytics systems. ACM Comput. Surv. 54(10s), 219:1\u2013219:38 (2022)","DOI":"10.1145\/3507904"},{"issue":"1","key":"5_CR4","first-page":"87","volume":"20","author":"NV Andrienko","year":"2020","unstructured":"Andrienko, N.V., Andrienko, G.L.: Spatio-temporal visual analytics: a vision for 2020s. J. Spatial Inf. Sci. 20(1), 87\u201395 (2020)","journal-title":"J. Spatial Inf. Sci."},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Astero, M., Rousu, J.: Learning symmetry-aware atom mapping in chemical reactions through deep graph matching. J. Cheminform. 16(46) (2024)","DOI":"10.1186\/s13321-024-00841-0"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Barsky, D., Foloppe, N., Ahmadia, S., III, D.M.W., Jr., A.D.M.: New insights into the structure of ABASIC DNA from molecular dynamics simulations. Nucleic Acids Res. 28(13) (2000)","DOI":"10.1093\/nar\/28.13.2613"},{"key":"5_CR7","doi-asserted-by":"publisher","unstructured":"Belal, Y., Mokhtar, S.B., Haddadi, H., Wang, J., Mashhadi, A.: Survey of federated learning models for spatial-temporal mobility applications. ACM Trans. Spatial Algorithms Syst. 10(3), 18:1\u201318:39 (2024). https:\/\/doi.org\/10.1145\/3666089, https:\/\/doi.org\/10.1145\/3666089","DOI":"10.1145\/3666089"},{"key":"5_CR8","unstructured":"Chen, J., et al.: Mixup-augmented meta-learning for sample-efficient fine-tuning of protein simulators (2023)"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Durrant, J., McCammon, J.: Molecular dynamics simulations and drug discovery. BMC Biol. 9(71) (2011)","DOI":"10.1186\/1741-7007-9-71"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Focardi, S., Cagnacci, F.: Animal movement. In: Mobility Data: Modeling, Management, and Understanding. Cambridge University Press (2013)","DOI":"10.1017\/CBO9781139128926.014"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Fu, Y., Zhao, J., Chen, Z.: Insights into the molecular mechanisms of protein-ligand interactions by molecular docking and molecular dynamics simulation: a case of oligopeptide binding protein. Comput. Math. Methods Medicine 2018 (2018)","DOI":"10.1155\/2018\/3502514"},{"key":"5_CR12","doi-asserted-by":"publisher","unstructured":"Hanser, T.: Federated learning for molecular discovery. Curr. Opin. Struct. Biol. 79 (2023). https:\/\/doi.org\/10.1016\/j.sbi.2023.102545","DOI":"10.1016\/j.sbi.2023.102545"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Herrero, D.A., Pedroche, D.S., Garc\u00eda, J., Molina, J.M.: Review and classification of trajectory summarisation algorithms: From compression to segmentation. Int. J. Distributed Sens. Networks 17(10) (2021)","DOI":"10.1177\/15501477211050729"},{"issue":"1","key":"5_CR14","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s13321-024-00822-3","volume":"16","author":"S Jaeger-Honz","year":"2024","unstructured":"Jaeger-Honz, S., Klein, K., Schreiber, F.: Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data. J. Cheminformatics 16(1), 28 (2024)","journal-title":"J. Cheminformatics"},{"issue":"1","key":"5_CR15","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.dss.2013.01.027","volume":"55","author":"B Jin","year":"2013","unstructured":"Jin, B., Zhuo, W., Hu, J., Chen, H., Yang, Y.: Specifying and detecting spatio-temporal events in the internet of things. Decis. Support Syst. 55(1), 256\u2013269 (2013)","journal-title":"Decis. Support Syst."},{"key":"5_CR16","unstructured":"Rodrigues Jr., J.F., Florea, L., de\u00a0Oliveira, M.C.F., Diamond, D., Jr., O.N.O.: A survey on big data and machine learning for chemistry. CoRR abs\/1904.10370 (2019). http:\/\/arxiv.org\/abs\/1904.10370"},{"issue":"3","key":"5_CR17","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1111\/cgf.14807","volume":"42","author":"B Kale","year":"2023","unstructured":"Kale, B., Clyde, A., Sun, M., Ramanathan, A., Stevens, R., Papka, M.E.: Chemograph: interactive visual exploration of the chemical space. Comput. Graph. Forum 42(3), 13\u201324 (2023)","journal-title":"Comput. Graph. Forum"},{"key":"5_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107332","volume":"230","author":"A Kordzadeh","year":"2023","unstructured":"Kordzadeh, A., Zarif, M., Amjad-Iranagh, S.: Molecular dynamics insight of interaction between the functionalized-carbon nanotube and cancerous cell membrane in doxorubicin delivery. Comput. Methods Programs Biomed. 230, 107332 (2023)","journal-title":"Comput. Methods Programs Biomed."},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Kotnana, S., Han, D., Anderson, T., Z\u00fcfle, A., Kavak, H.: Using generative adversarial networks to assist synthetic population creation for simulations. In: ANNSIM, pp. 1\u201312. IEEE (2022)","DOI":"10.23919\/ANNSIM55834.2022.9859422"},{"key":"5_CR20","doi-asserted-by":"publisher","unstructured":"Koubarakis, M., et al. (eds.): Spatio-Temporal Databases: The CHOROCHRONOS Approach. Lecture Notes in Computer Science. Springer (2003). https:\/\/doi.org\/10.1007\/b83622","DOI":"10.1007\/b83622"},{"key":"5_CR21","doi-asserted-by":"publisher","unstructured":"Liang, H., Zhang, Z., Hu, C., Gong, Y., Cheng, D.: A survey on spatio-temporal big data analytics ecosystem: resource management, processing platform, and applications. IEEE Trans. Big Data 10(2), 174\u2013193 (2024). https:\/\/doi.org\/10.1109\/TBDATA.2023.3342619","DOI":"10.1109\/TBDATA.2023.3342619"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Lin, X., Ma, S., Jiang, J., Hou, Y., Wo, T.: Error bounded line simplification algorithms for trajectory compression: an experimental evaluation. ACM Trans. Database Syst. 46(3), 11:1\u201311:44 (2021)","DOI":"10.1145\/3474373"},{"key":"5_CR23","doi-asserted-by":"publisher","unstructured":"Loglisci, C.: Using interactions and dynamics for mining groups of moving objects from trajectory data. Int. J. Geogr. Inf. Sci. 32(7), 1436\u20131468 (2018). https:\/\/doi.org\/10.1080\/13658816.2017.1416473","DOI":"10.1080\/13658816.2017.1416473"},{"issue":"1","key":"5_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10707-018-0329-2","volume":"23","author":"AR Mahmood","year":"2019","unstructured":"Mahmood, A.R., Punni, S., Aref, W.G.: Spatio-temporal access methods: a survey (2010\u20132017). GeoInformatica 23(1), 1\u201336 (2019)","journal-title":"GeoInformatica"},{"issue":"15","key":"5_CR25","doi-asserted-by":"publisher","first-page":"4814","DOI":"10.1021\/acs.jcim.3c00738","volume":"63","author":"S Majumdar","year":"2023","unstructured":"Majumdar, S., Palma, F.D., Spyrakis, F., Decherchi, S., Cavalli, A.: Molecular dynamics and machine learning give insights on the flexibility-activity relationships in tyrosine KINOME. J. Chem. Inf. Model. 63(15), 4814\u20134826 (2023)","journal-title":"J. Chem. Inf. Model."},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Metcalf, M., Bauman, N.P., Kowalski, K., de\u00a0Jong, W.A.: Resource-efficient chemistry on quantum computers with the variational quantum eigensolver and the double unitary coupled-cluster approach. J. Chem. Theory Comput. 16(10) (2020)","DOI":"10.1021\/acs.jctc.0c00421"},{"key":"5_CR27","unstructured":"Mokbel, M., et al.: Mobility data science: Perspectives and challenges. ACM Trans. Spatial Algorithms Syst. (2024). just Accepted"},{"key":"5_CR28","doi-asserted-by":"crossref","unstructured":"Morin, L., et al.: Molgrapher: graph-based visual recognition of chemical structures. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01791"},{"key":"5_CR29","doi-asserted-by":"publisher","unstructured":"Nedyalkova, M., Russo, G., Loche, P., Lattuada, M.: Revealing the formation dynamics of Janus polymer particles: Insights from experiments and molecular dynamics. J. Chem. Inf. Model. 63(23), 7453\u20137463 (2023). https:\/\/doi.org\/10.1021\/ACS.JCIM.3C01547","DOI":"10.1021\/ACS.JCIM.3C01547"},{"issue":"2","key":"5_CR30","first-page":"46","volume":"33","author":"L Nguyen-Dinh","year":"2010","unstructured":"Nguyen-Dinh, L., Aref, W.G., Mokbel, M.F.: Spatio-temporal access methods: Part 2 (2003\u20132010). IEEE Data Eng. Bull. 33(2), 46\u201355 (2010)","journal-title":"IEEE Data Eng. Bull."},{"key":"5_CR31","doi-asserted-by":"publisher","unstructured":"Orozco-Acosta, E., Adin, A., Ugarte, M.D.: Big problems in spatio-temporal disease mapping: methods and software. Comput. Methods Programs Biomed. 231, 107403 (2023). https:\/\/doi.org\/10.1016\/J.CMPB.2023.107403","DOI":"10.1016\/J.CMPB.2023.107403"},{"issue":"4","key":"5_CR32","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1007\/s10462-024-10731-4","volume":"57","author":"E Prasnikar","year":"2024","unstructured":"Prasnikar, E., Ljubic, M., Perdih, A., Borisek, J.: Machine learning heralding a new development phase in molecular dynamics simulations. Artif. Intell. Rev. 57(4), 102 (2024)","journal-title":"Artif. Intell. Rev."},{"key":"5_CR33","doi-asserted-by":"publisher","unstructured":"Prasnikar, E., Ljubic, M., Perdih, A., Borisek, J.: Machine learning heralding a new development phase in molecular dynamics simulations. Artif. Intell. Rev. 57(4), 102 (2024). https:\/\/doi.org\/10.1007\/S10462-024-10731-4","DOI":"10.1007\/S10462-024-10731-4"},{"key":"5_CR34","unstructured":"Qingjie, M., Liang, L., Yanfeng, Z., Yunpeng, G., Jun, L.: Progress in sulfur-containing dynamic polymers. J. Funct. Polym. 37(1) (2024)"},{"issue":"1","key":"5_CR35","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/s10707-006-0016-6","volume":"12","author":"A Raffaet\u00e0","year":"2008","unstructured":"Raffaet\u00e0, A., et al.: An application of advanced spatio-temporal formalisms to behavioural ecology. GeoInformatica 12(1), 37\u201372 (2008)","journal-title":"GeoInformatica"},{"issue":"4","key":"5_CR36","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1080\/13658816.2020.1870982","volume":"35","author":"C Renso","year":"2021","unstructured":"Renso, C., Bogorny, V., Tserpes, K., Matwin, S., de Mac\u00eado, J.A.F.: Multiple-aspect analysis of semantic trajectories(master). Int. J. Geogr. Inf. Sci. 35(4), 763\u2013766 (2021)","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"5_CR37","doi-asserted-by":"crossref","unstructured":"Ru, Z., Wu, Y., Shao, J., Yin, J., Qian, L., Miao, X.: A dual-modal graph learning framework for identifying interaction events among chemical and biotech drugs. Briefings Bioinform. 24(5) (2023)","DOI":"10.1093\/bib\/bbad271"},{"key":"5_CR38","unstructured":"Sahili, Z.A., Awad, M.: Spatio-temporal graph neural networks: a survey. CoRR abs\/2301.10569 (2023)"},{"key":"5_CR39","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1017\/S0962492923000016","volume":"32","author":"C Sch\u00fctte","year":"2023","unstructured":"Sch\u00fctte, C., Klus, S., Hartmann, C.: Overcoming the timescale barrier in molecular dynamics: transfer operators, variational principles and machine learning. Acta Numer 32, 517\u2013673 (2023)","journal-title":"Acta Numer"},{"key":"5_CR40","doi-asserted-by":"publisher","unstructured":"Shamail, A., Anowar, M.H., Trajcevski, G., Khokhar, A., Murad, S., Jameson, C.J.: Bond-aware moving clusters of atomic trajectories with relaxed persistency. In: Proceedings of the 32nd ACM SIGSPATIAL, pp. 605\u2013608. ACM (2024). https:\/\/doi.org\/10.1145\/3678717.3691298","DOI":"10.1145\/3678717.3691298"},{"key":"5_CR41","doi-asserted-by":"crossref","unstructured":"Shokry, A., Youssef, M.: Towards quantum computing for location tracking and spatial systems. In: SIGSPATIAL \u201921: 29th International Conference on Advances in Geographic Information Systems (2021)","DOI":"10.1145\/3474717.3483958"},{"issue":"1","key":"5_CR42","doi-asserted-by":"publisher","first-page":"2","DOI":"10.3390\/computers13010002","volume":"13","author":"C Stavrogiannis","year":"2024","unstructured":"Stavrogiannis, C., Sofos, F., Sagri, M., Vavougios, D., Karakasidis, T.E.: Twofold machine-learning and molecular dynamics: a computational framework. Comput. 13(1), 2 (2024)","journal-title":"Comput."},{"key":"5_CR43","unstructured":"Tai, W., Zhong, T., Trajcevski, G., Zhou, F.: Redundancy undermines the trustworthiness of self-interpretable GNNs. In: International Conference on Machine Learning (ICML) (2025). (accepted, to appear)"},{"key":"5_CR44","doi-asserted-by":"crossref","unstructured":"Tan, H., Luo, W., Ni, L.M.: Clost: a hadoop-based storage system for big spatio-temporal data analytics. In: CIKM (2012)","DOI":"10.1145\/2396761.2398589"},{"key":"5_CR45","doi-asserted-by":"crossref","unstructured":"Tang, J., Xia, L., Huang, C.: Explainable spatio-temporal graph neural networks. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM (2023)","DOI":"10.1145\/3583780.3614871"},{"key":"5_CR46","doi-asserted-by":"crossref","unstructured":"Trajcevski, G., Cao, H., Scheuermann, P., Wolfson, O., Vaccaro, D.: On-line data reduction and the quality of history in moving objects databases. In: Fifth ACM International MobiDE Workshop, pp. 19\u201326. ACM (2006)","DOI":"10.1145\/1140104.1140110"},{"key":"5_CR47","doi-asserted-by":"crossref","unstructured":"Wang, X., Jameson, C.J., , Murad, S.: Molecular dynamics simulations of chiral recognition of drugs by amylose polymers coated on amorphous silica. Mol. Phys. 119(1920) (2021)","DOI":"10.1080\/00268976.2021.1922772"},{"key":"5_CR48","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/j.neucom.2017.06.017","volume":"267","author":"Y Yang","year":"2017","unstructured":"Yang, Y., Xu, Y., Han, J., Wang, E., Chen, W., Yue, L.: Efficient traffic congestion estimation using multiple spatio-temporal properties. Neurocomputing 267, 344\u2013353 (2017)","journal-title":"Neurocomputing"},{"key":"5_CR49","unstructured":"Zhang, Y., Paquette, L.: An effect-size-based temporal interestingness metric for sequential pattern mining. In: Proceedings of EDM (2020)"},{"key":"5_CR50","doi-asserted-by":"crossref","unstructured":"Zhao, T., Luo, D., Zhang, X., Wang, S.: Towards faithful and consistent explanations for graph neural networks. In: International Conference on Web Search and Data Mining, pp. 634\u2013642 (2023)","DOI":"10.1145\/3539597.3570421"},{"key":"5_CR51","doi-asserted-by":"publisher","unstructured":"Zheng, Y., Capra, L., Wolfson, O., Yang, H.: Urban computing: concepts, methodologies, and applications. ACM Trans. Intell. Syst. Technol. 5(3), 38:1\u201338:55 (2014). https:\/\/doi.org\/10.1145\/2629592","DOI":"10.1145\/2629592"},{"issue":"3","key":"5_CR52","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1007\/s00779-020-01456-6","volume":"27","author":"B Zhou","year":"2023","unstructured":"Zhou, B., Chen, L., Zhao, S., Zhou, F., Li, S., Pan, G.: Spatio-temporal analysis of urban crime leveraging multisource crowdsensed data. Pers. Ubiquitous Comput. 27(3), 599\u2013612 (2023)","journal-title":"Pers. Ubiquitous Comput."},{"issue":"8","key":"5_CR53","doi-asserted-by":"publisher","first-page":"8128","DOI":"10.1109\/TCYB.2021.3049533","volume":"52","author":"F Zhou","year":"2022","unstructured":"Zhou, F., Liu, X., Zhong, T., Trajcevski, G.: Metamove: on improving human mobility classification and prediction via metalearning. IEEE Trans. Cybern. 52(8), 8128\u20138141 (2022)","journal-title":"IEEE Trans. Cybern."}],"container-title":["Communications in Computer and Information Science","New Trends in Database and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05727-3_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T07:44:26Z","timestamp":1758440666000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05727-3_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,22]]},"ISBN":["9783032057266","9783032057273"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05727-3_5","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,22]]},"assertion":[{"value":"22 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADBIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Advances in Databases and Information Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tampere","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Finland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adbis2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adbis2025.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}