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   Articles with "streaming data" as a keyword



A systematic review on detection and adaptation of concept drift in streaming data using machine learning techniques

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recommendations! Published in 2024 at "Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"

DOI: 10.1002/widm.1536

Abstract: Last decade demonstrate the massive growth in organizational data which keeps on increasing multi‐fold as millions of records get updated every second. Handling such vast and continuous data is challenging which further opens up many… read more here.

Keywords: streaming data; concept; detection adaptation; concept drift ... See more keywords

Efficient locality-sensitive hashing over high-dimensional streaming data

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recommendations! Published in 2020 at "Neural Computing and Applications"

DOI: 10.1007/s00521-020-05336-1

Abstract: Approximate nearest neighbor (ANN) search in high-dimensional spaces is fundamental in many applications. Locality-sensitive hashing (LSH) is a well-known methodology to solve the ANN problem. Existing LSH-based ANN solutions typically employ a large number of… read more here.

Keywords: streaming data; sensitive hashing; dimensional streaming; high dimensional ... See more keywords

Revisiting streaming anomaly detection: benchmark and evaluation

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recommendations! Published in 2024 at "Artificial Intelligence Review"

DOI: 10.1007/s10462-024-10995-w

Abstract: Anomaly detection in streaming data is an important task for many real-world applications, such as network security, fraud detection, and system monitoring. However, streaming data often exhibit concept drift, which means that the data distribution… read more here.

Keywords: detection; detection streaming; anomaly detection; streaming data ... See more keywords

Applying temporal dependence to detect changes in streaming data

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recommendations! Published in 2018 at "Applied Intelligence"

DOI: 10.1007/s10489-018-1254-7

Abstract: Detection of changes in streaming data is an important mining task, with a wide range of real-life applications. Numerous algorithms have been proposed to efficiently detect changes in streaming data. However, the limitation of existing… read more here.

Keywords: streaming data; changes streaming; applying temporal; temporal dependence ... See more keywords

Gradient-Based Training of Gaussian Mixture Models for High-Dimensional Streaming Data

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recommendations! Published in 2021 at "Neural Processing Letters"

DOI: 10.1007/s11063-021-10599-3

Abstract: We present an approach for efficiently training Gaussian Mixture Model (GMM) by Stochastic Gradient Descent (SGD) with non-stationary, high-dimensional streaming data. Our training scheme does not require data-driven parameter initialization (e.g., k-means) and can thus… read more here.

Keywords: high dimensional; streaming data; gaussian mixture; training gaussian ... See more keywords

Renewable estimation in expectile regression model with streaming data sets

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recommendations! Published in 2024 at "Journal of Statistical Computation and Simulation"

DOI: 10.1080/00949655.2024.2401132

Abstract: Streaming data continually expands over time, making it challenging to apply traditional expectile regression methods due to memory limitations. Expectile regression is advantageous for studying the entire conditional distribution of the response to a predictor,… read more here.

Keywords: regression model; expectile regression; renewable estimation; streaming data ... See more keywords

Tensor-based feedback control for locally structured high-dimensional streaming data under limited control capability

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recommendations! Published in 2024 at "IISE Transactions"

DOI: 10.1080/24725854.2024.2443959

Abstract: Abstract Structured high-dimensional streaming data offers abundant information that is crucial for process feedback control. Nevertheless, traditional control models predominantly emphasize the global patterns of spatiotemporal correlation within responses, often neglecting the local correlation structure.… read more here.

Keywords: control; high dimensional; streaming data; dimensional streaming ... See more keywords

A Multi-Tier Stacked Ensemble Algorithm to Reduce the Regret of Incremental Learning for Streaming Data

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recommendations! Published in 2018 at "IEEE Access"

DOI: 10.1109/access.2018.2868114

Abstract: Incremental Learning (IL) is an exciting paradigm that deals with classification problems based on a streaming or sequential data. IL aims to achieve the same level of prediction accuracy on streaming data as that of… read more here.

Keywords: incremental learning; streaming data; algorithm; multi tier ... See more keywords

Streaming Data Collection With a Private Sketch-Based Protocol

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recommendations! Published in 2024 at "IEEE Internet of Things Journal"

DOI: 10.1109/jiot.2024.3397908

Abstract: Data stream collection is critical to analyze service conditions and detect anomalies in time, especially in Internet of Things. However, it may undermine the individual privacy. Local differential privacy (LDP) has recently become a popular… read more here.

Keywords: data collection; privacy; streaming data; privsketch ... See more keywords
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Rethinking Visual Analytics for Streaming Data Applications

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recommendations! Published in 2017 at "IEEE Internet Computing"

DOI: 10.1109/mic.2017.2911428

Abstract: Visual analytics is entering a period of renewed growth due to a shift in focus from static to streaming data applications. In this article, the authors illustrate several challenges arising from this pivot and suggest… read more here.

Keywords: analytics streaming; visual analytics; streaming data; rethinking visual ... See more keywords

Online and Unsupervised Anomaly Detection for Streaming Data Using an Array of Sliding Windows and PDDs

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recommendations! Published in 2021 at "IEEE Transactions on Cybernetics"

DOI: 10.1109/tcyb.2019.2935066

Abstract: In this article, we propose an online and unsupervised anomaly detection algorithm for streaming data using an array of sliding windows and the probability density-based descriptors (PDDs) (based on these windows). This algorithm mainly consists… read more here.

Keywords: streaming data; online unsupervised; array; unsupervised anomaly ... See more keywords