Relevant bibliographies by topics / Streaming Data Analysis

Contents

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

Academic literature on the topic 'Streaming Data Analysis'

Author: Grafiati
Published: 10 December 2022
Last updated: 31 July 2025

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Journal articles on the topic "Streaming Data Analysis"

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Chowdhury, Sugnik Roy. "Twitter Data Analysis by Live Streaming." Journal of Computational and Theoretical Nanoscience 16, no. 8 (2019): 3178–82. http://dx.doi.org/10.1166/jctn.2019.8156.

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Streaming now a days have been of great use when comes to Social Media. Streaming of data have made it easy for Companies to understand the pros and cons of their product. Streaming acts as a survey now a days which a few years ago were done by a team of individual using pen and papers. In order to collect and process the streaming data from various streaming sites to produce an analytical report that helps to get a clear pictorial representation of events, the assets of streaming process generates a huge volume of real time data mainly referred to as “Big Data.” In order to aggregate, store a
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Ibrahim, Omar A., Yiqing Wang, and James M. Keller. "Analysis of Incremental Cluster Validity for Big Data Applications." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26, Suppl. 2 (2018): 47–62. http://dx.doi.org/10.1142/s0218488518400111.

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Online clustering has attracted attention due to the explosion of ubiquitous continuous sensing. Streaming clustering algorithms need to look for new structures and adapt as the data evolves, such that outliers are detected, and that new emerging clusters are automatically formed. The performance of a streaming clustering algorithm needs to be monitored over time to understand the behavior of the streaming data in terms of new emerging clusters and number of outlier data points. Small datasets with 2 or 3 dimensions can be monitored by plotting the clustering results as data evolves. However,
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Ambika, L. G. "Analysis of Twitter Streaming Data and Historical Data." International Journal for Research in Engineering Application & Management (IJREAM) 08, no. 10 (2023): 18–22. https://doi.org/10.5281/zenodo.7704092.

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Twitter can be used to share useful information. Keeping track of user postings and commonly used hashtags can also help you figure out what topics are getting the most attention on Twitter. It can also assist in discerning what issues are being discussed the most on Twitter, allowing users to make rapid and informed judgments based on current events. Analyzing Twitter trends allow us to learn more about what people are interested in, which aids corporate organisations or brands in increasing sales, political parties in better understanding people’s feelings and wants, and movie studios
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Bagui, Sikha, and Katie Jin. "A Survey of Challenges Facing Streaming Data." Transactions on Machine Learning and Artificial Intelligence 8, no. 4 (2020): 63–73. http://dx.doi.org/10.14738/tmlai.84.8579.

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This survey performs a thorough enumeration and analysis of existing methods for data stream processing. It is a survey of the challenges facing streaming data. The challenges addressed are preprocessing of streaming data, detection and dealing with concept drifts in streaming data, data reduction in the face of data streams, approximate queries and blocking operations in streaming data.
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Kim, Kyeongjoo. "Real-time Streaming Data Analysis using Spark." International Journal of Emerging Trends in Engineering Research 6, no. 1 (2018): 1–5. http://dx.doi.org/10.30534/ijeter/2018/01612018.

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Guo, Shu-Hui, and Xin Lu. "Live streaming: Data mining and behavior analysis." Acta Physica Sinica 69, no. 8 (2020): 088908. http://dx.doi.org/10.7498/aps.69.20191776.

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Matteussi, Kassiano J., Julio C. S. dos Anjos, Valderi R. Q. Leithardt, and Claudio F. R. Geyer. "Performance Evaluation Analysis of Spark Streaming Backpressure for Data-Intensive Pipelines." Sensors 22, no. 13 (2022): 4756. http://dx.doi.org/10.3390/s22134756.

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A significant rise in the adoption of streaming applications has changed the decision-making processes in the last decade. This movement has led to the emergence of several Big Data technologies for in-memory processing, such as the systems Apache Storm, Spark, Heron, Samza, Flink, and others. Spark Streaming, a widespread open-source implementation, processes data-intensive applications that often require large amounts of memory. However, Spark Unified Memory Manager cannot properly manage sudden or intensive data surges and their related in-memory caching needs, resulting in performance and
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Narendra Reddy Sanikommu. "Real-time stream processing engines: Architectural analysis and implementation considerations." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 3006–16. https://doi.org/10.30574/wjarr.2025.26.2.1916.

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This article provides an in-depth architectural analysis of three leading stream processing engines: Apache Spark Streaming, Apache Flink, and Kafka Streams. As organizations increasingly rely on real-time data processing capabilities to drive decision-making, understanding the fundamental architectural differences between these technologies has become crucial for successful implementation. The analysis explores how Spark Streaming's micro-batch approach prioritizes throughput and integration with the Spark ecosystem, while Flink's true streaming design enables minimal latency and sophisticate
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Yuan, Zhehu, Yinqi Sun, and Dennis Shasha. "Forgetful Forests: Data Structures for Machine Learning on Streaming Data under Concept Drift." Algorithms 16, no. 6 (2023): 278. http://dx.doi.org/10.3390/a16060278.

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Database and data structure research can improve machine learning performance in many ways. One way is to design better algorithms on data structures. This paper combines the use of incremental computation as well as sequential and probabilistic filtering to enable “forgetful” tree-based learning algorithms to cope with streaming data that suffers from concept drift. (Concept drift occurs when the functional mapping from input to classification changes over time). The forgetful algorithms described in this paper achieve high performance while maintaining high quality predictions on streaming d
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López-Lagunas, Abelardo, and Sek Chai. "Streaming Data Movement for Real-Time Image Analysis." Journal of Signal Processing Systems 62, no. 1 (2009): 29–42. http://dx.doi.org/10.1007/s11265-008-0336-x.

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Dissertations / Theses on the topic "Streaming Data Analysis"

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Anagnostopoulos, Christoforos. "A Statistical Framework for Streaming Data Analysis." Thesis, Imperial College London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.520838.

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Patni, Harshal Kamlesh. "Real Time Semantic Analysis of Streaming Sensor Data." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1324181415.

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Fairbanks, James Paul. "Graph analysis combining numerical, statistical, and streaming techniques." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/54972.

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Graph analysis uses graph data collected on a physical, biological, or social phenomena to shed light on the underlying dynamics and behavior of the agents in that system. Many fields contribute to this topic including graph theory, algorithms, statistics, machine learning, and linear algebra. This dissertation advances a novel framework for dynamic graph analysis that combines numerical, statistical, and streaming algorithms to provide deep understanding into evolving networks. For example, one can be interested in the changing influence structure over time. These disparate techniques each co
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Menglei, Min. "Anomaly detection based on multiple streaming sensor data." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-36275.

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Today, the Internet of Things is widely used in various fields, such as factories, public facilities, and even homes. The use of the Internet of Things involves a large number of sensor devices that collect various types of data in real time, such as machine voltage, current, and temperature. These devices will generate a large amount of streaming sensor data. These data can be used to make the data analysis, which can discover hidden relation such as monitoring operating status of a machine, detecting anomalies and alerting the company in time to avoid significant losses. Therefore, the appli
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Giannini, Andrea. "Social Network Analysis: Architettura Streaming Big Data di Raccolta e Analisi Dati da Twitter." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25378/.

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Negli ultimi anni i social media, come ad esempio Facebook, Twitter, WhatsApp, YouTube, si sono diffusi a macchia d'olio. Ormai quasi tutti accedono giornalmente su almeno uno di questi per informarsi, esprimere opinioni e interagire con altri utenti. Per questa ragione sono diventati fondamentali per i reparti marketing delle aziende essendo non solo un ottimo canale di comunicazione, ma anche una fonte di informazioni sui clienti e potenziali tali. La tesi si focalizza proprio su quest'ultimo aspetto. Il progetto Social Network Analysis (SNA) vuole essere infatti uno strumento attravers
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Kühn, Eileen [Verfasser], and A. [Akademischer Betreuer] Streit. "Online Analysis of Dynamic Streaming Data / Eileen Kühn ; Betreuer: A. Streit." Karlsruhe : KIT-Bibliothek, 2018. http://d-nb.info/1161008721/34.

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Moitra, Anindya. "Computation and Application of Persistent Homology on Streaming Data." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613686214764863.

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Zubeir, Abdulghani Ismail. "OAP: An efficient online principal component analysis algorithm for streaming EEG data." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-392403.

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Data processing on streaming data poses computational as well as statistical challenges. Streaming data requires that data processing algorithms are able to process a new data point within micro-seconds. This is especially challenging on dimension reduction, where traditional methods as Principal Component Analysis (PCA) require eigenvectors decomposition of a matrix based on the complete dataset. So a proper online version of PCA should avoid this computational involved step in favor for a more efficient update rule. This is implemented by an algorithm named Online Angle Preservation (OAP), w
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Vigraham, Sushrutha. "Design and Analysis of a Real-time Data Monitoring Prototype for the LWA Radio Telescope." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/31306.

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Increasing computing power has been helping researchers understand many complex scientific problems. Scientific computing helps to model and visualize complex processes such as molecular modelling, medical imaging, astrophysics and space exploration by processing large set of data streams collected through sensors or cameras. This produces a massive amount of data which consume a large amount of processing and storage resources. Monitoring the data streams and filtering unwanted information will enable efficient use of the available resources. This thesis proposes a data-centric system that ca
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Landford, Jordan. "Event Detection Using Correlation within Arrays of Streaming PMU Data." PDXScholar, 2016. http://pdxscholar.library.pdx.edu/open_access_etds/3031.

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This thesis provides a synchrophasor data analysis methodology that leverages both statistical correlation techniques and a statistical distribution in order to identify data inconsistencies, as well as power system contingencies. This research utilizes archived Phasor Measurement Unit (PMU) data obtained from the Bonneville Power Administration in order to show that this methodology is not only feasible, but extremely useful for power systems monitoring, decision support, and planning purposes. By analyzing positive sequence voltage angles between a pair of PMUs at two different substation lo
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Books on the topic "Streaming Data Analysis"

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

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Shumilina, Vera, Galina Krohicheva, Nataliya Izvarina, et al. Application of accounting, analysis and audit in enterprise management. AUS PUBLISHERS, 2021. http://dx.doi.org/10.26526/monography_618ba6f2989171.05397055.

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It is impractical to plan the further work of the company without taking into account analytical data for previous production periods. Analytical accounting data allows managers to build a company's strategy or make changes to an existing development plan. The importance of accounting for the enterprise plays a large role at the planning stage of the further business strategy. A competent manager studies accounting data before making the next decision regarding the company's finances. The easiest way to streamline accounting documents and eliminate errors in it is to conduct an internal audit.
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Books, Worth. Summary and Analysis of Streaming, Sharing, Stealing : Big Data and the Future of Entertainment: Based on the Book by Michael D. Smith and Rahul Telang. Worth Books, 2017.

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Gamble, Steven. Digital Flows. Oxford University PressNew York, 2024. http://dx.doi.org/10.1093/oso/9780197656389.001.0001.

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Abstract Digital Flows provides a radical study of the most recent chapter in the life of hip hop, one closely intertwined with the networked cultural flows of the internet. Some fifty years after its birth in the Bronx, hip hop is one of the most significant cultural forms of the internet age. Now that the internet is enmeshed in our everyday lives, hip hop is predominantly encountered and experienced online, where it comprises a third of all streamed music. People are constantly making, sharing, and commenting on hip hop—from Drake memes through viral TikTok dances to AI-generated rappers—ch
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Berglund, Karl. Reading Audio Readers. Bloomsbury Publishing Plc, 2024. http://dx.doi.org/10.5040/9781350358393.

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The first computational study of reading to focus on audiobooks, this book uses a unique and substantial set of reader consumption data to show how audiobooks and digital streaming platforms affect our literary culture. Offering an academic perspective on the kind of user data hoard we associate with tech companies, it asks: when it comes to audiobooks, what do people really read, and how and when do they read it? Tracking hundreds of thousands of readers on the level per user and hour, Reading Audio Readers combines computational methods from cultural analytics with theoretical perspectives f
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Jaeckle, Jeff, and Susan Ryan, eds. ReFocus: The Films of Barbara Kopple. Edinburgh University Press, 2019. http://dx.doi.org/10.3366/edinburgh/9781474439947.001.0001.

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Building on existing interviews, journal articles, and archival research, The Films of Barbara Kopple assesses Kopple’s entire career to date, paying particular attention historical contexts, technique, critical reception, and ongoing influence. Each chapter blends close analyses of the films with insights drawn from film history and documentary studies to demonstrate that Kopple has consistently and often doggedly pursued projects that document the experiences of the victimized, the voiceless, and those in crisis. The contributors treat the entire scope of Kopple’s career, from her work in th
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Book chapters on the topic "Streaming Data Analysis"

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Loia, Vincenzo, Francesco Orciuoli, and Angelo Gaeta. "Data Streaming Scenarios." In Computational Techniques for Intelligence Analysis. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20851-5_10.

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Cormode, Graham. "Streaming Methods in Data Analysis." In Data Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20424-6_1.

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Bezdek, James C. "Structural Assessment in Streaming Data." In Elementary Cluster Analysis. River Publishers, 2022. http://dx.doi.org/10.1201/9781003338086-14.

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Daruki, Samira, Justin Thaler, and Suresh Venkatasubramanian. "Streaming Verification in Data Analysis." In Algorithms and Computation. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48971-0_60.

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Chinthala, Santhi, Ramesh Mande, Suneetha Manne, and Sindhura Vemuri. "Sentiment Analysis on Twitter Streaming Data." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13728-5_18.

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Shete, Rushikesh M., and Virendra K. Sharma. "Adaptive and Efficient Streaming Data Analysis." In Recent Advances in Sciences, Engineering, Information Technology & Management. CRC Press, 2024. https://doi.org/10.1201/9781003598152-129.

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Sarmento, Rui, Márcia Oliveira, Mário Cordeiro, Shazia Tabassum, and João Gama. "Social Network Analysis in Streaming Call Graphs." In Studies in Big Data. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26989-4_10.

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Zhang, Xudong, Zhongwen Qian, Siqi Shen, Jia Shi, and Shujun Wang. "Streaming Massive Electric Power Data Analysis Based on Spark Streaming." In Database Systems for Advanced Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18590-9_14.

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Martins, Inês, João S. Resende, and João Gama. "Online Influence Forest for Streaming Anomaly Detection." In Advances in Intelligent Data Analysis XXI. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30047-9_22.

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Chen, Jianzong, Hanlu Li, Qing Xie, Lin Li, and Yongjian Liu. "Streaming Recommendation Algorithm with User Interest Drift Analysis." In Web and Big Data. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26075-0_10.

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Conference papers on the topic "Streaming Data Analysis"

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Jain, Ankush, Charles D. Cranor, Qing Zheng, Bradley W. Settlemyer, George Amvrosiadis, and Gary A. Grider. "CARP: Range Query-Optimized Indexing for Streaming Data." In SC24: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 2024. https://doi.org/10.1109/sc41406.2024.00093.

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Arora, Rahul, Manan Agarwal, Vinoth N. A. S, Uma Maheswari KM, and Brindha R. "MoodLens: A Streaming Framework for Facial Sentimental Analysis." In 2025 International Conference on Data Science and Business Systems (ICDSBS). IEEE, 2025. https://doi.org/10.1109/icdsbs63635.2025.11031829.

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Sharma, Tarunika, Anitha Gatla, Sorabh Lakhanpal, Gulshan Dhasmana, Yaragudipati Sri Lalitha, and Sajjad Ziara. "Enhancing Space Exploration with Real-Time Streaming Data Analysis." In 2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI). IEEE, 2024. https://doi.org/10.1109/icscai61790.2024.10866590.

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Luo, Xiaopeng, Yiqun Zhang, Yuzhu Ji, Peng Liu, and Taoting Xiao. "Efficient Topology-Driven Clustering for Imbalanced Streaming Biomedical Data Analysis." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822727.

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Varsha, N., and M. C Padma. "An Empirical Analysis of Drift Detection in User Based Data Streaming Using Opinion Mining." In 2024 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10691100.

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Fu, Yiwen, and Wanmin Ni. "Big Data analysis of e-commerce Live Streaming Mode based on Alibaba International Station*." In 2024 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI). IEEE, 2024. https://doi.org/10.1109/iiki65561.2024.00030.

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Madhuri, Rachakonda, Prashant Bachanna, Begari Pavan, and Mohammed Zameera Begum. "Design and Performance analysis of Asynchronous Network on Chip for Streaming data Transmission on FPGA." In 2024 International BIT Conference (BITCON). IEEE, 2024. https://doi.org/10.1109/bitcon63716.2024.10985385.

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Ferdman, Michael, and Babak Falsafi. "Last-Touch Correlated Data Streaming." In 2007 IEEE International Symposium on Performance Analysis of Systems & Software. IEEE, 2007. http://dx.doi.org/10.1109/ispass.2007.363741.

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Balduini, Marco, Sivam Pasupathipillai, and Emanuele Delia Valle. "Cost-Aware Streaming Data Analysis." In DEBS '18: The 12th ACM International Conference on Distributed and Event-based Systems. ACM, 2018. http://dx.doi.org/10.1145/3210284.3210294.

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Kang, Sungjoo, Kyung I. Ku, Sung Jin Hur, and Wan Choi. "Analysis of Software Streaming Data." In The 9th International Conference on Advanced Communication Technology. IEEE, 2007. http://dx.doi.org/10.1109/icact.2007.358576.

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Reports on the topic "Streaming Data Analysis"

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Li, Justin, Michael Eydenberg, Kevin Yarritu, Mayuri Shakamuri, and James Bridges. Streaming Analytics for Anomaly Detection in Large-Scale Data. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2462941.

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Sadot, Einat, Christopher Staiger, and Mohamad Abu-Abied. Studies of Novel Cytoskeletal Regulatory Proteins that are Involved in Abiotic Stress Signaling. United States Department of Agriculture, 2011. http://dx.doi.org/10.32747/2011.7592652.bard.

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In the original proposal we planned to focus on two proteins related to the actin cytoskeleton: TCH2, a touch-induced calmodulin-like protein which was found by us to interact with the IQ domain of myosin VIII, ATM1; and ERD10, a dehydrin which was found to associate with actin filaments. As reported previously, no other dehydrins were found to interact with actin filaments. In addition so far we were unsuccessful in confirming the interaction of TCH2 with myosin VIII using other methods. In addition, no other myosin light chain candidates were found in a yeast two hybrid survey. Nevertheless
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Varga, Tamas, Sean Colby, Swarup China, and Anil Krishna Battu. Machine learning approaches to streamline and enhance the analysis of multiscale imaging data for bioaerosol and soil particles. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1987875.

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Campbell, Heidi A., Jennifer Daly, Sophia A. Osteen, and Andrea Wallace. Mission to Serve Tech: Churches "Lock Down" Technology During The Global Pandemic. The Network For New Media, Religion & Digital Culture Studies, 2023. http://dx.doi.org/10.21423/oak/1969.1/197075.

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This report analyzes data collected from 2700 churches of the Center for Congregations in Indianapolis through the Connect Through Tech grant program between 2020-2021. This report analyzes data gathered from applications to the CTT grant and final reports submitted by these churches, in order to learn how churches understand and perceive the role of churches, technology, and their relationship changed during the first two years of the pandemic as they incorporated digital media into their ministry work. The “Mission to Serve Tech” report explores three key themes: (1) how leaders understandin
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Corkum, Eleanor, Tiffanie Perrault, and Erin C. Strumpf. Improving Breast Cancer Diagnosis Pathways in Quebec. CIRANO, 2023. http://dx.doi.org/10.54932/qsho2261.

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Delays in breast cancer diagnosis can worsen the severity of illness and reinforce inequalities. This report analyzes Quebec’s capabilities and performance along the diagnosis pathway, gathering information from the scientific literature on cancer care, government reports, and expert interviews. The first section outlines which types of breast cancer data Quebec collects, and how data availability impacts the measurement of performance indicators. The second section discusses how socio-economic factors and unclear guidelines for patients outside Quebec’s organized screening program create barr
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Visser, R., H. Kao, R. M. H. Dokht, A. B. Mahani, and S. Venables. A comprehensive earthquake catalogue for northeastern British Columbia: the northern Montney trend from 2017 to 2020 and the Kiskatinaw Seismic Monitoring and Mitigation Area from 2019 to 2020. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329078.

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To increase our understanding of induced seismicity, we develop and implement methods to enhance seismic monitoring capabilities in northeastern British Columbia (NE BC). We deploy two different machine learning models to identify earthquake phases using waveform data from regional seismic stations and utilize an earthquake database management system to streamline the construction and maintenance of an up-to-date earthquake catalogue. The completion of this study allows for a comprehensive catalogue in NE BC from 2014 to 2020 by building upon our previous 2014-2016 and 2017-2018 catalogues. Th
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Veung, Naron. Enhancing the Collaboration between TVET Institutions and the Private Sector in Cambodia: Implications for Skills Provision. Cambodia Development Resource Institute, 2024. https://doi.org/10.64202/cdri.

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Collaboration with the private sector has been an influential tool in addressing a variety of complex issues or problems in both developed and developing economies around the globe. Like many countries, Cambodia has strongly promoted collaboration with the private sector across all education sectors. In the Cambodian TVET sector, the Royal Government of Cambodia has emphasised public-private partnerships (PPP) in its national TVET Policy 2017-2025 and Skills Development Roadmap 2023-2035. Such collaboration can make TVET more industry-relevant by increasing the private sector’s role, duties, a
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Naron, Veung. Enhancing the Collaboration between TVET Institutions and the Private Sector in Cambodia: Implications for Skills Provision. Cambodia Development Resource Institute, 2024. https://doi.org/10.64202/wp-150.

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Collaboration with the private sector has been an influential tool in addressing a variety of complex issues or problems in both developed and developing economies around the globe. Like many countries, Cambodia has strongly promoted collaboration with the private sector across all education sectors. In the Cambodian TVET sector, the Royal Government of Cambodia has emphasised public-private partnerships (PPP) in its national TVET Policy 2017-2025 and Skills Development Roadmap 2023-2035. Such collaboration can make TVET more industry-relevant by increasing the private sector’s role, duties, a
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Veung, Naron. Enhancing the Collaboration between TVET Institutions and the Private Sector in Cambodia: Implications for Skills Provision. Cambodia Development Resource Institute, 2024. https://doi.org/10.64202/wp.150.202411.

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Collaboration with the private sector has been an influential tool in addressing a variety of complex issues or problems in both developed and developing economies around the globe. Like many countries, Cambodia has strongly promoted collaboration with the private sector across all education sectors. In the Cambodian TVET sector, the Royal Government of Cambodia has emphasised public-private partnerships (PPP) in its national TVET Policy 2017-2025 and Skills Development Roadmap 2023-2035. Such collaboration can make TVET more industry-relevant by increasing the private sector’s role, duties, a
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