Relevant bibliographies by topics / Streaming technology (Telecommunications) Data mining

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Streaming technology (Telecommunications) Data mining'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "Streaming technology (Telecommunications) Data mining"

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Manasvi Gawande, Manjiri Pise. "Application of Data Mining in Telecommunication Industry." International Journal on Recent and Innovation Trends in Computing and Communication 7, no. 1 (2019): 05–08. http://dx.doi.org/10.17762/ijritcc.v7i1.5218.

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Data Mining is a logical procedure intended to investigate data (normally a lot of data - commonly business or market related - otherwise called "enormous data") looking for predictable examples as well as methodical connections amongst factors, and after that to approve the discoveries by applying the recognized examples to new subsets of data. The telecommunications industry inside the division of data and correspondence technology is comprised of all Telecommunications/telephone companies and web access suppliers and assumes the urgent part in the development of versatile interchanges and t
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Du, Jiang Yi, and Yi Meng Chen. "Applicatiions and Research of Data Mining in Teaching." Applied Mechanics and Materials 58-60 (June 2011): 2659–63. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2659.

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Data mining technology has been widely used in the retail, finance, telecommunications and many other industries. With the promotion of education informationiation, useing the data mining technology in network education , finding useful knowledge in large education data to guide education and develop education become a necessary research. Based on the descriptionof the concept，characteristics, methods, and implement process of data mining, this paper introduces its several applications in teaching and the positive effect of teaching.
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Kelly, M. G. (Peggy), and James H. Wiebe. "Teaching Mathematics with Technology: Mining Mathematics on the Internet." Arithmetic Teacher 41, no. 5 (1994): 276–81. http://dx.doi.org/10.5951/at.41.5.0276.

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Throughout the Curriculum and Evaluation Standards for School Mathematics (NCTM 1989) the notion of students and teachers as critical thinkers, information seekers, and problem solvers is a priority. The Internet, an electronic highway connected by gateways from one computer network to another, furnishes a telecommunications link around the world. The Internet enables students and teachers to access authentic, real-time data for critical analysis. With access to such Internet service providers as a university computer network or a commercial service like Compuserve, Prodigy, or Applelink, stud
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Tang, De Huai. "Data Mining’s Network Traffic Data Analysis in Android Mobile Terminal." Applied Mechanics and Materials 644-650 (September 2014): 2055–58. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2055.

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With the rapid development of communication industry in China from 2G to 4G networks, operators’ competition is intense in data flow business. Android mobile terminal is now widely used by people. Network traffic analysis is the premise to improve network speed and real needs of customers, excavate valuable information in vast amounts of data, and an important work for network providers analyzing flow rate and value. This paper mainly introduced the relevant contents of data mining, and data mining’s network traffic data analysis in Android mobile terminal.With the development of computer tech
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Khalaf, Osamah Ibrahim, Ghaida Muttashar Abdulsahib, Hamed Daei Kasmaei, and Kingsley A. Ogudo. "A New Algorithm on Application of Blockchain Technology in Live Stream Video Transmissions and Telecommunications." International Journal of e-Collaboration 16, no. 1 (2020): 16–32. http://dx.doi.org/10.4018/ijec.2020010102.

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This article develops and defines Blockchain technology in its classic format. New suggested proposed algorithms are then analyzed in order to introduce new and modified versions of Blockchain technology. After that, fundamental infrastructure is presented in order to represent its application in new generation of telecommunications. In addition, this article interrogates these algorithms and their efficiency to make secure connections that transfer data packets in any format (boxes or packets of information) in a secure and encrypted method at which sender and receiver of information remain a
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Borodo, Salisu Musa, Siti Mariyam Shamsuddin, and Shafaatunnur Hasan. "Big Data Platforms and Techniques." Indonesian Journal of Electrical Engineering and Computer Science 1, no. 1 (2016): 191. http://dx.doi.org/10.11591/ijeecs.v1.i1.pp191-200.

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Data is growing at unprecedented rate and has led to huge volume generated; the data sources include mobile, internet and sensors. This voluminous data is generated and updated at high velocity by batch and streaming platforms. This data is also varied along structured and unstructured types. This volume, velocity and variety of data led to the term big data. Big data has been premised to contain untapped knowledge, its exploration and exploitation is termed big data analytics. This literature reviewed platforms such as batch processing, real time processing and interactive analytics used in b
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Chen, Chih Ping, Ju-Yin Weng, Chin-Sheng Yang, and Fan-Mei Tseng. "Employing a data mining approach for identification of mobile opinion leaders and their content usage patterns in large telecommunications datasets." Technological Forecasting and Social Change 130 (May 2018): 88–98. http://dx.doi.org/10.1016/j.techfore.2018.01.014.

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Guo, Kaiyuan, and Wenbo Wang. "Research on Telecom Fraud Detection Model Based on Cellular Network Data." Journal of Networking and Telecommunications 2, no. 1 (2020): 12. http://dx.doi.org/10.18282/jnt.v2i1.835.

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<p align="justify">With the rapid development of wireless communication technology, the use of mobile phones and other means of communication for telecommunications fraud has become a major problem that endangers user security. Aiming at this problem, this paper constructs a telecom fraud user detection model by in-depth analysis and mining of cellular network data. The model includes data processing, CNNcombine algorithm and model evaluation. First, in the data processing part, the data set is subjected to feature screening, coding, sampling, and the like. Secondly, the CNNcombine algor
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Srivani, B., N. Sandhya, and B. Padmaja Rani. "Literature review and analysis on big data stream classification techniques." International Journal of Knowledge-based and Intelligent Engineering Systems 24, no. 3 (2020): 205–15. http://dx.doi.org/10.3233/kes-200042.

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Rapid growth in technology and information lead the human to witness the improved growth in velocity, volume of data, and variety. The data in the business organizations demonstrate the development of big data applications. Because of the improving demand of applications, analysis of sophisticated streaming big data tends to become a significant area in data mining. One of the significant aspects of the research is employing deep learning approaches for effective extraction of complex data representations. Accordingly, this survey provides the detailed review of big data classification methodo
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Brzustewicz, Paweł, and Anupam Singh. "Sustainable Consumption in Consumer Behavior in the Time of COVID-19: Topic Modeling on Twitter Data Using LDA." Energies 14, no. 18 (2021): 5787. http://dx.doi.org/10.3390/en14185787.

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By using text mining techniques, this study identifies the topics of sustainable consumption that are important during the COVID-19 pandemic. An Application Programming Interface (API) streaming method was used to extract the data from Twitter. A total of 14,591 tweets were collected using Twitter streaming API. However, after data cleaning, 13,635 tweets were considered for analysis. The objectives of the study are to identify (1) the topics users tweet about sustainable consumption and (2) to detect the emotion-based sentiments in the tweets. The study used Latent Dirichlet Allocation (LDA)
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Dissertations / Theses on the topic "Streaming technology (Telecommunications) Data mining"

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Hohman, Elizabeth Leeds. "A dynamic graph model for representing streaming text documents." Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3062.

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Thesis (Ph.D.)--George Mason University, 2008.<br>Vita: p. 141. Thesis director: Edward J. Wegman. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computational Sciences and Informatics. Title from PDF t.p. (viewed July 3, 2008). Includes bibliographical references (p. 105-110). Also issued in print.
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Jin, Ruoming. "New techniques for efficiently discovering frequent patterns." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1121795612.

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Thesis (Ph. D.)--Ohio State University, 2005.<br>Title from first page of PDF file. Document formatted into pages; contains xvii, 170 p.; also includes graphics. Includes bibliographical references (p. 160-170). Available online via OhioLINK's ETD Center
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Xu, Jialing, and 徐加羚. "On live data streaming over peer-to-peer networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44904472.

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Krasic, Charles. "A framework for quality-adaptive media streaming--encode once, stream anywhere /." Full text open access at:, 2004. http://content.ohsu.edu/u?/etd,630.

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Bhat, Amit. "Low-latency Estimates for Window-Aggregate Queries over Data Streams." PDXScholar, 2011. https://pdxscholar.library.pdx.edu/open_access_etds/161.

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Obtaining low-latency results from window-aggregate queries can be critical to certain data-stream processing applications. Due to a DSMS's lack of control over incoming data (typically, because of delays and bursts in data arrival), timely results for a window-aggregate query over a data stream cannot be obtained with guarantees about the results' accuracy. In this thesis, I propose a technique, which I term prodding, to obtain early result estimates for window-aggregate queries over data streams. The early estimates are obtained in addition to the regular query results. The proposed techniqu
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Mukherji, Abhishek. "SNIF TOOL - Sniffing for patterns in continuous streams." Worcester, Mass. : Worcester Polytechnic Institute, 2008. http://www.wpi.edu/Pubs/ETD/Available/etd-021108-150542/.

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Lee, Yen-Chi. "Error resilient video streaming over lossy networks." Diss., Available online, Georgia Institute of Technology, 2004:, 2003. http://etd.gatech.edu/theses/available/etd-04082004-180302/unrestricted/lee%5fyen-chi%5f200312%5fphd.pdf.

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Kumar, Abhishek. "Network Data Streaming: Algorithms for Network Measurement and Monitoring." Diss., Available online, Georgia Institute of Technology, 2005, 2005. http://etd.gatech.edu/theses/available/etd-11172005-143837/.

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Thesis (Ph. D.)--Computing, Georgia Institute of Technology, 2006.<br>Dr. Mostafa Ammar, Committee Member ; Dr. Mark Crovella, Committee Member ; Dr. Constantinos Dovrolis, Committee Member ; Dr. Ellen Zegura, Committee Chair ; Dr. Jun Xu, Committee Chair. Vita. Includes bibliographical references.
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Li, Jin. "Window Queries Over Data Streams." PDXScholar, 2008. https://pdxscholar.library.pdx.edu/open_access_etds/2675.

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Evaluating queries over data streams has become an appealing way to support various stream-processing applications. Window queries are commonly used in many stream applications. In a window query, certain query operators, especially blocking operators and stateful operators, appear in their windowed versions. Previous research work in evaluating window queries typically requires ordered streams and this order requirement limits the implementations of window operators and also carries performance penalties. This thesis presents efficient and flexible algorithms for evaluating window queries. We
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Hilley, David B. "Temporal streams programming abstractions for distributed live stream analysis applications /." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31695.

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Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2010.<br>Committee Chair: Ramachandran, Umakishore; Committee Member: Clark, Nathan; Committee Member: Haskin, Roger; Committee Member: Pu, Calton; Committee Member: Rehg, James. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Books on the topic "Streaming technology (Telecommunications) Data mining"

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1951-, Özsu M. Tamer, ed. Data stream management. Morgan & Claypool, 2010.

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Roumeliotis, Rachel, ed. Visualizing Streaming Data: Interactive Analysis Beyond Static Limits. O’Reilly Media, 2018.

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Roumeliotis, Rachel, and Jeff Bleiel, eds. Streaming Systems: The What, Where, When and How of Large-Scale Data Processing. O'Reilly Media, 2018.

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Jacques, Roy. The power of now: Real-time analytics and IBM InfoSphere Streams. McGraw-Hill Education, 2015.

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Damper, R. I. Data Mining Techniques Speech Syn (Telecommunications Technology & Applications Series). Springer, 2001.

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author, Telang Rahul, ed. Streaming, sharing, stealing: Big data and the future of entertainment. MIT Press, 2016.

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Streaming: Movies, Media, and Instant Access. University Press of Kentucky, 2013.

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Kafka Streams in Action: Real-time apps and microservices with the Kafka Streams API. Manning Publications, 2018.

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PacketCable Implementation (Networking Technology). Cisco Press, 2007.

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Grinapol, Corinne. Reed Hastings and Netflix. 2014.

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Book chapters on the topic "Streaming technology (Telecommunications) Data mining"

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Jiang, Yanxin, Xian Mei, and Guanglu Sun. "Research on Data Mining Technology of Social Network Associated Information." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93719-9_3.

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Jiang, Yi, and Yi-huo Jiang. "Data Mining Method of English Online Learning Behavior Based on Machine Learning Technology." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84383-0_11.

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Weiss, Gary. "Data Mining in the Telecommunications Industry." In Networking and Telecommunications. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-986-1.ch015.

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The telecommunications industry was one of the first to adopt data mining technology. This is most likely because telecommunication companies routinely generate and store enormous amounts of high-quality data, have a very large customer base, and operate in a rapidly changing and highly competitive environment. Telecommunication companies utilize data mining to improve their marketing efforts, identify fraud, and better manage their telecommunication networks. However, these companies also face a number of data mining challenges due to the enormous size of their data sets, the sequential and temporal aspects of their data, and the need to predict very rare events—such as customer fraud and network failures—in real-time. The popularity of data mining in the telecommunications industry can be viewed as an extension of the use of expert systems in the telecommunications industry (Liebowitz, 1988). These systems were developed to address the complexity associated with maintaining a huge network infrastructure and the need to maximize network reliability while minimizing labor costs. The problem with these expert systems is that they are expensive to develop because it is both difficult and timeconsuming to elicit the requisite domain knowledge from experts. Data mining can be viewed as a means of automatically generating some of this knowledge directly from the data.
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Abdelhafez, Hoda Ahmed. "Mining Big Data and Streams." In Encyclopedia of Information Science and Technology, Fourth Edition. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2255-3.ch036.

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Mining big data is getting a lot of attention currently because the businesses need more complex information in order to increase their revenue and gain competitive advantage. Therefore, mining the huge amount of data as well as mining real-time data needs to be done by new data mining techniques/approaches. This chapter will discuss big data volume, variety and velocity, data mining techniques and open source tools for handling very large datasets. Moreover, the chapter will focus on two industrial areas telecommunications and healthcare and lessons learned from them.
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Kudyba, Stephan, and Richard Hoptroff. "An Introduction to Information Technology and Business Intelligence." In Data Mining and Business Intelligence. IGI Global, 2001. http://dx.doi.org/10.4018/978-1-930708-03-7.ch001.

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The world of commerce has undergone a transformation since the early 1990s, which has increasingly included the utilization of information technologies by firms across industry sectors in order to achieve greater productivity and profitability. In other words, through use of such technologies as mainframes, PCs, telecommunications, state-of-the-art software applications and the Internet, corporations seek to utilize productive resources in a way that augment the efficiency with which they provide the most appropriate mix of goods and services to their ultimate consumer. This process has provided the backbone to the evolution of the information economy which has included increased investment in information technology (IT), the demand for IT labor and the initiation of such new paradigms as e-commerce.
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Lu, Shen, and Richard S. Segall. "Data Streaming Processing Window Joined With Graphics Processing Units (GPUs)." In Encyclopedia of Information Science and Technology, Fifth Edition. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3479-3.ch043.

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Big data is large-scale data and can be either discrete or continuous. This article entails research that discusses the continuous case of big data often called “data streaming.” More and more businesses will depend on being able to process and make decisions on streams of data. This article utilizes the algorithmic side of data stream processing often called “stream analytics” or “stream mining.” Data streaming Windows Join can be improved by using graphics processing unit (GPU) for higher performance computing. Data streams are generated by two independent threads: one thread can be used to generate Data Stream A, and the other thread can be used to generate Data Stream B. One would use a Windows Join thread to merge the two data streams, which is also the process of “Data Stream Window Join.” The Window Join process can be implemented in parallel that can efficiently improve the computing speed. Experiments are provided for Data Stream Window Joins using both static and dynamic data.
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Govindarajan, M. "Challenges for Big Data Security and Privacy." In Encyclopedia of Information Science and Technology, Fourth Edition. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2255-3.ch033.

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Security and privacy issues are magnified by the volume, variety, and velocity of Big Data, such as Large-scale cloud infrastructures, diversity of data sources and formats, the streaming nature of data acquisition and high volume inter-cloud migration. In the past, Big Data was limited to very large organizations such as governments and large enterprises that could afford to create and own the infrastructure necessary for hosting and mining large amounts of data. These infrastructures were typically proprietary and were isolated from general networks. Today, Big Data is cheaply and easily accessible to organizations large and small through public cloud infrastructure. The purpose of this chapter is to highlight the Big Data security and privacy challenges and also presents some solutions for these challenges, but it does not provide a definitive solution for the problem. It rather points to some directions and technologies that might contribute to solve some of the most relevant and challenging Big Data security and privacy issues.
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Alexiou, A. "Adaptive Transmission of Multimedia Data over UMTS." In Encyclopedia of Mobile Computing and Commerce. IGI Global, 2007. http://dx.doi.org/10.4018/978-1-59904-002-8.ch004.

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As communications technology is being developed, users’ demand for multimedia services raises. Meanwhile, the Internet has enjoyed tremendous growth in recent years. Consequently, there is a great interest in using the IP-based networks to provide multimedia services. One of the most important areas in which the issues are being debated is the development of standards for the universal mobile telecommunications system (UMTS). UMTS constitutes the third generation of cellular wireless networks which aims to provide high-speed data access along with real-time voice calls. Wireless data is one of the major boosters of wireless communications and one of the main motivations of the next-generation standards. Bandwidth is a valuable and limited resource for UMTS and every wireless network in general. Therefore, it is of extreme importance to exploit this resource in the most efficient way. Consequently, when a user experiences a streaming video, there should be enough bandwidth available at any time for any other application that the mobile user might need. In addition, when two different applications run together, the network should guarantee that there is no possibility for any of the above-mentioned applications to prevail against the other by taking all the available channel bandwidth. Since Internet applications adopt mainly TCP as the transport protocol, while streaming applications mainly use RTP, the network should guarantee that RTP does not prevail against the TCP traffic. This means that there should be enough bandwidth available in the wireless channel for the Internet applications to run properly.
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Krithika L. B., Abhisek Mazumdar, Rajesh Kaluri, and Jing Wang. "A Framework on Enterprise-Grade Smart Contract Using Blockchain." In Transforming Businesses With Bitcoin Mining and Blockchain Applications. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0186-3.ch005.

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Blockchain technology is very trending and promising. It can revolutionize the traditional way of manipulation of data in many industries. There are industries which blockchain can disrupt: banking, cyber security, smart contract, insurance, cloud storage, government, healthcare, media streaming. The decentralized approach of blockchain using peer-to-peer system to verify the correct record of the ledger, which builds a trust in the system. A system can be compiled and made to get adopted with the concept of smart contract. The aim of the work is to develop a system that is flexible enough to get implemented in the industries like finance, cyber security, data storage, buying and selling of properties, healthcare, etc. This will use a one-way encryption method known as SHA-256. A block with the 256-character code bind with the other metadata of the block will be termed as a smart contract for the item.
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Conference papers on the topic "Streaming technology (Telecommunications) Data mining"

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Liu, Jia, Lin Liu, and Wei Chen. "The Research and Application on Streaming Data of GIS Data Mining." In 2009 First International Workshop on Database Technology and Applications, DBTA. IEEE, 2009. http://dx.doi.org/10.1109/dbta.2009.140.

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"Session TAM1-1: Data Mining." In 2009 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology. IEEE, 2009. http://dx.doi.org/10.1109/ecticon.2009.5137088.

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Perova, Iryna, Yelizaveta Brazhnykova, Nelia Miroshnychenko, and Yevgeniy Bodyanskiy. "Information Technology for Medical Data Stream Mining." In 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET). IEEE, 2020. http://dx.doi.org/10.1109/tcset49122.2020.235399.

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Joao, Zolana, Mjumo Mzyece, and Anish Kurien. "Matrix Decomposition Methods for the Improvement of Data Mining in Telecommunications." In 2009 IEEE Vehicular Technology Conference (VTC 2009-Fall). IEEE, 2009. http://dx.doi.org/10.1109/vetecf.2009.5378904.

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Dragovic, Rade, Miodrag Ivkovic, Bojan Perovic, and Djuro Klipa. "Dataveillance and data mining as a technology support to the process of investigation." In 2011 19th Telecommunications Forum Telfor (TELFOR). IEEE, 2011. http://dx.doi.org/10.1109/telfor.2011.6143780.

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Wongchinsri, Pornwatthana, and Werasak Kuratach. "A survey - data mining frameworks in credit card processing." In 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2016. http://dx.doi.org/10.1109/ecticon.2016.7561287.

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Lei Li, Fang-Cheng Shen, and De-Zhang Yang. "Application of data mining based on Rough Sets in the field of telecommunications." In 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccsit.2010.5565033.

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Chen, Ke, and Jia Wang. "The Data Mining Technology Based on CIMS and Its Application on Automotive Remanufacturing." In 1st International ICST Conference on Forensic Applications and Techniques in Telecommunications, Information and Multimedia. ACM, 2008. http://dx.doi.org/10.4108/wkdd.2008.2659.

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Jongmuenwai, Benjapuk, Sudajai Lowanichchai, and Saisunee Jabjone. "Comparision using Data Mining Algorithm Techniques for Predicting of Dengue fever Data in Northeastern of Thailand." In 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2018. http://dx.doi.org/10.1109/ecticon.2018.8619953.

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Zhang, Junsheng, and Ming Bai. "Research on the Application of Data Mining to Customer Relationship Management in the Mobile Telecommunications Industry." In 2011 International Conference on Internet Technology and Applications (iTAP). IEEE, 2011. http://dx.doi.org/10.1109/itap.2011.6006233.

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