{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T16:15:22Z","timestamp":1747152922798,"version":"3.40.5"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030474102"},{"type":"electronic","value":"9783030474119"}],"license":[{"start":{"date-parts":[[2020,6,27]],"date-time":"2020-06-27T00:00:00Z","timestamp":1593216000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,6,27]],"date-time":"2020-06-27T00:00:00Z","timestamp":1593216000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-47411-9_23","type":"book-chapter","created":{"date-parts":[[2020,6,26]],"date-time":"2020-06-26T18:58:46Z","timestamp":1593197926000},"page":"429-449","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DistSNNMF: Solving Large-Scale Semantic Topic Model Problems on HPC for Streaming Texts"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3079-4169","authenticated-orcid":false,"given":"Fatma S.","family":"Gadelrab","sequence":"first","affiliation":[]},{"given":"Rowayda A.","family":"Sadek","sequence":"additional","affiliation":[]},{"given":"Mohamed H.","family":"Haggag","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,6,27]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"Li, K.C., Jiang, H., Yang, L.T., Cuzzocrea, A. (eds.): Big Data: Algorithms, Analytics, and, Applications. CRC Press (2015)","DOI":"10.1201\/b18050"},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Al-Drees, A., Bin-Hezam, R., Al-Muwayshir, R.: Unified retrieval model of big data. In: INNS Conference on Big Data, pp. 323\u2013332. Springer International Publishing (2016)","DOI":"10.1007\/978-3-319-47898-2_33"},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Desarkar, A., Das, A.: Big-data analytics, machine learning algorithms and, scalable\/parallel\/distributed algorithms. In: Internet of Things and, Big Data Technologies for Next Generation Healthcare, pp. 159\u2013197. Springer International Publishing (2017)\u200f","DOI":"10.1007\/978-3-319-49736-5_8"},{"key":"23_CR4","doi-asserted-by":"crossref","unstructured":"Plato\u0161, J., Gajdo\u0161, P., Kr\u00f6mer, P., Sn\u00e1\u0161el, V.: Non-negative matrix factorization on GPU. In: Networked Digital Technologies, pp. 21\u201330 (2010)","DOI":"10.1007\/978-3-642-14292-5_4"},{"key":"23_CR5","unstructured":"\u0158eh\u016f\u0159ek, R.: Scalability of Semantic Analysis in Natural Language Processing (Doctoral dissertation, Masarykova univerzita, Fakulta informatiky) (2011)"},{"key":"23_CR6","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.ins.2016.12.014","volume":"390","author":"J Yan","year":"2017","unstructured":"Yan, J., Zeng, J., Liu, Z.Q., Yang, L., Gao, Y.: Towards big topic modeling. Inf. Sci. 390, 15\u201331 (2017)","journal-title":"Inf. Sci."},{"key":"23_CR7","unstructured":"Bhardwaj, M.: Parallel Approach for Implementing Data Mining Algorithms (2016)"},{"key":"23_CR8","unstructured":"Chong, P.K., Karuppiah, E.K., Yong, K.K.: A Multi-GPU framework for in-memory text data analytics. In: 2013 27th International Conference on Advanced Information Networking and, Applications Workshops (WAINA), pp. 1411\u20131416. IEEE (2013"},{"issue":"1","key":"23_CR9","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1007\/s42452-019-1836-y","volume":"2","author":"FS Gadelrab","year":"2020","unstructured":"Gadelrab, F.S., Haggag, M.H., Sadek, R.A.: Novel semantic tagging detection algorithms based non-negative matrix factorization. SN Appl. Sci. 2(1), 54 (2020)","journal-title":"SN Appl. Sci."},{"key":"23_CR10","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.jcss.2017.09.010","volume":"92","author":"W Yang","year":"2018","unstructured":"Yang, W., Li, K., Li, K.: A parallel computing method using blocked format with optimal partitioning for SpMV on GPU. J. Comput. Syst. Sci. 92, 152\u2013170 (2018)","journal-title":"J. Comput. Syst. Sci."},{"key":"23_CR11","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.jpdc.2015.06.010","volume":"85","author":"W Liu","year":"2015","unstructured":"Liu, W., Vinter, B.: A framework for general sparse matrix\u2013matrix multiplication on GPUs and heterogeneous processors. J. Parallel Distrib. Comput. 85, 47\u201361 (2015)","journal-title":"J. Parallel Distrib. Comput."},{"key":"23_CR12","unstructured":"Yuk, J.H.: A Large-Scale Sparse Matrix Multiplication Method based on Streaming Matrix to GPUs (Doctoral dissertation, DGIST) (2017)"},{"key":"23_CR13","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.ins.2014.08.062","volume":"292","author":"U Erra","year":"2015","unstructured":"Erra, U., Senatore, S., Minnella, F., Caggianese, G.: Approximate tf-idf based on topic extraction from massive message stream using the GPU. Inf. Sci. 292, 143\u2013161 (2015)","journal-title":"Inf. Sci."},{"issue":"2","key":"23_CR14","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.jpdc.2010.08.002","volume":"71","author":"Y Zhang","year":"2011","unstructured":"Zhang, Y., Mueller, F., Cui, X., Potok, T.: Data-intensive document clustering on graphics processing unit (GPU) clusters. J. Parallel Distrib. Comput. 71(2), 211\u2013224 (2011)","journal-title":"J. Parallel Distrib. Comput."},{"key":"23_CR15","unstructured":"Ciccoti, P., Oral, H.S., Kestor, G., Gioiosa, R., Strande, S., Taufer, M., Carrington, L.: Conquering Big Data with High Performance Computing. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF) (2016)"},{"issue":"2","key":"23_CR16","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1007\/s11227-018-2706-x","volume":"75","author":"CH Hsu","year":"2019","unstructured":"Hsu, C.H., Fox, G., Min, G., Sharma, S.: Advances in big data programming, system software and HPC convergence. J. Supercomputing 75(2), 489\u2013493 (2019)","journal-title":"J. Supercomputing"},{"key":"23_CR17","doi-asserted-by":"crossref","unstructured":"Shi, S., Wang, Q., Chu, X.: Performance modeling and, evaluation of distributed deep learning frameworks on gpus. In: 2018 IEEE 16th Intl Conf on Dependable, Autonomic and, Secure Computing, 16th Intl Conf on Pervasive Intelligence and, Computing, 4th Intl Conf on Big Data Intelligence and, Computing and, Cyber Science and, Technology Congress (DASC\/PiCom\/DataCom\/CyberSciTech), pp. 949\u2013957. IEEE (2018)\u200f","DOI":"10.1109\/DASC\/PiCom\/DataCom\/CyberSciTec.2018.000-4"},{"key":"23_CR18","unstructured":"Goldsborough, P.: A tour of TensorFlow. arXiv preprint \narXiv:1610.01178\n\n (2016)"},{"key":"23_CR19","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Kudlur, M., et al.: TensorFlow: a system for large-scale machine learning. OSDI 16, 265\u2013283 (2016)"},{"key":"23_CR20","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Ghemawat, S., et al.: Tensorflow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv preprint \narXiv:1603.04467\n\n (2016)\u200f"},{"key":"23_CR21","doi-asserted-by":"crossref","unstructured":"Xie, X., Liang, Y., Li, X., Tan, W.: CuLDA_CGS: Solving Large-Scale LDA Problems on GPUs. arXiv preprint \narXiv:1803.04631\n\n (2018)\u200f","DOI":"10.1145\/3307681.3325407"},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Fuentes-Pineda, G., Meza-Ruiz, I.V.: Topic Discovery in Massive Text Corpora Based on Min-Hashing. arXiv preprint \narXiv:1807.00938\n\n (2018)","DOI":"10.1016\/j.eswa.2019.06.024"},{"issue":"5","key":"23_CR23","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1007\/s11390-018-1871-y","volume":"33","author":"Y Li","year":"2018","unstructured":"Li, Y., Song, W.Z., Yang, B.: Stochastic variational inference-based parallel and online supervised topic model for large-scale text processing. J. Comput. Sci. Technol. 33(5), 1007\u20131022 (2018)","journal-title":"J. Comput. Sci. Technol."},{"issue":"1","key":"23_CR24","doi-asserted-by":"publisher","first-page":"57","DOI":"10.26599\/BDMA.2018.9020006","volume":"1","author":"B Zhao","year":"2018","unstructured":"Zhao, B., Zhou, H., Li, G., Huang, Y.: ZenLDA: large-scale topic model training on distributed data-parallel platf orm. Big Data Mining Anal. 1(1), 57\u201374 (2018)","journal-title":"Big Data Mining Anal."},{"key":"23_CR25","unstructured":"Gropp, C., Herzog, A., Safro, I., Wilson, P.W., Apon, A. W.: Scalable Dynamic Topic Modeling with Clustered Latent Dirichlet Allocation (clda). arXiv preprint \narXiv:1610.07703\n\n. (2016)"},{"key":"23_CR26","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.patcog.2017.11.002","volume":"76","author":"D Tu","year":"2018","unstructured":"Tu, D., Chen, L., Lv, M., Shi, H., Chen, G.: Hierarchical online NMF for detecting and tracking topic hierarchies in a text stream. Pattern Recogn. 76, 203\u2013214 (2018)","journal-title":"Pattern Recogn."},{"key":"23_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, D., Han, Y., Li, X.: Dynamic detection method of micro-blog topic based on time series. In: International Conference of Pioneering Computer Scientists, Engineers and, Educators, pp. 192\u2013200. Springer, Singapore (2018)\u200f","DOI":"10.1007\/978-981-13-2206-8_17"},{"key":"23_CR28","doi-asserted-by":"crossref","unstructured":"Mej\u00eda-Roa, E., Tabas-Madrid, D., Setoain, J., Garc\u00eda, C., Tirado, F., Pascual-Montano, A.: NMF-mGPU: non-negative matrix factorization on multi-GPU systems. BMC Bioinform. 16(1), 4Stochastic3 (2015)","DOI":"10.1186\/s12859-015-0485-4"},{"key":"23_CR29","unstructured":"Newman, D., Smyth, P., Steyvers, M.: Scalable parallel topic models. J. Intell. Community Res. Dev. 5 (2006)\u200f"},{"key":"23_CR30","doi-asserted-by":"crossref","unstructured":"Li, Y., Feng, D., Lu, M., Li, D.: A distributed topic model for large-scale streaming text. In: International Conference on Knowledge Science, Engineering and Management, pp. 37\u201348. Springer, Cham (2019)\u200f","DOI":"10.1007\/978-3-030-29563-9_4"},{"key":"23_CR31","doi-asserted-by":"crossref","unstructured":"Shi, S., Wang, Q., Chu, X.: Performance modeling and, evaluation of distributed deep learning frameworks on gpus. In: 2018 IEEE 16th Intl Conf on Dependable, Autonomic and, Secure Computing, 16th Intl Conf on Pervasive Intelligence and, Computing, 4th Intl Conf on Big Data Intelligence and, Computing and, Cyber Science and, Technology Congress (DASC\/PiCom\/DataCom\/CyberSciTech), pp. 949\u2013957. IEEE (2018)","DOI":"10.1109\/DASC\/PiCom\/DataCom\/CyberSciTec.2018.000-4"}],"container-title":["Studies in Systems, Decision and Control","Recent Advances in Intelligent Systems and Smart Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-47411-9_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,6,26]],"date-time":"2020-06-26T19:20:46Z","timestamp":1593199246000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-47411-9_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,27]]},"ISBN":["9783030474102","9783030474119"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-47411-9_23","relation":{},"ISSN":["2198-4182","2198-4190"],"issn-type":[{"type":"print","value":"2198-4182"},{"type":"electronic","value":"2198-4190"}],"subject":[],"published":{"date-parts":[[2020,6,27]]},"assertion":[{"value":"27 June 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}