{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:41:51Z","timestamp":1740123711857,"version":"3.37.3"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,1,5]],"date-time":"2019-01-05T00:00:00Z","timestamp":1546646400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,1,5]],"date-time":"2019-01-05T00:00:00Z","timestamp":1546646400000},"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":["Mobile Netw Appl"],"published-print":{"date-parts":[[2019,2,15]]},"DOI":"10.1007\/s11036-018-1204-y","type":"journal-article","created":{"date-parts":[[2019,1,5]],"date-time":"2019-01-05T10:27:34Z","timestamp":1546684054000},"page":"282-294","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Effective Big Data Retrieval Using Deep Learning Modified Neural Networks"],"prefix":"10.1007","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7043-8855","authenticated-orcid":false,"given":"T.","family":"Prasanth","sequence":"first","affiliation":[]},{"given":"M.","family":"Gunasekaran","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,1,5]]},"reference":[{"key":"1204_CR1","doi-asserted-by":"crossref","unstructured":"Irfan S, Babu BV (2016) Information retrieval in big data using evolutionary computation: A survey. In: Computing, Communication and Automation (ICCCA), International Conference on, pp. 208-213, IEEE","DOI":"10.1109\/CCAA.2016.7813720"},{"key":"1204_CR2","doi-asserted-by":"crossref","unstructured":"Zhao F, Zhu Y, Jin H, Yang LT (2016) A personalized hashtag recommendation approach using LDA-based topic model in microblog environment. Futur Gener Comput Syst 65:196\u2013206. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167739X15003258","DOI":"10.1016\/j.future.2015.10.012"},{"key":"1204_CR3","unstructured":"DineshMavaluru RS, Sugumaran V (2014) Big data analytics in information retrieval: promise and potential. In: Proceedings of 08th IRF International Conference. Bengaluru, pp. 41-46"},{"key":"1204_CR4","doi-asserted-by":"publisher","first-page":"22","DOI":"10.9756\/BIJSESC.8236","volume":"6","author":"MM Kodabagi","year":"2016","unstructured":"Kodabagi MM, Sarashetti D, Naik V (2016) A Text Information Retrieval Technique for Big Data Using Map Reduce. Bonfring International Journal of Software Engineering and Soft Computing 6:22\u201326","journal-title":"Bonfring International Journal of Software Engineering and Soft Computing"},{"key":"1204_CR5","doi-asserted-by":"crossref","unstructured":"Cuzzocrea A, Lee W, Leung CK (2015) High-recall information retrieval from linked big data. In: Computer Software and Applications Conference (COMPSAC), IEEE 39th Annual, Vol. 2, pp. 712-717, IEEE","DOI":"10.1109\/COMPSAC.2015.152"},{"key":"1204_CR6","doi-asserted-by":"crossref","unstructured":"Chiranjeevi HS, Shenoy M, Prabhu S, Sundhar S (2016) DSSM with text hashing technique for text document retrieval in next-generation search engine for big data and data analytics. In: Engineering and Technology (ICETECH), IEEE International Conference on, pp. 395-399, IEEE","DOI":"10.1109\/ICETECH.2016.7569283"},{"key":"1204_CR7","doi-asserted-by":"crossref","unstructured":"Portilla Herrera NA, L\u00f3pez Gomez F, Bucheli VA, SolartePab\u00f3n O (2017) Semantic annotation and retrieval of scientific documents in a big data environment. IET digital library 7th Latin American Conference on Networked and Electronic Media, pp. 33-38","DOI":"10.1049\/ic.2017.0032"},{"key":"1204_CR8","doi-asserted-by":"crossref","unstructured":"Ketu S, Agarwal S (2015) Performance enhancement of distributed K-Means clustering for big Data analytics through in-memory computation. In: Contemporary Computing (IC3), Eighth International Conference on, pp. 318-324, IEEE","DOI":"10.1109\/IC3.2015.7346700"},{"key":"1204_CR9","doi-asserted-by":"crossref","unstructured":"Chen C, Zhu X, Shen P, Hu J (2014) A hierarchical clustering method for big data oriented ciphertext search. In: Computer Communications Workshops (INFOCOM WKSHPS), IEEE Conference on, pp. 559-564, IEEE","DOI":"10.1109\/INFCOMW.2014.6849292"},{"issue":"7","key":"1204_CR10","doi-asserted-by":"publisher","first-page":"1919","DOI":"10.1109\/TKDE.2016.2531661","volume":"28","author":"Y Wang","year":"2016","unstructured":"Wang Y, Liu J, Huang Y, Feng X (2016) Using hashtag graph-based topic model to connect semantically-related words without co-occurrence in microblogs. IEEE Trans Knowl Data Eng 28(7):1919\u20131933","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1204_CR11","doi-asserted-by":"crossref","unstructured":"Caballero I, Serrano M, Piattini M (2014) A data quality in use model for big data. In: International Conference on Conceptual Modeling, pp. 65-74. Springer, Cham","DOI":"10.1007\/978-3-319-12256-4_7"},{"key":"1204_CR12","doi-asserted-by":"publisher","first-page":"522","DOI":"10.1016\/j.compeleceng.2016.04.015","volume":"54","author":"NA Sakr","year":"2016","unstructured":"Sakr NA, ELdesouky AI, Arafat H (2016) An efficient fast-response content-based image retrieval framework for big data. Comput Electr Eng 54:522\u2013538","journal-title":"Comput Electr Eng"},{"issue":"2","key":"1204_CR13","doi-asserted-by":"publisher","first-page":"950","DOI":"10.1109\/TGRS.2017.2756911","volume":"56","author":"Y Li","year":"2018","unstructured":"Li Y, Zhang Y, Huang X, Zhu H, Ma J (2018) Large-scale remote sensing image retrieval by deep hashing neural networks. IEEE Trans Geosci Remote Sens 56(2):950\u2013965","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"3","key":"1204_CR14","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1109\/TIP.2017.2651390","volume":"26","author":"L Liu","year":"2017","unstructured":"Liu L, Yu M, Shao L (2017) Learning short binary codes for large-scale image retrieval. IEEE Trans Image Process 26(3):1289\u20131299","journal-title":"IEEE Trans Image Process"},{"key":"1204_CR15","doi-asserted-by":"crossref","unstructured":"Prasanth T, Gunasekaran M (2017) A mutual refinement technique for big data retrieval using hash tag graph. Cluster Computing, pp. 1-11","DOI":"10.1007\/s10586-017-1320-7"},{"issue":"2","key":"1204_CR16","doi-asserted-by":"publisher","first-page":"892","DOI":"10.1109\/TGRS.2015.2469138","volume":"54","author":"Beg\u00fcmDemir, and Lorenzo Bruzzone","year":"2016","unstructured":"Beg\u00fcmDemir, and Lorenzo Bruzzone (2016) Hashing-based scalable remote sensing image search and retrieval in large archives. IEEE Trans Geosci Remote Sens 54(2):892\u2013904","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1204_CR17","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1016\/j.jocs.2017.02.005","volume":"28","author":"G Kehua","year":"2018","unstructured":"Kehua G, Liang Z, Tang Y, Chi T (2018) SOR: An optimized semantic ontology retrieval algorithm for heterogeneous multimedia big data. J Comput Sci 28:455\u2013465","journal-title":"J Comput Sci"},{"issue":"3","key":"1204_CR18","doi-asserted-by":"publisher","first-page":"3677","DOI":"10.1007\/s11042-017-5219-3","volume":"77","author":"F Zou","year":"2018","unstructured":"Zou F, Tang X, Li K, Wang Y, Song J, Yang S, Ling H (2018) Hidden semantic hashing for fast retrieval over large scale document collection. Multimedia Tools and Applications 77(3):3677\u20133697","journal-title":"Multimedia Tools and Applications"},{"issue":"8","key":"1204_CR19","first-page":"7262","volume":"4","author":"AS Joshi","year":"2017","unstructured":"Joshi AS, Kulkarni O, Kakandikar GM, Nandedkar VM (2017) Cuckoo Search Optimization-A Review. Materials Today: Proceedings 4(8):7262\u20137269","journal-title":"Materials Today: Proceedings"}],"container-title":["Mobile Networks and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11036-018-1204-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11036-018-1204-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11036-018-1204-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,13]],"date-time":"2021-07-13T20:19:10Z","timestamp":1626207550000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11036-018-1204-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,5]]},"references-count":19,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,2,15]]}},"alternative-id":["1204"],"URL":"https:\/\/doi.org\/10.1007\/s11036-018-1204-y","relation":{},"ISSN":["1383-469X","1572-8153"],"issn-type":[{"type":"print","value":"1383-469X"},{"type":"electronic","value":"1572-8153"}],"subject":[],"published":{"date-parts":[[2019,1,5]]},"assertion":[{"value":"5 January 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}