Physics

Streaming data

Computer network

STREAMS

Algorithm

Distributed computing

Optics

Data mining

Machine learning

Adaptation (eye)

Embedded system

Smart city

Data science

Data stream

Computer science

Computation

Big data

Data stream mining

Concept drift

Telecommunications

Internet of Things

Smart Cities

Real-world data streams pose a unique challenge to the implementation of machine learning (ML) models and data analysis. A notable problem that has been introduced by the growth of Internet of Things (IoT) deployments across the smart city ecosystem is that the statistical properties of data streams can change over time, resulting in poor prediction performance and ineffective decisions. While concept drift detection methods aim to patch this problem, emerging communication and sensing technologies are generating a massive amount of data, requiring distributed environments to perform computation tasks across smart city administrative domains. In this article, we implement and test a number of state-of-the-art active concept drift detection algorithms for time series analysis within a distributed environment. We use real-world data streams and provide critical analysis of results retrieved. The challenges of implementing concept drift adaptation algorithms, along with their applications in smart cities, are also discussed.

Concept Drift Adaptation Techniques in Distributed Environment for Real-World Data Streams