Relevant bibliographies by topics / Streaming Data Processing for Machine Learning

Contents

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

Academic literature on the topic 'Streaming Data Processing for Machine Learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Streaming Data Processing for Machine Learning"

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Bajwa, Waheed U., Volkan Cevher, Dimitris Papailiopoulos, and Anna Scaglione. "Machine Learning From Distributed, Streaming Data [From the Guest Editors]." IEEE Signal Processing Magazine 37, no. 3 (2020): 11–13. http://dx.doi.org/10.1109/msp.2020.2972654.

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Khan, Muhammad Taimoor, Mehr Durrani, Shehzad Khalid, and Furqan Aziz. "Online Knowledge-Based Model for Big Data Topic Extraction." Computational Intelligence and Neuroscience 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/6081804.

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Lifelong machine learning (LML) models learn with experience maintaining a knowledge-base, without user intervention. Unlike traditional single-domain models they can easily scale up to explore big data. The existing LML models have high data dependency, consume more resources, and do not support streaming data. This paper proposes online LML model (OAMC) to support streaming data with reduced data dependency. With engineering the knowledge-base and introducing new knowledge features the learning pattern of the model is improved for data arriving in pieces. OAMC improves accuracy as topic cohe
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Bandi, Raswitha, J. Amudhavel, and R. Karthik. "Machine Learning with PySpark - Review." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 1 (2018): 102. http://dx.doi.org/10.11591/ijeecs.v12.i1.pp102-106.

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<p>A reasonable distributed memory-based Computing system for machine learning is Apache Spark. Spark is being superior in computing when compared with Hadoop. Apache Spark is a quick, simple to use for handling big data that has worked in modules of Machine Learning, streaming SQL, and graph processing. We can apply machine learning algorithms to big data easily, which makes it simple by using Spark and its machine learning library MLlib, even this can be made simpler by using the Python API PySpark. This paper presents the study on how to develop machine learning algorithms in PySpark.
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Boachie, Emmanuel, and Chunlin Li. "Big Data Processing with Apache Spark in University Institutions: Spark Streaming and Machine Learning Algorithm." International Journal of Continuing Engineering Education and Life-Long Learning 28, no. 4 (2018): 1. http://dx.doi.org/10.1504/ijceell.2018.10017171.

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Boachie, Emmanuel, and Chunlin Li. "Big data processing with Apache Spark in university institutions: spark streaming and machine learning algorithm." International Journal of Continuing Engineering Education and Life-Long Learning 29, no. 1/2 (2019): 5. http://dx.doi.org/10.1504/ijceell.2019.099217.

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Hassan, Fawzya, and Masoud E. Shaheen. "Predicting Diabetes from Health-based Streaming Data using Social Media, Machine Learning and Stream Processing Technologies." International Journal of Engineering Research and Technology 13, no. 8 (2020): 1957. http://dx.doi.org/10.37624/ijert/13.8.2020.1957-1967.

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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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Mahmood, Tariq, and Tatheer Fatima. "CONCEPT DRIFT IN STREAMING DATA: A SYSTEMATIC LITERATURE REVIEW." KIET Journal of Computing and Information Sciences 4, no. 1 (2021): 17. http://dx.doi.org/10.51153/kjcis.v4i1.43.

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World is generating immeasurable amount of data every minute, that needs to be analyzed for better decision making. In order to fulfil this demand of faster analytics, businesses are adopting efficient stream processing and machine learning techniques. However, data streams are particularly challenging to handle. One of the prominent problems faced while dealing with streaming data is concept drift. Concept drift is described as, an unexpected change in the underlying distribution of the streaming data that can be observed as time passes. In this work, we have conducted a systematic literature
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Cumbane, Silvino Pedro, and Gyozo Gidófalvi. "Review of Big Data and Processing Frameworks for Disaster Response Applications." ISPRS International Journal of Geo-Information 8, no. 9 (2019): 387. http://dx.doi.org/10.3390/ijgi8090387.

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Natural hazards result in devastating losses in human life, environmental assets and personal, and regional and national economies. The availability of different big data such as satellite imageries, Global Positioning System (GPS) traces, mobile Call Detail Records (CDRs), social media posts, etc., in conjunction with advances in data analytic techniques (e.g., data mining and big data processing, machine learning and deep learning) can facilitate the extraction of geospatial information that is critical for rapid and effective disaster response. However, disaster response systems development
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Kim, Yoon-Ki, and Yongsung Kim. "DiPLIP: Distributed Parallel Processing Platform for Stream Image Processing Based on Deep Learning Model Inference." Electronics 9, no. 10 (2020): 1664. http://dx.doi.org/10.3390/electronics9101664.

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Recently, as the amount of real-time video streaming data has increased, distributed parallel processing systems have rapidly evolved to process large-scale data. In addition, with an increase in the scale of computing resources constituting the distributed parallel processing system, the orchestration of technology has become crucial for proper management of computing resources, in terms of allocating computing resources, setting up a programming environment, and deploying user applications. In this paper, we present a new distributed parallel processing platform for real-time large-scale ima
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Dissertations / Theses on the topic "Streaming Data Processing for Machine Learning"

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García-Martín, Eva. "Extraction and Energy Efficient Processing of Streaming Data." Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15532.

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The interest in machine learning algorithms is increasing, in parallel with the advancements in hardware and software required to mine large-scale datasets. Machine learning algorithms account for a significant amount of energy consumed in data centers, which impacts the global energy consumption. However, machine learning algorithms are optimized towards predictive performance and scalability. Algorithms with low energy consumption are necessary for embedded systems and other resource constrained devices; and desirable for platforms that require many computations, such as data centers. Data s
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Kumar, Saurabh. "Real-Time Road Traffic Events Detection and Geo-Parsing." Thesis, Purdue University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10842958.

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<p> In the 21<sup>st</sup> century, there is an increasing number of vehicles on the road as well as a limited road infrastructure. These aspects culminate in daily challenges for the average commuter due to congestion and slow moving traffic. In the United States alone, it costs an average US driver $1200 every year in the form of fuel and time. Some positive steps, including (a) introduction of the push notification system and (b) deploying more law enforcement troops, have been taken for better traffic management. However, these methods have limitations and require extensive planning. Anoth
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Åkerström, Emelie. "Real-time Outlier Detection using Unbounded Data Streaming and Machine Learning." Thesis, Luleå tekniska universitet, Datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-80044.

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Accelerated advancements in technology, the Internet of Things, and cloud computing have spurred an emergence of unstructured data that is contributing to rapid growth in data volumes. No human can manage to keep up with monitoring and analyzing these unbounded data streams and thus predictive and analytic tools are needed. By leveraging machine learning this data can be converted into insights which are enabling datadriven decisions that can drastically accelerate innovation, improve user experience, and drive operational efficiency. The purpose of this thesis is to design and implement a sys
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Wang, Zheng. "Machine learning based mapping of data and streaming parallelism to multi-cores." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5664.

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Multi-core processors are now ubiquitous and are widely seen as the most viable means of delivering performance with increasing transistor densities. However, this potential can only be realised if the application programs are suitably parallel. Applications can either be written in parallel from scratch or converted from existing sequential programs. Regardless of how applications are parallelised, the code must be efficiently mapped onto the underlying platform to fully exploit the hardware’s potential. This thesis addresses the problem of finding the best mappings of data and streaming para
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Alzubi, Omar A. "Designing machine learning ensembles : a game coalition approach." Thesis, Swansea University, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.678293.

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Osama, Muhammad. "Machine learning for spatially varying data." Licentiate thesis, Uppsala universitet, Avdelningen för systemteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-429234.

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Many physical quantities around us vary across space or space-time. An example of a spatial quantity is provided by the temperature across Sweden on a given day and as an example of a spatio-temporal quantity we observe the counts of the corona virus cases across the globe. Spatial and spatio-temporal data enable opportunities to answer many important questions. For example, what the weather would be like tomorrow or where the highest risk for occurrence of a disease is in the next few days? Answering questions such as these requires formulating and learning statistical models. One of the chal
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Awodokun, Olugbenga. "Classification of Patterns in Streaming Data Using Clustering Signatures." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504880155623189.

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Fothergill, John Simon. "The coaching-machine learning interface : indoor rowing." Thesis, University of Cambridge, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648459.

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de, la Rúa Martínez Javier. "Scalable Architecture for Automating Machine Learning Model Monitoring." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280345.

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Last years, due to the advent of more sophisticated tools for exploratory data analysis, data management, Machine Learning (ML) model training and model serving into production, the concept of MLOps has gained more popularity. As an effort to bring DevOps processes to the ML lifecycle, MLOps aims at more automation in the execution of diverse and repetitive tasks along the cycle and at smoother interoperability between teams and tools involved. In this context, the main cloud providers have built their own ML platforms [4, 34, 61], offered as services in their cloud solutions. Moreover, multip
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Svantesson, David. "Implementing Streaming Parallel Decision Trees on Graphic Processing Units." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230953.

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Decision trees have long been a prevalent area within machine learning. With streaming data environments as well as large datasets becoming increasingly common, researchers have developed decision tree algorithms adapted to streaming data. One such algorithm is SPDT, which approaches the streaming data problem by making use of workers on a network combined with a dynamic histogram approximation of the data. There exist several implementations for decision trees on GPU, but those are uncommon in a streaming data setting. In this research, conducted at RISE SICS, the possibilities of acceleratin
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Books on the topic "Streaming Data Processing for Machine Learning"

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Putatunda, Sayan. Practical Machine Learning for Streaming Data with Python. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6867-4.

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Myles, White John, ed. Machine learning for hackers. O'Reilly Media, 2012.

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Bhattacharjee, Arup, Samir Kr Borgohain, Badal Soni, Gyanendra Verma, and Xiao-Zhi Gao, eds. Machine Learning, Image Processing, Network Security and Data Sciences. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6315-7.

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Bhattacharjee, Arup, Samir Kr Borgohain, Badal Soni, Gyanendra Verma, and Xiao-Zhi Gao, eds. Machine Learning, Image Processing, Network Security and Data Sciences. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6318-8.

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Utgoff, Paul E. Machine learning of inductive bias. Kluwer Academic Publishers, 1986.

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Pathak, Manas A. Privacy-Preserving Machine Learning for Speech Processing. Springer New York, 2013.

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Advances in machine learning and data mining for astronomy. Chapman and Hall/CRC, 2012.

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Rasmussen, Carl Edward. Gaussian processes for machine learning. MIT Press, 2005.

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I, Williams Christopher K., ed. Gaussian processes for machine learning. MIT Press, 2006.

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Autonomous learning from the environment. Computer Science Press, 1994.

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Book chapters on the topic "Streaming Data Processing for Machine Learning"

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Agnihotram, Gopichand, Rajesh Kumar, Pandurang Naik, and Rahul Yadav. "Virtual Conversation with Real-Time Prediction of Body Moments/Gestures on Video Streaming Data." In Machine Learning and Information Processing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1884-3_11.

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Singh, Pramod. "Data Processing." In Machine Learning with PySpark. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4131-8_3.

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Putatunda, Sayan. "Supervised Learning for Streaming Data." In Practical Machine Learning for Streaming Data with Python. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6867-4_3.

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Rengarajan, Krushnaa, and Vijay Krishna Menon. "Generalizing Streaming Pipeline Design for Big Data." In Machine Intelligence and Signal Processing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1366-4_12.

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Žliobaitė, Indrė, Albert Bifet, Bernhard Pfahringer, and Geoff Holmes. "Active Learning with Evolving Streaming Data." In Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23808-6_39.

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Putatunda, Sayan. "An Introduction to Streaming Data." In Practical Machine Learning for Streaming Data with Python. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6867-4_1.

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Singh, Himanshu. "Data Processing in AWS." In Practical Machine Learning with AWS. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6222-1_5.

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Kang, Myeongsu, and Jing Tian. "Machine Learning: Data Pre-processing." In Prognostics and Health Management of Electronics. John Wiley and Sons Ltd, 2018. http://dx.doi.org/10.1002/9781119515326.ch5.

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Sarkar, Dipanjan, Raghav Bali, and Tushar Sharma. "Processing, Wrangling, and Visualizing Data." In Practical Machine Learning with Python. Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-3207-1_3.

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Suryadevara, Nagender. "Browser-Based Data Processing." In Beginning Machine Learning in the Browser. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6843-8_2.

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Conference papers on the topic "Streaming Data Processing for Machine Learning"

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Wu, Tong. "Online Tensor Low-Rank Representation for Streaming Data." In 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2020. http://dx.doi.org/10.1109/mlsp49062.2020.9231620.

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Naufal, Ahmad Naufal, Samy Abdelhamid Samy, Nenisurya Hashim Nenisurya, et al. "Machine Learning as Accelerating Tool in Remote Operation Realisation through Monitoring Oil and Gas Equipments and Identifying its Failure Mode." In International Petroleum Technology Conference. IPTC, 2021. http://dx.doi.org/10.2523/iptc-21493-ms.

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Abstract Equipment failure, unplanned downtime operation, and environmental damage cost represent critical challenges in overall oil and gas business from well reservoir identification and drilling strategy to production and processing. Identifying and managing the risks around assets that could fail and cause redundant and expensive downtime are the core of plant reliability in oil and gas industry. In the current digital era; there is an essential need of innovative data-driven solutions to address these challenges, especially, monitoring and diagnosis of plant equipment operations, recogniz
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Yu, Wenjian, Yu Gu, Jian Li, Shenghua Liu, and Yaohang Li. "Single-Pass PCA of Large High-Dimensional Data." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/468.

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Principal component analysis (PCA) is a fundamental dimension reduction tool in statistics and machine learning. For large and high-dimensional data, computing the PCA (i.e., the top singular vectors of the data matrix) becomes a challenging task. In this work, a single-pass randomized algorithm is proposed to compute PCA with only one pass over the data. It is suitable for processing extremely large and high-dimensional data stored in slow memory (hard disk) or the data generated in a streaming fashion. Experiments with synthetic and real data validate the algorithm's accuracy, which has orde
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Abeykoon, Vibhatha, Supun Kamburugamuve, Kannan Govindrarajan, et al. "Streaming Machine Learning Algorithms with Big Data Systems." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006337.

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Lima, Wesllen Sousa, and Eduardo J. P. Souto. "Reconhecimento de atividades humanas baseado na análise de fluxo contínuo de dados simbólicos." In Simpósio Brasileiro de Sistemas Multimídia e Web. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/webmedia_estendido.2020.13054.

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Smartphones sensing capabilities have enabled the development of Human Activity Recognition (HAR) solutions for better understanding human behavior through computational techniques. However, these solutions have been difficult to perform in dynamic scenarios because they do not observe data evolution over time and the high consumption of computational resources, such as memory, processing and energy. This occurs because the HAR problem for smartphones has been solved through classification models generated by offline machine learning algorithms that, in this case, are limited by a data history
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Theodossiou, S., and N. Singh Rainu. "Digital Initiatives, Infrastructures and Data Ecosystems in the Maritime Sector." In International Conference on Marine Engineering and Technology Oman. IMarEST, 2019. http://dx.doi.org/10.24868/icmet.oman.2019.017.

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Efficiency, performance and monitoring of vessels becomes of paramount importance around the globe. Assets security, vessels efficiency, new directives and legislation with regard to emissions quality and many others, urge the global maritime industry to take the right initiatives and make the appropriate investments to develop data ecosystems, that over time, if used intelligently, coherently and consistently, will allow owners and managers to reap tangible benefits such as, among others, significant cost savings, better vessel management and longer vessel life span. As of today, most shipown
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Herodotou, Herodotos, Despoina Chatzakou, and Nicolas Kourtellis. "A Streaming Machine Learning Framework for Online Aggression Detection on Twitter." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377980.

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Warren, Peter, Hessein Ali, Hossein Ebrahimi, and Ranajay Ghosh. "Rapid Defect Detection and Classification in Images Using Convolutional Neural Networks." In ASME Turbo Expo 2021: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/gt2021-59801.

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Abstract Several image processing methods have been implemented over recent years to assist and partially replace on-site technician visual inspection of both manufactured parts and operational equipments. Convolutional neural networks (CNNs) have seen great success in their ability to both identify and classify anomalies within images, in some cases they do this to a higher degree of accuracy than an expert human. Several parts that are manufactured for various aspects of turbomachinery operation must undergo a visual inspection prior to qualification. Machine learning techniques can streamli
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Wu, Haibo, Shiliang Shi, and Qifeng Nian. "Streaming Machine Learning For Real-Time Gas Concentration Prediction." In 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). IEEE, 2019. http://dx.doi.org/10.1109/bigdatasecurity-hpsc-ids.2019.00019.

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Khade, Rohan, Jessica Lin, and Nital Patel. "Frequent Set Mining for Streaming Mixed and Large Data." In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2015. http://dx.doi.org/10.1109/icmla.2015.218.

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Reports on the topic "Streaming Data Processing for Machine Learning"

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Davis, Benjamin. Applying Machine Learning to the Classification of DC-DC Converters: Real-world data collection processing & Validation. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1670255.

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Salter, R., Quyen Dong, Cody Coleman, et al. Data Lake Ecosystem Workflow. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40203.

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The Engineer Research and Development Center, Information Technology Laboratory’s (ERDC-ITL’s) Big Data Analytics team specializes in the analysis of large-scale datasets with capabilities across four research areas that require vast amounts of data to inform and drive analysis: large-scale data governance, deep learning and machine learning, natural language processing, and automated data labeling. Unfortunately, data transfer between government organizations is a complex and time-consuming process requiring coordination of multiple parties across multiple offices and organizations. Past succ
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