{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T16:38:35Z","timestamp":1758818315753,"version":"3.40.3"},"publisher-location":"Berlin, Heidelberg","reference-count":33,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783662661451"},{"type":"electronic","value":"9783662661468"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-662-66146-8_6","type":"book-chapter","created":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T19:12:31Z","timestamp":1664305951000},"page":"127-147","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Pattern Mining Framework for\u00a0Improving Billboard Advertising Revenue"],"prefix":"10.1007","author":[{"given":"P. Revanth","family":"Rathan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P. Krishna","family":"Reddy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anirban","family":"Mondal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,28]]},"reference":[{"key":"6_CR1","unstructured":"Billboard Advertisement Eyeball views. http:\/\/www.runningboards.com.au\/outdoor\/relocatable-billboards. Accessed 1 July 2022"},{"key":"6_CR2","unstructured":"Billboard Advertisement Marketing Conversion Scheme. https:\/\/www.electro-mech.com\/team-sports\/advertising\/billboard-advertising-cost-per-thousand-viewers\/. Accessed 1 July 2022"},{"key":"6_CR3","unstructured":"Billboard Advertisement Marketing Conversion Scheme. https:\/\/www.adquick.com\/billboard-cost Accessed 1 July 2022"},{"key":"6_CR4","unstructured":"U.S. Advertising Industry - Statistics & Facts. https:\/\/www.statista.com\/topics\/979\/advertising-in-the-us\/. Accessed 1 July 2022"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C., Yu, P.S.: A new framework for itemset generation. In: Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pp. 18\u201324. ACM (1998)","DOI":"10.1145\/275487.275490"},{"issue":"9","key":"6_CR6","first-page":"1498","volume":"13","author":"S Bian","year":"2020","unstructured":"Bian, S., Guo, Q., Wang, S., Yu, J.X.: Efficient algorithms for budgeted influence maximization on massive social networks. Proce. Very Large DataBases Endowment 13(9), 1498\u20131510 (2020)","journal-title":"Proce. Very Large DataBases Endowment"},{"issue":"2","key":"6_CR7","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/s41060-018-0165-3","volume":"8","author":"A Budhiraja","year":"2018","unstructured":"Budhiraja, A., Ralla, A., Reddy, P.K.: Coverage pattern based framework to improve search engine advertising. Int. J. Data Sci. Analytics 8(2), 199\u2013211 (2018). https:\/\/doi.org\/10.1007\/s41060-018-0165-3","journal-title":"Int. J. Data Sci. Analytics"},{"key":"6_CR8","doi-asserted-by":"crossref","unstructured":"Domingos, P., Richardson, M.: Mining the network value of customers. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 57\u201366 (2001)","DOI":"10.1145\/502512.502525"},{"issue":"5","key":"6_CR9","doi-asserted-by":"publisher","first-page":"1393","DOI":"10.1007\/s10618-019-00641-3","volume":"33","author":"L Gangumalla","year":"2019","unstructured":"Gangumalla, L., Reddy, P.K., Mondal, A.: Multi-location visibility query processing using portion-based transactional modeling and pattern mining. Data Min. Knowl. Disc. 33(5), 1393\u20131416 (2019). https:\/\/doi.org\/10.1007\/s10618-019-00641-3","journal-title":"Data Min. Knowl. Disc."},{"issue":"3","key":"6_CR10","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1007\/s10844-014-0318-3","volume":"45","author":"P Gowtham Srinivas","year":"2014","unstructured":"Gowtham Srinivas, P., Krishna Reddy, P., Trinath, A.V., Bhargav, S., Uday Kiran, R.: Mining coverage patterns from transactional databases. J. Intell. Inf. Syst. 45(3), 423\u2013439 (2014). https:\/\/doi.org\/10.1007\/s10844-014-0318-3","journal-title":"J. Intell. Inf. Syst."},{"key":"6_CR11","unstructured":"Gray, J., Reuter, A.: Transaction Processing: Concepts and Techniques. Elsevier (1992)"},{"issue":"3","key":"6_CR12","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1109\/TKDE.2016.2621038","volume":"29","author":"L Guo","year":"2016","unstructured":"Guo, L., Zhang, D., Cong, G., Wu, W., Tan, K.L.: Influence maximization in trajectory databases. IEEE Trans. Knowl. Data Eng. 29(3), 627\u2013641 (2016)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"6_CR13","doi-asserted-by":"publisher","first-page":"30878","DOI":"10.1109\/ACCESS.2019.2903277","volume":"7","author":"M Huang","year":"2019","unstructured":"Huang, M., Fang, Z., Xiong, S., Zhang, T.: Interest-driven outdoor advertising display location selection using mobile phone data. IEEE Access 7, 30878\u201330889 (2019)","journal-title":"IEEE Access"},{"issue":"6","key":"6_CR14","doi-asserted-by":"publisher","first-page":"1179","DOI":"10.1080\/13658816.2020.1823396","volume":"35","author":"M Huang","year":"2021","unstructured":"Huang, M., Fang, Z., Weibel, R., Zhang, T., Huang, H.: Dynamic optimization models for displaying outdoor advertisement at the right time and place. Int. J. Geogr. Inf. Sci. 35(6), 1179\u20131204 (2021)","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Kempe, D., Kleinberg, J., Tardos, \u00c9.: Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 137\u2013146 (2003)","DOI":"10.1145\/956750.956769"},{"key":"6_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-16-3964-7","volume-title":"Periodic Pattern Mining","year":"2021","unstructured":"Kiran, R.U., Fournier-Viger, P., Luna, J.M., Lin, J.C.-W., Mondal, A. (eds.): Periodic Pattern Mining. Springer, Singapore (2021). https:\/\/doi.org\/10.1007\/978-981-16-3964-7"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Kiran, R.U., Pallikila, P., Luna, J., Fournier-Viger, P., Toyoda, M., Reddy, P.K.: Discovering relative high utility itemsets in very large transactional databases using null-invariant measure. In: IEEE International Conference on Big Data, pp. 252\u2013262. IEEE (2021)","DOI":"10.1109\/BigData52589.2021.9672064"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Li, G., Chen, S., Feng, J., Tan, K.L., Li, W.S.: Efficient location-aware influence maximization. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 87\u201398 (2014)","DOI":"10.1145\/2588555.2588561"},{"issue":"10","key":"6_CR19","doi-asserted-by":"publisher","first-page":"1852","DOI":"10.1109\/TKDE.2018.2807843","volume":"30","author":"Y Li","year":"2018","unstructured":"Li, Y., Fan, J., Wang, Y., Tan, K.L.: Influence maximization on social graphs: a survey. IEEE Trans. Knowl. Data Eng. 30(10), 1852\u20131872 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"4","key":"6_CR20","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1109\/TBDATA.2017.2717978","volume":"4","author":"Y Li","year":"2017","unstructured":"Li, Y., Bao, J., Li, Y., Wu, Y., Gong, Z., Zheng, Y.: Mining the most influential $$ k $$-location set from massive trajectories. IEEE Trans. Big Data 4(4), 556\u2013570 (2017)","journal-title":"IEEE Trans. Big Data"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Liu, B., Hsu, W., Ma, Y.: Mining association rules with multiple minimum supports. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 337\u2013341 (1999)","DOI":"10.1145\/312129.312274"},{"issue":"1","key":"6_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TVCG.2016.2598432","volume":"23","author":"D Liu","year":"2016","unstructured":"Liu, D., et al.: SmartAdP: visual analytics of large-scale taxi trajectories for selecting billboard locations. IEEE Trans. Visual Comput. Graph. 23(1), 1\u201310 (2016)","journal-title":"IEEE Trans. Visual Comput. Graph."},{"issue":"7","key":"6_CR23","first-page":"1","volume":"22","author":"K Lou","year":"2020","unstructured":"Lou, K., Yang, Y., Wang, E., Liu, Z., Baker, T., Bashir, A.K.: Reinforcement learning based advertising strategy using crowdsensing vehicular data. IEEE Trans. Intell. Transp. Syst. 22(7), 1\u201313 (2020)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"6_CR24","doi-asserted-by":"crossref","unstructured":"Ralla, A., Siddiqie, S., Reddy, P.K., Mondal, A.: Coverage pattern mining based on MapReduce. In: Proceedings of the ACM International Conference on Data Science and Management of Data, pp. 209\u2013213 (2020)","DOI":"10.1145\/3371158.3371188"},{"key":"6_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1007\/978-3-030-86472-9_10","volume-title":"Database and Expert Systems Applications","author":"PR Rathan","year":"2021","unstructured":"Rathan, P.R., Reddy, P.K., Mondal, A.: Improving billboard advertising revenue using transactional modeling and pattern mining. In: Strauss, C., Kotsis, G., Tjoa, A.M., Khalil, I. (eds.) DEXA 2021. LNCS, vol. 12923, pp. 112\u2013118. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86472-9_10"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 61\u201370 (2002)","DOI":"10.1145\/775047.775057"},{"key":"6_CR27","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/978-3-642-30220-6_12","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"PG Srinivas","year":"2012","unstructured":"Srinivas, P.G., Reddy, P.K., Bhargav, S., Kiran, R.U., Kumar, D.S.: Discovering coverage patterns for banner advertisement placement. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012. LNCS (LNAI), vol. 7302, pp. 133\u2013144. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-30220-6_12"},{"issue":"2","key":"6_CR28","doi-asserted-by":"publisher","first-page":"1058","DOI":"10.1109\/TII.2019.2891258","volume":"16","author":"L Wang","year":"2020","unstructured":"Wang, L., Yu, Z., Yang, D., Ma, H., Sheng, H.: Efficiently targeted billboard advertising using crowdsensing vehicle trajectory data. IEEE Trans. Industr. Inf. 16(2), 1058\u20131066 (2020)","journal-title":"IEEE Trans. Industr. Inf."},{"issue":"5","key":"6_CR29","doi-asserted-by":"publisher","first-page":"5787","DOI":"10.3233\/JIFS-179666","volume":"38","author":"TY Wu","year":"2020","unstructured":"Wu, T.Y., Lin, J.C.W., Yun, U., Chen, C.H., Srivastava, G., Lv, X.: An efficient algorithm for fuzzy frequent itemset mining. J. Intell. Fuzzy Syst. 38(5), 5787\u20135797 (2020)","journal-title":"J. Intell. Fuzzy Syst."},{"issue":"3","key":"6_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3480245","volume":"16","author":"Y Wu","year":"2021","unstructured":"Wu, Y., Luo, L., Li, Y., Guo, L., Fournier-Viger, P., Zhu, X., Wu, X.: Ntp-miner: nonoverlapping three-way sequential pattern mining. ACM Trans. Knowl. Discov. Data 16(3), 1\u201321 (2021)","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"6_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, P., Bao, Z., Li, Y., Li, G., Zhang, Y., Peng, Z.: Trajectory-driven influential billboard placement. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2748\u20132757 (2018)","DOI":"10.1145\/3219819.3219946"},{"key":"6_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, Y., Bao, Z., Mo, S., Zhang, P.: Optimizing impression counts for outdoor advertising. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1205\u20131215 (2019)","DOI":"10.1145\/3292500.3330829"},{"issue":"2","key":"6_CR33","first-page":"32","volume":"33","author":"Y Zheng","year":"2010","unstructured":"Zheng, Y., Xie, X., Ma, W.Y.: GeoLife: a collaborative social networking service among user, location and trajectory. IEEE Database Eng. Bull. 33(2), 32\u201339 (2010)","journal-title":"IEEE Database Eng. Bull."}],"container-title":["Lecture Notes in Computer Science","Transactions on Large-Scale Data- and Knowledge-Centered Systems LII"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-662-66146-8_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T19:13:38Z","timestamp":1664306018000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-662-66146-8_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783662661451","9783662661468"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-662-66146-8_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"28 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}