Dayu Xu^1,#, Jiaming Lü^1,#, Xuyao Zhang², Hongtao Zhang^1,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2557-2573, 2024, DOI:10.32604/cmc.2024.045932

Abstract Data stream clustering is integral to contemporary big data applications. However, addressing the ongoing influx of data streams efficiently and accurately remains a primary challenge in current research. This paper aims to elevate the efficiency and precision of data stream clustering, leveraging the TEDA (Typicality and Eccentricity Data Analysis) algorithm as a foundation, we introduce improvements by integrating a nearest neighbor search algorithm to enhance both the efficiency and accuracy of the algorithm. The original TEDA algorithm, grounded in the concept of “Typicality and Eccentricity Data Analytics”, represents an evolving and recursive method that requires no prior knowledge. While the… More >

Nur Laila Ab Ghani^1,2,*, Izzatdin Abdul Aziz^1,2, Said Jadid AbdulKadir^1,2

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4649-4668, 2023, DOI:10.32604/cmc.2023.035987

Abstract Clustering high dimensional data is challenging as data dimensionality increases the distance between data points, resulting in sparse regions that degrade clustering performance. Subspace clustering is a common approach for processing high-dimensional data by finding relevant features for each cluster in the data space. Subspace clustering methods extend traditional clustering to account for the constraints imposed by data streams. Data streams are not only high-dimensional, but also unbounded and evolving. This necessitates the development of subspace clustering algorithms that can handle high dimensionality and adapt to the unique characteristics of data streams. Although many articles have contributed to the literature… More >

Abdul Sattar Palli^1,6,*, Jafreezal Jaafar^1,2, Manzoor Ahmed Hashmani^1,3, Heitor Murilo Gomes^4,5, Aeshah Alsughayyir⁷, Abdul Rehman Gilal¹

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1827-1845, 2023, DOI:10.32604/cmc.2023.033934

Abstract Every application in a smart city environment like the smart grid, health monitoring, security, and surveillance generates non-stationary data streams. Due to such nature, the statistical properties of data changes over time, leading to class imbalance and concept drift issues. Both these issues cause model performance degradation. Most of the current work has been focused on developing an ensemble strategy by training a new classifier on the latest data to resolve the issue. These techniques suffer while training the new classifier if the data is imbalanced. Also, the class imbalance ratio may change greatly from one input stream to another,… More >

Idris Rabiu^1,3,*, Naomie Salim², Maged Nasser^1,4, Aminu Da’u¹, Taiseer Abdalla Elfadil Eisa⁵, Mhassen Elnour Elneel Dalam⁶

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6001-6017, 2023, DOI:10.32604/cmc.2023.035221

Abstract Textual data streams have been extensively used in practical applications where consumers of online products have expressed their views regarding online products. Due to changes in data distribution, commonly referred to as concept drift, mining this data stream is a challenging problem for researchers. The majority of the existing drift detection techniques are based on classification errors, which have higher probabilities of false-positive or missed detections. To improve classification accuracy, there is a need to develop more intuitive detection techniques that can identify a great number of drifts in the data streams. This paper presents an adaptive unsupervised learning technique,… More >

E. Susi^*, A. P. Shanthi

Computer Systems Science and Engineering, Vol.45, No.3, pp. 3231-3246, 2023, DOI:10.32604/csse.2023.032104

Abstract Handling sentiment drifts in real time twitter data streams are a challenging task while performing sentiment classifications, because of the changes that occur in the sentiments of twitter users, with respect to time. The growing volume of tweets with sentiment drifts has led to the need for devising an adaptive approach to detect and handle this drift in real time. This work proposes an adaptive learning algorithm-based framework, Twitter Sentiment Drift Analysis-Bidirectional Encoder Representations from Transformers (TSDA-BERT), which introduces a sentiment drift measure to detect drifts and a domain impact score to adaptively retrain the classification model with domain relevant… More >

N. Penchalaiah¹, Abeer S. Al-Humaimeedy², Mashael Maashi³, J. Chinna Babu^4,*, Osamah Ibrahim Khalaf⁵, Theyazn H. H. Aldhyani⁶

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5349-5365, 2022, DOI:10.32604/cmc.2022.029639

Abstract The expanding amounts of information created by Internet of Things (IoT) devices places a strain on cloud computing, which is often used for data analysis and storage. This paper investigates a different approach based on edge cloud applications, which involves data filtering and processing before being delivered to a backup cloud environment. This Paper suggest designing and implementing a low cost, low power cluster of Single Board Computers (SBC) for this purpose, reducing the amount of data that must be transmitted elsewhere, using Big Data ideas and technology. An Apache Hadoop and Spark Cluster that was used to run a… More >

Alaa Eisa¹, Nora EL-Rashidy², Mohammad Dahman Alshehri^3,*, Hazem M. El-bakry¹, Samir Abdelrazek¹

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2901-2921, 2022, DOI:10.32604/cmc.2022.021342

Abstract At this current time, data stream classification plays a key role in big data analytics due to its enormous growth. Most of the existing classification methods used ensemble learning, which is trustworthy but these methods are not effective to face the issues of learning from imbalanced big data, it also supposes that all data are pre-classified. Another weakness of current methods is that it takes a long evaluation time when the target data stream contains a high number of features. The main objective of this research is to develop a new method for incremental learning based on the proposed ant… More >

Marta Mieczyńska^1,*, Ireneusz Czarnowski²

Computer Systems Science and Engineering, Vol.36, No.1, pp. 69-82, 2021, DOI:10.32604/csse.2021.014327

Abstract Automatic Identification System (AIS) data stream analysis is based on the AIS data of different vessel’s behaviours, including the vessels’ routes. When the AIS data consists of outliers, noises, or are incomplete, then the analysis of the vessel’s behaviours is not possible or is limited. When the data consists of outliers, it is not possible to automatically assign the AIS data to a particular vessel. In this paper, a clustering method is proposed to support the AIS data analysis, to qualify noises and outliers with respect to their suitability, and finally to aid the reconstruction of the vessel’s trajectory. In… More >

Le Sun^1,*, Qiandi Yu¹, Dandan Peng¹, Sudha Subramani², Xuyang Wang¹

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 603-619, 2021, DOI:10.32604/cmc.2020.012515

Abstract Recently, an increasing number of works start investigating the combination of fog computing and electronic health (ehealth) applications. However, there are still numerous unresolved issues worth to be explored. For instance, there is a lack of investigation on the disease prediction in fog environment and only limited studies show, how the Quality of Service (QoS) levels of fog services and the data stream mining techniques influence each other to improve the disease prediction performance (e.g., accuracy and time efficiency). To address these issues, we propose a fog-based framework for disease prediction based on Medical sensor data streams, named FogMed. This… More >

Wang Qi^∗

Computer Systems Science and Engineering, Vol.33, No.5, pp. 369-378, 2018, DOI:10.32604/csse.2018.33.369

Abstract With the continuous development of database technology, the data volume that can be stored and processed by the database is increasing. How to dig out information that people are interested in from the massive data is one of the important issues in the field of database research. This article starts from the user demand analysis, and makes an in-depth study of various query expansion problems of skylines. Then, according to different application scenarios, this paper proposes efficient and targeted solutions to effectively meet the actual needs of people. Based on k- representative skyline query problem in the data stream environment,… More >