{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:39:10Z","timestamp":1776811150610,"version":"3.51.2"},"reference-count":36,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Computational Methods in Sciences and Engineering"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:p>With the rapid development and widespread adoption of wearable technology, a new type of lifelog data is being collected and used in numerous studies. We refer to these data as informative lifelog which usually contain GPS, images, videos, text, etc. GPS trajectory data in lifelogs is typically categorized into continuous and discrete trajectories. Finding a point of interest (POI) from discrete trajectories is a challenging task to do and has caught little attention so far. This paper suggests an LP-DBSCAN model for mining personal trajectories from discrete GPS trajectory data. It makes use of the hierarchical structure information implied in GPS trajectory data and it is suggested a variable-levels, variable-parameters clustering method (LP-DBSCAN) based on the DBSCAN algorithm to increase the precision of finding POI information. Finally, the Liu lifelog dataset is subjected to a systematic evaluation. In terms of GPS data that are not evenly distributed geographically, the experimental results demonstrated that the proposed algorithm could more accurately identify POI information and address the adverse effects caused by the global parameters of the traditional DBSCAN algorithm.<\/jats:p>","DOI":"10.3233\/jcm-237061","type":"journal-article","created":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T10:19:03Z","timestamp":1709893143000},"page":"357-368","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Research and application of the global positioning system (GPS) clustering algorithm based on multilevel functions"],"prefix":"10.66113","volume":"24","author":[{"given":"Guoqi","family":"Liu","sequence":"first","affiliation":[{"name":"Shenyang Jianzhu University","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingxi","family":"Zheng","sequence":"additional","affiliation":[{"name":"Shenyang Jianzhu University","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siqi","family":"Niu","sequence":"additional","affiliation":[{"name":"Shenyang Jianzhu University","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Ma","sequence":"additional","affiliation":[{"name":"Shenyang Jianzhu University","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","published-online":{"date-parts":[[2024,3]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","unstructured":"YenAZ FuMH AngWH ChuTT TsaiSH HuangHH ChenHH. Visual lifelog retrieval: humans and machines interpretation on first-person images. Multimed Tools Appl. 2023. doi: 10.1007\/s11042-023-14344-x.","DOI":"10.1007\/s11042-023-14344-x"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102529"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.2196\/30517"},{"issue":"6","key":"e_1_3_1_5_2","first-page":"5","article-title":"Big data analysis in psychology and social sciences: perspective directions of research","volume":"40","author":"Nestik TA","year":"2019","unstructured":"NestikTA ZhuravlevAL. Big data analysis in psychology and social sciences: perspective directions of research. Psikhol Zh. 2019; 40(6): 5-17.","journal-title":"Psikhol Zh."},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.3390\/su122410324"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1108\/OIR-04-2018-0119"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","unstructured":"AlamN GrahamY. Memento: a prototype search engine for LSC 2021. Multimed Tools Appl. 2023. doi: 10.1007\/s11042-023-15067-9.","DOI":"10.1007\/s11042-023-15067-9"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1108\/OIR-03-2018-0108"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2018-025939"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3073469"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-021-10755-w"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.3349\/ymj.2022.63.S84"},{"issue":"3","key":"e_1_3_1_14_2","first-page":"92","article-title":"Lifelog image retrieval based on semantic relevance mapping","volume":"17","author":"Xu QL","year":"2021","unstructured":"XuQL Del MolinoAG LinJ FangF SubbarajuV LiLY LimJH. Lifelog image retrieval based on semantic relevance mapping. ACM T Multim Comput. 2021; 17(3): 92.","journal-title":"ACM T Multim Comput."},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.buildenv.2021.107835"},{"issue":"2","key":"e_1_3_1_16_2","first-page":"1963","article-title":"Image-Based Lifelogging: User Emotion Perspective","volume":"67","author":"Bum J","year":"2021","unstructured":"BumJ ChooH WhangJJ. Image-Based Lifelogging: User Emotion Perspective. CMC-Comput Mater Con. 2021; 67(2): 1963-1977.","journal-title":"CMC-Comput Mater Con."},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20071852"},{"issue":"2","key":"e_1_3_1_18_2","first-page":"3","article-title":"Analysis of enterprise social media intelligence acquisition based on data crawler technology","volume":"11","author":"Yu L","year":"2021","unstructured":"YuL GuiZ. Analysis of enterprise social media intelligence acquisition based on data crawler technology. Entrep Res J. 2021; 11(2): 3-23.","journal-title":"Entrep Res J."},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2896934"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106710"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.3390\/su10093187"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi8080345"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00779-018-1175-9"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtrangeo.2020.102787"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi8070308"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi10030161"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi10100669"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1080\/10630732.2017.1400814"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi8050218"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cities.2021.103158"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.3390\/su13095003"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3077583"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.116734"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-10453-z"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.3390\/su12041543"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.3390\/land10050523"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi10110775"}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JCM-237061","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JCM-237061","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JCM-237061","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:05:42Z","timestamp":1776809142000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JCM-237061"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":36,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["10.3233\/JCM-237061"],"URL":"https:\/\/doi.org\/10.3233\/jcm-237061","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1]]}}}