University of Oulu

Mehmood, H.; Kostakos, P.; Cortes, M.; Anagnostopoulos, T.; Pirttikangas, S.; Gilman, E. Concept Drift Adaptation Techniques in Distributed Environment for Real-World Data Streams. Smart Cities 2021, 4, 349-371.

Concept drift adaptation techniques in distributed environment for real-world data streams

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Author: Mehmood, Hassan1; Kostakos, Panos1; Cortes, Marta1;
Organizations: 1Center for Ubiquitous Computing, University of Oulu, Pentti Kaiteran katu 1, 90570 Oulu, Finland
2DigiT.DSS.Lab, Department of Business Administration, University of West Attica, P. Ralli & Thivon 250, Aigaleo, 122 44 Athens, Greece
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.5 MB)
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Language: English
Published: Multidisciplinary Digital Publishing Institute, 2021
Publish Date: 2021-05-19


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.

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Series: Smart cities
ISSN: 2624-6511
ISSN-E: 2624-6511
ISSN-L: 2624-6511
Volume: 4
Issue: 1
Pages: 349 - 371
DOI: 10.3390/smartcities4010021
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This research work has been financially supported by Academy of Finland UrBOT project (323630), by Academy of Finland 6Genesis Flagship (318927), by EU Horizon 2020 project CUTLER: Coastal Urban developmenT through the LEnses of Resiliency, under contract no. 770469 ( (accessed on 16 January 2021)), by Riitta ja Jorma J. Takasen Säätiö sr ( (accessed on 16 January 2021)).
EU Grant Number: (770469) CUTLER - Coastal Urban developmenT through the LEnses of Resiliency
Academy of Finland Grant Number: 323630
Detailed Information: 323630 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
Copyright information: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (