Streaming Data
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Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 113
Rafał Zdunek ◽  
Krzysztof Fonał

Nonnegative Tucker decomposition (NTD) is a robust method used for nonnegative multilinear feature extraction from nonnegative multi-way arrays. The standard version of NTD assumes that all of the observed data are accessible for batch processing. However, the data in many real-world applications are not static or are represented by a large number of multi-way samples that cannot be processing in one batch. To tackle this problem, a dynamic approach to NTD can be explored. In this study, we extend the standard model of NTD to an incremental or online version, assuming volatility of observed multi-way data along one mode. We propose two computational approaches for updating the factors in the incremental model: one is based on the recursive update model, and the other uses the concept of the block Kaczmarz method that belongs to coordinate descent methods. The experimental results performed on various datasets and streaming data demonstrate high efficiently of both algorithmic approaches, with respect to the baseline NTD methods.

2022 ◽  
pp. 1-1
Ze Deng ◽  
Ze Deng ◽  
Yue Wang ◽  
Tao Liu ◽  
Schahram Dustdar ◽  

2021 ◽  
Vol 5 (4) ◽  
pp. 456
Shaimaa Safaa Ahmed Alwaisi ◽  
Maan Nawaf Abbood ◽  
Luma Fayeq Jalil ◽  
Shahreen Kasim ◽  
Mohd Farhan Mohd Fudzee ◽  

The amount of data in our world has been rapidly keep growing from time to time.  In the era of big data, the efficient processing and analysis of big data using machine learning algorithm is highly required, especially when the data comes in form of streams. There is no doubt that big data has become an important source of information and knowledge in making decision process. Nevertheless, dealing with this kind of data comes with great difficulties; thus, several techniques have been used in analyzing the data in the form of streams. Many techniques have been proposed and studied to handle big data and give decisions based on off-line batch analysis. Today, we need to make a constructive decision based on online streaming data analysis. Many researchers in recent years proposed some different kind of frameworks for processing the big data streaming. In this work, we explore and present in detail some of the recent achievements in big data streaming in term of contributions, benefits, and limitations. As well as some of recent platforms suitable to be used for big data streaming analytics. Moreover, we also highlight several issues that will be faced in big data stream processing. In conclusion, it is hoped that this study will assist the researchers in choosing the best and suitable framework for big data streaming projects.

2021 ◽  

Abstract The growth of technology evaluation and the influence of smart gazettes, which have a very complex structure, the amount of data in an organization, E-Commerce, and ERP explodes. When data is processed as described, it becomes the engine of every individual. According to projections from 2025, social media, IoT, streaming data, and geodata will generate 80% of unstructured data, and there will be 4.8 billion tech enthusiasts. The most popular social media trend allows users to access publicly available data. Hackers are highly qualified in both the web space and the dark web, and the rise of complexity and digitization of this public access will cause loopholes in legislation. The major goal of this study is to gather information about the cyber vulnerability of electronic news. Data collection, text standardization, and feature extraction were all part of the initial step. In the second step, MapReduce was used to obtain demographic insights using a multi-layered categorization strategy. Cybercrime is classified using a classifier technique, and the model has a 53 percent accuracy rate. Phishing is a result of cyber weaknesses, and it has been discovered in a higher number of metropolitan cities. Men, rather than women, make up the majority of crime victims. Individuals should be made aware of secure access to websites and media, according to the findings of the study. People should be aware of cyber vulnerabilities, as well as cyber laws enacted under the IPC, the IT Act 2000, and CERT-In.

2021 ◽  
Vol 11 (24) ◽  
pp. 12073
Michael Heigl ◽  
Enrico Weigelt ◽  
Dalibor Fiala ◽  
Martin Schramm

Over the past couple of years, machine learning methods—especially the outlier detection ones—have anchored in the cybersecurity field to detect network-based anomalies rooted in novel attack patterns. However, the ubiquity of massive continuously generated data streams poses an enormous challenge to efficient detection schemes and demands fast, memory-constrained online algorithms that are capable to deal with concept drifts. Feature selection plays an important role when it comes to improve outlier detection in terms of identifying noisy data that contain irrelevant or redundant features. State-of-the-art work either focuses on unsupervised feature selection for data streams or (offline) outlier detection. Substantial requirements to combine both fields are derived and compared with existing approaches. The comprehensive review reveals a research gap in unsupervised feature selection for the improvement of outlier detection methods in data streams. Thus, a novel algorithm for Unsupervised Feature Selection for Streaming Outlier Detection, denoted as UFSSOD, will be proposed, which is able to perform unsupervised feature selection for the purpose of outlier detection on streaming data. Furthermore, it is able to determine the amount of top-performing features by clustering their score values. A generic concept that shows two application scenarios of UFSSOD in conjunction with off-the-shell online outlier detection algorithms has been derived. Extensive experiments have shown that a promising feature selection mechanism for streaming data is not applicable in the field of outlier detection. Moreover, UFSSOD, as an online capable algorithm, yields comparable results to a state-of-the-art offline method trimmed for outlier detection.

2021 ◽  
Sharmin Afrose ◽  
Danfeng Daphne Yao ◽  
Olivera Kotevska

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