Time series big data: a survey on data stream frameworks, analysis and algorithms

Almeida, Ana Margarida; Brás, Susana; Sargento, Susana; Pinto, Filipe Cabral

doi:10.1186/s40537-023-00760-1

Cited by 14 publications

(3 citation statements)

References 82 publications

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“…The updated model can be used to predict a label y i for an unlabeled → x i . The primary difference between traditional and online learning is how the data are presented [25]. In the former, the dataset is static and entirely available, whereas data instances are presented sequentially over time in online learning.…”

Section: Incremental Online Learningmentioning

confidence: 99%

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Incremental Online Machine Learning for Detecting Malicious Nodes in Vehicular Communications Using Real-Time Monitoring

Ajjaj,

El Houssaini,

Hain

et al. 2023

Telecom

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Detecting malicious activities in Vehicular Ad hoc Networks (VANETs) is an important research field as it can prevent serious damage within the network and enhance security and privacy. In this regard, a number of approaches based on machine learning (ML) algorithms have been proposed. However, they encounter several challenges due to data being constantly generated over time; this can impact the performance of models trained on fixed datasets as well as cause the need for real-time data analysis to obtain timely responses to potential threats in the network. Therefore, it is crucial for machine learning models to learn and improve their predictions or decisions in real time as new data become available. In this paper, we propose a new approach for attack detection in VANETs based on incremental online machine learning. This approach uses data collected from the monitoring of the VANET nodes’ behavior in real time and trains an online model using incremental online learning algorithms. More specifically, this research addresses the detection of black hole attacks that pose a significant threat to the Ad hoc On Demand Distance Vector (AODV) routing protocol. The data used for attack detection are gathered from simulating realistic VANET scenarios using the well-known simulators Simulation of Urban Mobility (SUMO) and Network Simulator (NS-3). Further, key features which are relevant in capturing the behavior of VANET nodes under black hole attack are monitored over time. The performance of two online incremental classifiers, Adaptive Random Forest (ARF) and K-Nearest Neighbors (KNN), are assessed in terms of Accuracy, Recall, Precision, and F1-score metrics, as well as training and testing time. The results show that ARF can be successfully applied to classify and detect black hole nodes in VANETs. ARF outperformed KNN in all performance measures but required more time to train and test compared to KNN. Our findings indicate that incremental online learning, which enables continuous and real-time learning, can be a potential method for identifying attacks in VANETs.

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Section: Incremental Online Learningmentioning

confidence: 99%

“…There is a need to process a small amount of data at a time to learn from it [25]. This is in contrast to traditional machine learning techniques, which need a lot of computing resources, particularly when training on big datasets [24];…”

mentioning

confidence: 99%

Incremental Online Machine Learning for Detecting Malicious Nodes in Vehicular Communications Using Real-Time Monitoring

Ajjaj,

El Houssaini,

Hain

et al. 2023

Telecom

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“…In recent years, neural networks and artificial intelligence have become increasingly preferred approaches for the predictive modeling of various processes. The potential of neural networks and artificial intelligence cannot be denied, but the decision to use a classical time series model in the current study was made after careful consideration of the factors outlined below [17][18][19]:…”

Section: Introductionmentioning

confidence: 99%

Implementation of a Prediction Model in a Smart System for Enhancing Comfort in Dwellings

Zaharieva,

Georgiev,

Georgiev

et al. 2023

Electronics

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This article introduces a novel approach to ensuring optimal comfort in residential environments, using a smart system powered by predictive modeling. At its core lies a complex algorithm, presented alongside a detailed block diagram, guiding the system’s operations, which are tailored for residential comfort. The primary focus is on the time series analysis of forecasting relative humidity—a critical parameter influencing comfort in living spaces. Among the various prediction models analyzed, a model based on the Fourier equation emerged as the most efficient, accounting for approximately 81% of variances in data. Upon validation, the model showcases an impressive relative error of just ±0.1%. The research underscores the potential of leveraging advanced forecasting in optimizing devices like dehumidifiers or air humidifiers, ensuring the desired comfort while minimizing energy consumption. This innovative integration paves the way for a smarter, more sustainable residential living experience.

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Stream Data Model and Architecture

Anjum,

Yadav,

Yadav

2024

Studies in Big Data

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Time series big data: a survey on data stream frameworks, analysis and algorithms

Cited by 14 publications

References 82 publications

Incremental Online Machine Learning for Detecting Malicious Nodes in Vehicular Communications Using Real-Time Monitoring

Incremental Online Machine Learning for Detecting Malicious Nodes in Vehicular Communications Using Real-Time Monitoring

Implementation of a Prediction Model in a Smart System for Enhancing Comfort in Dwellings

Stream Data Model and Architecture

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