1,720,954 research outputs found
Flink-ML: machine learning in Apache Flink
Get PDFThe emergence of Big Data has spurred the development of various frameworks designed for efficient data storage and processing. Key frameworks include Hadoop, Spark, Flink, Storm, Pig, and Zookeeper. Among these, Apache Flink stands out as a prominent open-source platform known for its powerful stream and batch processing capabilities. It functions as a versatile engine for large-scale processing, incorporating built-in modules for streaming, SQL, machine learning (ML), and visualization tasks.This paper introduces Flink-ML, Flink’s open-source distributed machine learning library, which has been added to the Flink ecosystem in response to the exponential growth of machine learning applications in recent years. Flink-ML addresses the increasing demand for scalable machine learning solutions by offering efficient implementations of a variety of algorithms. As the community around Flink continues to grow, so too does the number of contributors and available algorithms within Flink-ML.Flink-ML is designed to support multiple programming languages and provides a high-level API that leverages Flink’s rich ecosystem. This integration simplifies the development of end-to-end machine learning pipelines, allowing developers to efficiently build and deploy models. Overall, Flink-ML enhances the capabilities of the Flink framework, making it an ideal choice for organizations looking to harness the power of machine learning within their Big Data projects
Machine learning in big data: A performance benchmarking study of Flink-ML and Spark MLlib
Get PDFMachine learning (ML) in big data frameworks plays a critical role in real-time analytics, decision making, and predictive modeling. Among the most prominent ML libraries for large-scale data processing are Flink-ML, the machine learning extension of Apache Flink, and MLlib, the machine learning library of Apache Spark. This paper provides a comparative analysis of these two frameworks, evaluating their performance, scalability, streaming capabilities, iterative computation efficiency, and ease of integration with external deep learning frameworks. Flink-ML is designed for real-time, event-driven ML applications and provides native support for streaming-based model training and inference. In contrast, Spark MLlib is optimized for batch processing and micro-batch streaming, making it more suitable for traditional machine learning workflows. Experimental results show that training time is nearly identical for both frameworks, with Spark MLlib requiring 4006.4 seconds and Flink-ML 4003.2 seconds, demonstrating comparable efficiency in batch training and streaming-based model updates. Accuracy results show that Flink-ML (74.9%) slightly outperforms Spark MLlib (74.7%), suggesting that continuous learning in Flink-ML may contribute to better generalization. Inference throughput is slightly higher for Spark MLlib (8.4 images/sec) compared to Flink-ML (8.2 images/sec), suggesting that Spark's batch execution provides a slight advantage in processing efficiency. Both frameworks consume the same amount of memory (30.2%), confirming that TensorFlow's deep learning operations dominate resource consumption rather than architectural differences between Spark and Flink. These results highlight the tradeoffs between Flink-ML and Spark MLlib, and guide data scientists and engineers in selecting the appropriate framework based on specific ML workflow requirements and scalability considerations
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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