Multi-type concept drift detection under a dual-layer variable sliding window in frequent pattern mining with cloud computing

Abstract

The detection of different types of concept drift has wide applications in the fields of cloud computing and security information detection. Concept drift detection can indeed assist in promptly identifying instances where model performance deteriorates or when there are changes in data distribution. This paper focuses on the problem of concept drift detection in order to conduct frequent pattern mining. To address the limitation of fixed sliding windows in adapting to evolving data streams, we propose a variable sliding window frequent pattern mining algorithm, which dynamically adjusts the window size to adapt to new concept drifts and detect them in a timely manner. Furthermore, considering the challenge of existing concept drift detection algorithms that struggle to adapt to different types of drifting data simultaneously, we introduce an additional dual-layer embedded variable sliding window. This approach helps differentiate types of concept drift and incorporates a decay model for drift adaptation. The proposed algorithm can effectively detect different types of concept drift in data streams, perform targeted drift adaptation, and exhibit efficiency in terms of time complexity and memory consumption. Additionally, the algorithm maintains stable performance, avoiding abrupt changes due to window size variations and ensuring overall robustness.

Dynamic task offloading edge-aware optimization framework for enhanced UAV operations on edge computing platform

“…The practice of task offloading, which involves moving computationally demanding activities to edge computing nodes that are located close to the operational region of the UAV, is a typical solution that is used to reduce latency and conserve onboard resources 8 . Using machine learning algorithms for real-time data processing is one alternative strategy that may empower UAVs to make autonomous judgments in response to sensor inputs and ambient variables better 9 . Using edge caching techniques makes it possible to store data that is accessed frequently locally, hence further reducing latency and the requirement for data transmission.…”