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A data lake-based security transmission and storage scheme for streaming big data
Streaming big data presents unique security challenges due to its real-time generation and distributed transmission methods, making it vulnerable to...
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OEC: an online ensemble classifier for mining data streams with noisy labels
Distilling actionable patterns from large-scale streaming data in the presence of concept drift is a challenging problem, especially when data is...
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Mining Top-k Frequent Patterns over Streaming Graphs
Mining top-k frequent patterns is an important operation on graphs, which is defined as finding k interesting subgraphs with the highest frequency....
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LASSO for streaming data with adaptative filtering
Streaming data is ubiquitous in modern machine learning, and so the development of scalable algorithms to analyze this sort of information is a topic...
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Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature review
This study investigates existing input privacy-preserving data mining (PPDM) methods and privacy-preserving data stream mining methods (PPDSM),...
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Detection of advanced persistent threats using hashing and graph-based learning on streaming data
Many activities in the cybersecurity realm can be represented using graphs stream, such as call graphs. In this paper, we introduce an innovative...
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Streaming data cleaning based on speed change
Errors are prevalent in data sequences, such as GPS trajectories or sensor readings. Existing methods on cleaning sequential data employ a constraint...
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SETL: a transfer learning based dynamic ensemble classifier for concept drift detection in streaming data
Concept drift is one of the most prominent issues in streaming data that machine learning models need to address. Most of the research in the field...
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Speeding up pattern matching in streaming time-series via block vector and multilevel lower bound
The pattern matching is one of the essential tasks in streaming time-series data mining. Its purpose is to identify all sliding windows in streaming...
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Trends in Data Stream Mining
Learning from data streams is a hot topic in machine learning and data mining. This article presents our recent work on the topic of learning from...
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Streaming Linked Data Life Cycle
The term Linked Data, which was invented by Sir Tim Berners-Lee, refers to a set of best practices for publishing data using Semantic Web...
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An effective cost-sensitive sparse online learning framework for imbalanced streaming data classification and its application to online anomaly detection
Class imbalance is one of the most challenging problems in streaming data mining due to its adverse impact on predictive capability of online models....
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MCMSTStream: applying minimum spanning tree to KD-tree-based micro-clusters to define arbitrary-shaped clusters in streaming data
Stream clustering has emerged as a vital area for processing streaming data in real-time, facilitating the extraction of meaningful information....
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Event detection from real-time twitter streaming data using community detection algorithm
The increasing popularity of social media services has led to more and more people using Twitter. There are millions of tweets with a high amount of...
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Rest security framework for event streaming bus architecture
Businesses are confronted with a massive influx of real-time data originating from various sources such as application logs, website clickstreams,...
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Towards a deep learning-based outlier detection approach in the context of streaming data
Uncommon observations that significantly vary from the norm are referred to as outliers. Outlier detection, which aims to detect unexpected behavior,...
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Learning from streaming data with unsupervised heterogeneous domain adaptation
Many efforts on domain adaptation focus on stationary environments and assume that the target domain samples are available before the learning...
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Using Twitter to analysis of social innovation through user comments data mining
The objective of the research was to extract the opinions of social media users regarding social innovation, recognize common terms, and comprehend...
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Vietnamese hate and offensive detection using PhoBERT-CNN and social media streaming data
Society needs to develop a system to detect hate and offense to build a healthy and safe environment. However, current research in this field still...
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Mining frequent itemsets from streaming transaction data using genetic algorithms
This paper presents a study of mining frequent itemsets from streaming data in the presence of concept drift. Streaming data, being volatile in...
