{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T12:12:50Z","timestamp":1753272770227,"version":"3.37.3"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T00:00:00Z","timestamp":1501200000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Data Sci Anal"],"published-print":{"date-parts":[[2017,12]]},"DOI":"10.1007\/s41060-017-0065-y","type":"journal-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T07:24:28Z","timestamp":1501226668000},"page":"285-299","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Scalable and flexible clustering solutions for mobile phone-based population indicators"],"prefix":"10.1007","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3881-8900","authenticated-orcid":false,"given":"Alessandro","family":"Lulli","sequence":"first","affiliation":[]},{"given":"Lorenzo","family":"Gabrielli","sequence":"additional","affiliation":[]},{"given":"Patrizio","family":"Dazzi","sequence":"additional","affiliation":[]},{"given":"Matteo","family":"Dell\u2019Amico","sequence":"additional","affiliation":[]},{"given":"Pietro","family":"Michiardi","sequence":"additional","affiliation":[]},{"given":"Mirco","family":"Nanni","sequence":"additional","affiliation":[]},{"given":"Laura","family":"Ricci","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,7,28]]},"reference":[{"key":"65_CR1","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1016\/j.neucom.2015.09.070","volume":"174","author":"K Xu","year":"2016","unstructured":"Xu, K., Zou, K., Huang, Y., Yu, X., Zhang, X.: Mining community and inferring friendship in mobile social networks. Neurocomputing 174, 605\u2013616 (2016)","journal-title":"Neurocomputing"},{"issue":"5","key":"65_CR2","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1068\/b32047","volume":"33","author":"C Ratti","year":"2006","unstructured":"Ratti, C., Frenchman, D., Pulselli, R.M., Williams, S.: Mobile landscapes: using location data from cell phones for urban analysis. Environ. Plan. B Plan. Des. 33(5), 727\u2013748 (2006)","journal-title":"Environ. Plan. B Plan. Des."},{"issue":"1","key":"65_CR3","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1109\/TITS.2010.2074196","volume":"12","author":"F Calabrese","year":"2011","unstructured":"Calabrese, F., et al.: Real-time urban monitoring using cell phones: a case study in rome. IEEE Trans. Intell. Transp. Syst. 12(1), 141\u2013151 (2011)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"65_CR4","doi-asserted-by":"crossref","unstructured":"Gabrielli, L., et\u00a0al.: City users\u2019 classification with mobile phone data. In: 2015 IEEE International Conference on Big Data, pp. 1007\u20131012 (2015)","DOI":"10.1109\/BigData.2015.7363852"},{"key":"65_CR5","doi-asserted-by":"crossref","unstructured":"Lulli, A., et\u00a0al.: Improving population estimation from mobile calls: a clustering approach. In: 2016 IEEE Symposium on Computers and Communication (ISCC). IEEE (2016)","DOI":"10.1109\/ISCC.2016.7543882"},{"key":"65_CR6","doi-asserted-by":"crossref","unstructured":"Deville, P., et al.: Dynamic population mapping using mobile phone data. Proc. Natl. Acad. Sci. 111(45), 15888\u201315893 (2014)","DOI":"10.1073\/pnas.1408439111"},{"key":"65_CR7","volume-title":"Mobile Landscapes: Graz in Real Time","author":"C Ratti","year":"2007","unstructured":"Ratti, C., et al.: Mobile Landscapes: Graz in Real Time. Springer, New York (2007)"},{"issue":"1","key":"65_CR8","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/10630731003597306","volume":"17","author":"R Ahas","year":"2010","unstructured":"Ahas, R., et al.: Using mobile positioning data to model locations meaningful to users of mobile phones. J. Urban Technol. 17(1), 3\u201327 (2010)","journal-title":"J. Urban Technol."},{"issue":"6","key":"65_CR9","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1016\/j.pmcj.2013.07.006","volume":"9","author":"V Etter","year":"2013","unstructured":"Etter, V., et al.: Where to go from here? Mobility prediction from instantaneous information. Pervasive Mob. Comput. 9(6), 784\u2013797 (2013)","journal-title":"Pervasive Mob. Comput."},{"key":"65_CR10","unstructured":"De Jonge, E., van Pelt, M., Roos, M.: Time patterns, geospatial clustering and mobility statistics based on mobile phone network data. In: Paper for the Federal Committee on Statistical Methodology research conference, Washington, USA (2012)"},{"key":"65_CR11","first-page":"10","volume":"14","author":"M Terada","year":"2013","unstructured":"Terada, M., Nagata, T., Kobayashi, M.: Population estimation technology for mobile spatial statistics. NTT DOCOMO Techn. J. 14, 10\u201315 (2013)","journal-title":"NTT DOCOMO Techn. J."},{"key":"65_CR12","unstructured":"Furletti, B., et\u00a0al.: Use of mobile phone data to estimate mobility flows. measuring urban population and inter-city mobility using big data in an integrated approach. In: Proceedings of the 47th Meeting of the Italian Statistical Society (2014)"},{"key":"65_CR13","unstructured":"Ester, M., et\u00a0al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd, pp. 226\u2013231 (1996)"},{"key":"65_CR14","doi-asserted-by":"crossref","unstructured":"He, Y., et\u00a0al .: Mr-dbscan: an efficient parallel density-based clustering algorithm using mapreduce. In: 2011 IEEE International Conference on Parallel and Distributed Systems, pp. 473\u2013480. IEEE (2011)","DOI":"10.1109\/ICPADS.2011.83"},{"key":"65_CR15","doi-asserted-by":"crossref","unstructured":"Lulli, A., et\u00a0al.: Scalable k-nn based text clustering. In: 2015 IEEE International Conference on Big Data, pp. 958\u2013963. IEEE (2015)","DOI":"10.1109\/BigData.2015.7363845"},{"key":"65_CR16","doi-asserted-by":"crossref","unstructured":"Lulli, A., Ricci, L., Carlini, E., Dazzi, P., Lucchese, C.: Cracker: crumbling large graphs into connected components. In: 2015 IEEE Symposium on Computers and Communication (ISCC), pp. 574\u2013581. IEEE (2015)","DOI":"10.1109\/ISCC.2015.7405576"},{"issue":"3","key":"65_CR17","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1109\/TPDS.2016.2591038","volume":"28","author":"A Lulli","year":"2017","unstructured":"Lulli, A., Carlini, E., Dazzi, P., Lucchese, C., Ricci, L.: Fast connected components computation in large graphs by vertex pruning. IEEE Trans. Parallel Distrib. Syst. 28(3), 760\u2013773 (2017)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"65_CR18","first-page":"10","volume":"10","author":"M Zaharia","year":"2010","unstructured":"Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. HotCloud 10, 10\u201310 (2010)","journal-title":"HotCloud"},{"issue":"3","key":"65_CR19","doi-asserted-by":"crossref","first-page":"157","DOI":"10.14778\/3021924.3021932","volume":"10","author":"A Lulli","year":"2016","unstructured":"Lulli, A., Dell\u2019Amico, M., Michiardi, P., Ricci, L.: Ng-dbscan: scalable density-based clustering for arbitrary data. Proc. VLDB Endow. 10(3), 157\u2013168 (2016)","journal-title":"Proc. VLDB Endow."},{"key":"65_CR20","first-page":"34","volume":"1","author":"B Desgraupes","year":"2013","unstructured":"Desgraupes, B.: Clustering indices. Univ. Paris Ouest-Lab ModalX 1, 34 (2013)","journal-title":"Univ. Paris Ouest-Lab ModalX"},{"key":"65_CR21","doi-asserted-by":"crossref","unstructured":"Liu, Y., Li, Z., Xiong, H., Gao, X., Wu, J.: Understanding of internal clustering validation measures. In: 2010 IEEE 10th International Conference on Data Mining (ICDM), pp. 911\u2013916. IEEE (2010)","DOI":"10.1109\/ICDM.2010.35"},{"key":"65_CR22","volume-title":"Computer Architecture: A Quantitative Approach","author":"DA Patterson","year":"2011","unstructured":"Patterson, D.A.: Computer Architecture: A Quantitative Approach. Elsevier, Amsterdam (2011)"}],"container-title":["International Journal of Data Science and Analytics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s41060-017-0065-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-017-0065-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s41060-017-0065-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2017,10,24]],"date-time":"2017-10-24T06:30:16Z","timestamp":1508826616000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s41060-017-0065-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,7,28]]},"references-count":22,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2017,12]]}},"alternative-id":["65"],"URL":"https:\/\/doi.org\/10.1007\/s41060-017-0065-y","relation":{},"ISSN":["2364-415X","2364-4168"],"issn-type":[{"type":"print","value":"2364-415X"},{"type":"electronic","value":"2364-4168"}],"subject":[],"published":{"date-parts":[[2017,7,28]]}}}