Literatura académica sobre el tema "Text Data Streams"
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Artículos de revistas sobre el tema "Text Data Streams"
Liu, Yu-Bao, Jia-Rong Cai, Jian Yin y Ada Wai-Chee Fu. "Clustering Text Data Streams". Journal of Computer Science and Technology 23, n.º 1 (enero de 2008): 112–28. http://dx.doi.org/10.1007/s11390-008-9115-1.
Texto completoAggarwal, Charu C. y Philip S. Yu. "On clustering massive text and categorical data streams". Knowledge and Information Systems 24, n.º 2 (6 de agosto de 2009): 171–96. http://dx.doi.org/10.1007/s10115-009-0241-z.
Texto completoFRAHLING, GEREON, PIOTR INDYK y CHRISTIAN SOHLER. "SAMPLING IN DYNAMIC DATA STREAMS AND APPLICATIONS". International Journal of Computational Geometry & Applications 18, n.º 01n02 (abril de 2008): 3–28. http://dx.doi.org/10.1142/s0218195908002520.
Texto completoZhang, Yuhong, Guang Chu, Peipei Li, Xuegang Hu y Xindong Wu. "Three-layer concept drifting detection in text data streams". Neurocomputing 260 (octubre de 2017): 393–403. http://dx.doi.org/10.1016/j.neucom.2017.04.047.
Texto completoRusso, Matthew, Tatsunori Hashimoto, Daniel Kang, Yi Sun y Matei Zaharia. "Accelerating Aggregation Queries on Unstructured Streams of Data". Proceedings of the VLDB Endowment 16, n.º 11 (julio de 2023): 2897–910. http://dx.doi.org/10.14778/3611479.3611496.
Texto completoPetrasova, Svitlana, Nina Khairova y Anastasiia Kolesnyk. "TECHNOLOGY FOR IDENTIFICATION OF INFORMATION AGENDA IN NEWS DATA STREAMS". Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, n.º 1 (5) (12 de julio de 2021): 86–90. http://dx.doi.org/10.20998/2079-0023.2021.01.14.
Texto completoAL-Dyani, Wafa Zubair, Farzana Kabir Ahmad y Siti Sakira Kamaruddin. "A Survey on Event Detection Models for Text Data Streams". Journal of Computer Science 16, n.º 7 (1 de julio de 2020): 916–35. http://dx.doi.org/10.3844/jcssp.2020.916.935.
Texto completoHasan, Maryam, Elke Rundensteiner y Emmanuel Agu. "Automatic emotion detection in text streams by analyzing Twitter data". International Journal of Data Science and Analytics 7, n.º 1 (9 de febrero de 2018): 35–51. http://dx.doi.org/10.1007/s41060-018-0096-z.
Texto completoZhao, Xuezhuan, Ziheng Zhou, Lingling Li, Lishen Pei y Zhaoyi Ye. "Scene Text Detection Based On Fusion Network". International Journal of Pattern Recognition and Artificial Intelligence 35, n.º 10 (29 de mayo de 2021): 2153005. http://dx.doi.org/10.1142/s0218001421530050.
Texto completoAzkan, Can, Markus Spiekermann y Henry Goecke. "Uncovering Research Streams in the Data Economy Using Text Mining Algorithms". Technology Innovation Management Review 9, n.º 11 (1 de enero de 2019): 62–74. http://dx.doi.org/10.22215/timreview/1284.
Texto completoTesis sobre el tema "Text Data Streams"
Snowsill, Tristan. "Data mining in text streams using suffix trees". Thesis, University of Bristol, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.556708.
Texto completoMejova, Yelena Aleksandrovna. "Sentiment analysis within and across social media streams". Diss., University of Iowa, 2012. https://ir.uiowa.edu/etd/2943.
Texto completoHill, Geoffrey. "Sensemaking in Big Data: Conceptual and Empirical Approaches to Actionable Knowledge Generation from Unstructured Text Streams". Kent State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=kent1433597354.
Texto completoPinho, Roberto Dantas de. "Espaço incremental para a mineração visual de conjuntos dinâmicos de documentos". Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-14092009-123807/.
Texto completoVisual representations are often adopted to explore document collections, assisting in knowledge extraction, and avoiding the thorough analysis of thousands of documents. Document maps present individual documents in visual spaces in such a way that their placement reflects similarity relations or connections between them. Building these maps requires, among other tasks, placing each document and identifying interesting areas or subsets. A current challenge is to visualize dynamic data sets. In Information Visualization, adding and removing data elements can strongly impact the underlying visual space. That can prevent a user from preserving a mental map that could assist her/him on understanding the content of a growing collection of documents or tracking changes on the underlying data set. This thesis presents a novel algorithm to create dynamic document maps, capable of maintaining a coherent disposition of elements, even for completely renewed sets. The process is inherently incremental, has low complexity and places elements on a 2D grid, analogous to a chess board. Consistent results were obtained as compared to (non-incremental) multidimensional scaling solutions, even when applied to visualizing domains other than document collections. Moreover, the corresponding visualization is not susceptible to occlusion. To assist users in indentifying interesting subsets, a topic extraction technique based on association rule mining was also developed. Together, they create a visual space where topics and interesting subsets are highlighted and constantly updated as the data set changes
Wu, Yingyu. "Using Text based Visualization in Data Analysis". Kent State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=kent1398079502.
Texto completoYoung, Tom y Mark Wigent. "Dynamic Formatting of the Test Article Data Stream". International Foundation for Telemetering, 2010. http://hdl.handle.net/10150/605948.
Texto completoCrossman, Nathaniel C. "Stream Clustering And Visualization Of Geotagged Text Data For Crisis Management". Wright State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1590957641168863.
Texto completoFranco, Tom. "Performing Frame Transformations to Correctly Stream Position Data". University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1491562251744704.
Texto completoVickers, Stephen R. "Examining the Duplication of Flight Test Data Centers". International Foundation for Telemetering, 2011. http://hdl.handle.net/10150/595653.
Texto completoAircraft flight test data processing began with on site data analysis from the very first aircraft design. This method of analyzing flight data continued from the early 1900's to the present day. Today each new aircraft program builds a separate data center for post flight processing (PFP) to include operations, system administration, and management. Flight Test Engineers (FTE) are relocated from geographical areas to ramp up the manpower needed to analyze the PFP data center products and when the first phase of aircraft design and development is completed the FTE headcount is reduced with the FTE either relocated to another program or the FTE finds other employment. This paper is a condensed form of the research conducted by the author on how the methodology of continuing to build PFP data centers cost the aircraft company millions of dollars in development and millions of dollars on relocation plus relocation stress effects on FTE which can hinder productivity. This method of PFP data center development can be avoided by the consolidation of PFP data centers using present technology.
Yates, James William. "Mixing Staged Data Flow and Stream Computing Techniques in Modern Telemetry Data Acquisition/Processing Architectures". International Foundation for Telemetering, 1999. http://hdl.handle.net/10150/608707.
Texto completoToday’s flight test processing systems must handle many more complex data formats than just the PCM and analog FM data streams of yesterday. Many flight test programs, and their respective test facilities, are looking to leverage their computing assets across multiple customers and programs. Typically, these complex programs require the ability to handle video, packet, and avionics bus data in real time, in addition to handling the more traditional PCM format. Current and future telemetry processing systems must have an architecture that will support the acquisition and processing of these varied data streams. This paper describes various architectural designs of both staged data flow and stream computing architectures, including current and future implementations. Processor types, bus design, and the effects of varying data types, including PCM, video, and packet telemetry, will be discussed.
Libros sobre el tema "Text Data Streams"
Dufort, Benoit y Gordon W. Roberts. Analog Test Signal Generation Using Periodic ΣΔ-Encoded Data Streams. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4377-0.
Texto completo1959-, Roberts Gordon W., ed. Analog test signal generation using periodic [sigma delta]-encoded data streams. Boston: Kluwer Academic, 2000.
Buscar texto completoDufort, Benoit. Analog test signal generation using periodic [sigma delta]-encoded data streams. New York: Springer Science+Business Media, 2000.
Buscar texto completoVerderaime, V. Test load verification through strain data analysis. Washington, DC: National Aeronautics and Space Administration, 1995.
Buscar texto completoLane, Norman E. Users manual for the Automated Performance Test System (APTS). Orlando, FL: Essex Corp., 1990.
Buscar texto completoSwarts, Jason y Cheryl Geisler. Coding Streams of Language: Techniques for the Systematic Coding of Text, Talk, and Other Verbal Data. University Press of Colorado, 2020.
Buscar texto completoAnalyzing streams of language: Twelve steps to the systematic coding of text, talk, and other verbal data. New York: Longman, 2003.
Buscar texto completoGiacovazzo, Carmelo. Phasing in Crystallography. Oxford University Press, 2013. http://dx.doi.org/10.1093/oso/9780199686995.001.0001.
Texto completoAnalog Test Signal Generation Using Periodic -Encoded Data Streams. Island Press, 2000.
Buscar texto completoTest load verification through strain data analysis. MSFC, Ala: National Aeronautics and Space Administration, Marshall Space Flight Center, 1995.
Buscar texto completoCapítulos de libros sobre el tema "Text Data Streams"
Aggarwal, Charu C. "Mining Text Streams". En Mining Text Data, 297–321. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-3223-4_9.
Texto completoJoshi, Basanta, Umanga Bista y Manoj Ghimire. "Intelligent Clustering Scheme for Log Data Streams". En Computational Linguistics and Intelligent Text Processing, 454–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54903-8_38.
Texto completoRothkrantz, Leon J. M., Jacek C. Wojdeł y Pascal Wiggers. "Fusing Data Streams in Continuous Audio-Visual Speech Recognition". En Text, Speech and Dialogue, 33–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551874_5.
Texto completoLiu, Yubao, Jiarong Cai, Jian Yin y Ada Wai-Chee Fu. "Clustering Massive Text Data Streams by Semantic Smoothing Model". En Advanced Data Mining and Applications, 389–400. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73871-8_36.
Texto completoFeng, Xiao, Shuwu Zhang, Wei Liang y Jie Liu. "Efficient Location-Based Event Detection in Social Text Streams". En Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques, 213–22. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23862-3_21.
Texto completoFung, Gabriel Pui Cheong, Jeffrey Xu Yu y Hongjun Lu. "Classifying Text Streams in the Presence of Concept Drifts". En Advances in Knowledge Discovery and Data Mining, 373–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24775-3_45.
Texto completoBosch, Harald, Robert Krüger y Dennis Thom. "Data-Driven Exploration of Real-Time Geospatial Text Streams". En Machine Learning and Knowledge Discovery in Databases, 203–7. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23461-8_14.
Texto completoWindmann, Stefan y Christian Kühnert. "Information modeling and knowledge extraction for machine learning applications in industrial production systems". En Machine Learning for Cyber Physical Systems, 73–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62746-4_8.
Texto completoWeik, Martin H. "text data stream". En Computer Science and Communications Dictionary, 1773. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_19461.
Texto completoWittenberg, Thomas, Thomas Lang, Thomas Eixelberger y Roland Grube. "Acquisition of Semantics for Machine-Learning and Deep-Learning based Applications". En Unlocking Artificial Intelligence, 153–75. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64832-8_8.
Texto completoActas de conferencias sobre el tema "Text Data Streams"
Rajski, Janusz, Maciej Trawka, Jerzy Tyszer y Bartosz Włodarczak. "Test Data Encryption with a New Stream Cipher". En 2024 IEEE International Test Conference (ITC), 313–22. IEEE, 2024. http://dx.doi.org/10.1109/itc51657.2024.00052.
Texto completoAhmad, Zaaba, Azlinah Mohamed, Mike Conway, Rozanizam Zakaria, Noor Hasimah Ibrahim Teo y Ruhaila Maskat. "MyDAS Corpus: Malay Social Media Texts for Detecting Depression, Anxiety, and Stress on Facebook". En 2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS), 111–20. IEEE, 2024. http://dx.doi.org/10.1109/aidas63860.2024.10730385.
Texto completoZuo, Yunfan, Yuyang Ye, Hongchao Zhang, Tinghuan Chen, Hao Yan y Longxing Shi. "A Graph-Learning-Driven Prediction Method for Combined Electromigration and Thermomigration Stress on Multi-Segment Interconnects". En 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE), 1–6. IEEE, 2024. http://dx.doi.org/10.23919/date58400.2024.10546799.
Texto completoCalvo Martinez, John. "Event Mining over Distributed Text Streams". En WSDM 2018: The Eleventh ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3159652.3170462.
Texto completoHe, Qi, Kuiyu Chang, Ee-Peng Lim y Jun Zhang. "Bursty Feature Representation for Clustering Text Streams". En Proceedings of the 2007 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2007. http://dx.doi.org/10.1137/1.9781611972771.50.
Texto completoWhitney, Paul, Dave Engel y Nick Cramer. "Mining for Surprise Events Within Text Streams". En Proceedings of the 2009 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2009. http://dx.doi.org/10.1137/1.9781611972795.53.
Texto completoYin, Jianhua, Daren Chao, Zhongkun Liu, Wei Zhang, Xiaohui Yu y Jianyong Wang. "Model-based Clustering of Short Text Streams". En KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3219819.3220094.
Texto completoZhang, Yang, Xue Li y Maria Orlowska. "One-Class Classification of Text Streams with Concept Drift". En 2008 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2008. http://dx.doi.org/10.1109/icdmw.2008.54.
Texto completoSun, Gang, Jianqiao Liu, Wei Mengxue, Wang Zhongxin, Zhao Jia y Guan Xiaowen. "An Ensemble Classification Algorithm for Imbalanced Text Data Streams". En 2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE, 2020. http://dx.doi.org/10.1109/icaica50127.2020.9182576.
Texto completoWang, Xiting, Shixia Liu, Yangqiu Song y Baining Guo. "Mining evolutionary multi-branch trees from text streams". En KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2487575.2487603.
Texto completoInformes sobre el tema "Text Data Streams"
Hajj, Ramez y Babak Asadi. Review of Illinois Multiple Stress Creep and Recovery Data for Future Implementation. Illinois Center for Transportation, diciembre de 2023. http://dx.doi.org/10.36501/0197-9191/23-027.
Texto completoGeorge, D. L. y R. C. Burkey. PR-015-06603-R01 Tests of Instruments for Measuring Hydrocarbon Dew Points in Natural Gas Streams Phase 1. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), enero de 2008. http://dx.doi.org/10.55274/r0010820.
Texto completoGinzel. L51748 Detection of Stress Corrosion Induced Toe Cracks-Advancement of the Developed Technique. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), marzo de 1996. http://dx.doi.org/10.55274/r0010659.
Texto completoGeorge. PR-015-08610-R01 Laboratory Conformation of the Effect of Methanol on Gas Chromatograph Performance. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), noviembre de 2010. http://dx.doi.org/10.55274/r0010717.
Texto completoZevotek, Robin, Keith Stakes y Joseph Willi. Impact of Fire Attack Utilizing Interior and Exterior Streams on Firefighter Safety and Occupant Survival: Full-Scale Experiments. UL Firefighter Safety Research Institute, enero de 2018. http://dx.doi.org/10.54206/102376/dnyq2164.
Texto completoParkins y Leis. L51654 Spatial Densities of Stress-Corrosion Cracks in Line-Pipe Steels. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), abril de 1992. http://dx.doi.org/10.55274/r0010367.
Texto completoVaughn, Tim y Daniel Olsen. PR-179-19601-R03 Evaluation of Online Analyzers for Multiple Gas Contaminants-Field Test. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), septiembre de 2022. http://dx.doi.org/10.55274/r0012242.
Texto completoLeis, B. N., O. C. Chang y T. A. Bubenik. GTI-000232 Leak vs Rupture for Steel Low-Stress Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), enero de 2001. http://dx.doi.org/10.55274/r0011871.
Texto completoGutiérrez, José E. y Luis Fernández Lafuerza. Credit line runs and bank risk management: evidence from the disclosure of stress test results. Madrid: Banco de España, diciembre de 2022. http://dx.doi.org/10.53479/25006.
Texto completoGutiérrez, José E. y Luis Fernández Lafuerza. Credit line runs and bank risk management: evidence from the disclosure of stress test results. Madrid: Banco de España, enero de 2023. http://dx.doi.org/10.53479/24998.
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