Streaming Machine Learning Algorithms with Big Data Systems

Abeykoon, Vibhatha; Laszewski, Gregor von; Kamburugamuve, Supun; Govindrarajan, Kannan; Wickramasinghe, Pulasthi; Widanage, Chathura; Perera, Niranda; Uyar, Ahmet; Gunduz, Gurhan; Akkas, Selahattin

doi:10.1109/bigdata47090.2019.9006337

Cited by 9 publications

(9 citation statements)

References 8 publications

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“…Frameworks like PyTorch (Paszke et al, 2019) adopted this HPC philosophy, and distributed runtimes like Horovod (Sergeev and Del Balso, 2018) generalized this practice for most of the existing deep learning frameworks. They were adopting this philosophy along the same time HPC-driven big data systems like Twister2 (Fox, 2017;Abeykoon et al, 2019;Wickramasinghe et al, 2019) were created to bridge the gap between data engineering and deep learning. But with the language boundaries of Java (Ekanayake et al, 2016) and usability with native-C++ based Python implementations were favoured over JVM-based systems.…”

Section: Related Workmentioning

confidence: 99%

HPTMT Parallel Operators for High Performance Data Science and Data Engineering

et al. 2022

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Data-intensive applications are becoming commonplace in all science disciplines. They are comprised of a rich set of sub-domains such as data engineering, deep learning, and machine learning. These applications are built around efficient data abstractions and operators that suit the applications of different domains. Often lack of a clear definition of data structures and operators in the field has led to other implementations that do not work well together. The HPTMT architecture that we proposed recently, identifies a set of data structures, operators, and an execution model for creating rich data applications that links all aspects of data engineering and data science together efficiently. This paper elaborates and illustrates this architecture using an end-to-end application with deep learning and data engineering parts working together. Our analysis show that the proposed system architecture is better suited for high performance computing environments compared to the current big data processing systems. Furthermore our proposed system emphasizes the importance of efficient compact data structures such as Apache Arrow tabular data representation defined for high performance. Thus the system integration we proposed scales a sequential computation to a distributed computation retaining optimum performance along with highly usable application programming interface.

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Section: Related Workmentioning

confidence: 99%

HPTMT Parallel Operators for High Performance Data Science and Data Engineering

et al. 2022

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“…[22][23][24] Various problems related to machine learning and data have been extensively researched. [25][26][27][28] In addition, the number of studies on the processing of text data, which has an important place in the amount of data produced, also shows the importance of text processing. However, we will mainly focus on attention and transformer structures for text processing.…”

Section: Related Workmentioning

confidence: 99%

Text classification using improved bidirectional transformer

Tezgider

Yildiz

Aydın

2021

Concurrency and Computation

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Text data have an important place in our daily life. A huge amount of text data is generated everyday. As a result, automation becomes necessary to handle these large text data. Recently, we are witnessing important developments with the adaptation of new approaches in text processing. Attention mechanisms and transformers are emerging as methods with significant potential for text processing. In this study, we introduced a bidirectional transformer (BiTransformer) constructed using two transformer encoder blocks that utilize bidirectional position encoding to take into account the forward and backward position information of text data. We also created models to evaluate the contribution of attention mechanisms to the classification process. Four models, including long short term memory, attention, transformer, and BiTransformer, were used to conduct experiments on a large Turkish text dataset consisting of 30 categories. The effect of using pretrained embedding on models was also investigated. Experimental results show that the classification models using transformer and attention give promising results compared with classical deep learning models. We observed that the BiTransformer we proposed showed superior performance in text classification.

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“…Twister2 is a promising stream processing framework that presents a high performance and low latency, outperforming Apache Storm and Apache Flink. 24,25 Nevertheless, at the moment, Twiter2 only supports two machine learning algorithms.…”

Section: Related Workmentioning

confidence: 99%

A fast and accurate threat detection and prevention architecture using stream processing

Lobato

Lopez

Cárdenas

et al. 2021

Concurrency and Computation

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Late detection of security breaches increases the risk of irreparable damages and limits any mitigation attempts. We propose a fast and accurate threat detection and prevention architecture that combines the advantages of real-time streaming with batch processing over a historical database. We create a dataset by capturing both legitimate and malicious traffic and propose two ways of combining packets into flows, one considering a time window and the other analyzing the first few packets of each flow per period. We also investigate the effectiveness of our proposal on real-world network traces obtained from a significant Brazilian network operator providing broadband Internet to their customers. We implement and evaluate three classification algorithms and two anomaly detection methods. The results show an accuracy higher than 95% and an excellent trade-off between attack detection and false-positive rates. We further propose an improved scheme based on software defined networks that automatically prevents threats by analyzing only the first few packets of a flow. The proposal promptly and efficiently blocks threats, is robust, and can scale up, even when the attacker employs spoofed IP.

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Streaming Machine Learning Algorithms with Big Data Systems

Cited by 9 publications

References 8 publications

HPTMT Parallel Operators for High Performance Data Science and Data Engineering

HPTMT Parallel Operators for High Performance Data Science and Data Engineering

Text classification using improved bidirectional transformer

A fast and accurate threat detection and prevention architecture using stream processing

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