Currently, there is an increasing interest in the use of Radio Frequency Identification (RFID) tags which incorporate passive or battery-less sensors. These systems are known as computational RFID (CRFID). Several CRFID tags together with a reader se...
To achieve the full analytical potential of the streaming data from the internet of things, the interconnection of various data sources is needed. By definition, those sources are heterogeneous and their integration is not a trivial task. A common ap...
Sensor data streams often represent signals/trajectories which are twice differentiable (e.g., to give a continuous velocity and acceleration), and this property must be reflected in their segmentation. An adaptive streaming algorithm for this proble...
Developing robot control software systems is difficult because of a wide variety of requirements, including hardware systems and sensors, even though robots are demanding nowadays. Middleware systems, such as Robot Operating System (ROS), are being d...
The functional linear model
Web systems have become a valuable source of semi-structured and streaming data. In this sense, Entity Resolution (ER) has become a key solution for integrating multiple data sources or identifying similarities between data items, namely entities. To...
Nowadays, ubiquitous technology makes life easier, especially devices that use the internet (IoT). IoT devices have been used to generate data in various domains, including healthcare, industry, and education. However, there are often problems with t...
A significant rise in the adoption of streaming applications has changed the decision-making processes in the last decade. This movement has led to the emergence of several Big Data technologies for in-memory processing, such as the systems Apache St...
To design an algorithm for detecting outliers over streaming data has become an important task in many common applications, arising in areas such as fraud detections, network analysis, environment monitoring and so forth. Due to the fact that real-ti...
Streaming data are present all around us. From traditional radio systems streaming audio to today’s connected end-user devices constantly sending information or accessing services, data are flowing constantly between nodes across various networ...
The volume of streaming sensor data from various environmental sensors continues to increase rapidly due to wider deployments of IoT devices at much greater scales than ever before. This, in turn, causes massive increase in the fog, cloud network tra...
In the current paper, we propose a machine learning forecasting model for the accurate prediction of qualitative weather information on winter precipitation types, utilized in Apache Spark Streaming distributed framework. The proposed model receives...
Today, the use of digital medicine is becoming more and more common in medicine. With the use of digital medicine, health data can be shared, processed, and visualized using computer algorithms. One of the problems currently facing digital medicine i...
Currently, there is an increasing interest in the use of Radio Frequency Identification (RFID) tags which incorporate passive or battery-less sensors. These systems are known as computational RFID (CRFID). Several CRFID tags together with a reader se...
Over the past couple of years, machine learning methods—especially the outlier detection ones—have anchored in the cybersecurity field to detect network-based anomalies rooted in novel attack patterns. However, the ubiquity of massive con...
Network intrusion, such as denial of service, probing attacks, and phishing, comprises some of the complex threats that have put the online community at risk. The increase in the number of these attacks has given rise to a serious interest in the res...
Today, big data are generated from many sources, and there is a huge demand for storing, managing, processing, and querying on big data. The MapReduce model and its counterpart open source implementation Hadoop, has proven itself as the de facto solu...
In this paper, a memory-efficient outlier detection (MEOD) approach for streaming data is proposed. The approach uses a local correlation integral (LOCI) algorithm for outlier detection, finding the outlier based on the density of neighboring points...
Ensuring the trustworthiness of data used in real-time analytics remains a critical challenge in smart city monitoring and decision-making. This is because the traditional data validation methods are insufficient for handling the dynamic and heteroge...
The rapid advancement of technology has greatly expanded the capabilities of unmanned aerial vehicles (UAVs) in wireless communication and edge computing domains. The primary objective of UAVs is the seamless transfer of video data streams to emergen...
This paper addresses a method to transmit streaming data via rolling shutter camera-based optical camera communications (OCC). Since the amount of data that can be contained within one frame is limited, and the continuity of received data cannot be g...
In recent years, detecting anomalies in real-world computer networks has become a more and more challenging task due to the steady increase of high-volume, high-speed and high-dimensional streaming data, for which ground truth information is not avai...
The estimation of correlation matrices in high-dimensional data streams presents a fundamental conflict between computational efficiency and statistical robustness. Moment-based estimators, such as Pearson’s correlation, offer linear
Future-oriented networking infrastructures are characterized by highly dynamic Streaming Data (SD) whose volume, speed and number of dimensions increased significantly over the past couple of years, energized by trends such as Software-Defined Networ...
The large amount of programmable logic controller (PLC) sensing data has rapidly increased in the manufacturing environment. Therefore, a large data store is necessary for Big Data platforms. In this paper, we propose a Hadoop ecosystem for the suppo...
Incremental feature extraction algorithms are designed to analyze large-scale data streams. Many of them suffer from high computational cost, time complexity, and data dependency, which adversely affects the processing of the data stream. With this m...
Computation, data transmission, and all the activities involved in Artificial Intelligence of Things (AIoT) systems raise growing concerns in terms of resource consumption and environmental impact. Industrial asset monitoring is a data-intensive use...
The growing adoption of wearable devices creates a critical need for robust and secure Internet of Things solutions to manage biometric data streams. Current architectures often lack emphasis on seamless data capture, secure cloud storage and integra...
In the actual panorama of machine learning (ML) algorithms, the issue of the real-time information extraction/classification/manipulation/analysis of data streams (DS) is acquiring an ever-growing relevance. They arrive generally at high speed and al...
The efficient data access of streaming vehicle data is the foundation of analyzing, using and mining vehicle data in smart cities, which is an approach to understand traffic environments. However, the number of vehicles in urban cities has grown rapi...
The capability of efficiently processing the data streams emitted by nowadays ubiquitous sensing devices enables the development of new intelligent services. Data Stream Processing (DSP) applications allow for processing huge volumes of data in near...
Online anomaly detection is critical for industrial safety and security monitoring but is facing challenges due to the complexity of evolving data streams from working conditions and performance degradation. Unfortunately, existing approaches fall sh...
Deep learning has been broadly applied in many fields and has greatly improved efficiency compared to traditional approaches. However, it cannot resolve issues well when there are a lack of training samples, or in some varying cases, it cannot give a...
Modern multi-tiered data storage systems optimize file access by managing data across a hybrid composition of caches and storage tiers while using policies whose decisions can severely impact the storage system’s performance. Recently, differen...
With the rapid growth of live-streaming e-commerce and digital marketing, abnormal marketing behaviors have become increasingly concealed, coordinated, and intertwined across heterogeneous data modalities, posing substantial challenges to data-driven...
Live streaming commerce (LSC) effectively combines the traditional real economy and e-commerce. Based on more than half a million unique GIS data values on LSC activities sourced via Taobao (Alibaba), we traced the spatial distribution of different p...
Not all features in many real-world applications, such as medical diagnosis and fraud detection, are available from the start. They are formed and individually flow over time. Online streaming feature selection (OSFS) has recently attracted much atte...
As new cyberattacks are launched against systems and networks on a daily basis, the ability for network intrusion detection systems to operate efficiently in the big data era has become critically important, particularly as more low-power Internet-of...
In mobile short-form video streaming, the video application usually provides the user with a playlist of recommended videos to be played one by one. In order to prevent playback stalls caused by possible fluctuations in the mobile network, after fini...
Tropospheric systems are widely used by military forces as they provide long-distance, real-time communication. Slow-fading propagation loss reduces link availability and limits its data-carrying capacity. Beam pointing dynamics provides knowledge of...
The paper presents the results of an experimental investigation and statistical analysis of the data streams generated by popular Internet services. It is found that every investigated Internet service generates data streams of a specific type, posse...
With the proliferation of sensors and IoT technologies, stream data are increasingly stored and analysed, but rarely combined, due to the heterogeneity of sources and technologies. Semantics are increasingly used to share sensory data, but not so muc...
Stream Data Warehouses (SDWs) were introduced to integrate traditional data warehousing with continuous processing of incoming data streams. Such capabilities are increasingly relevant to computational intelligence systems operating in dynamic, data-...
The development in the fields of clean energy, particularly wind and photovoltaic power, generates a large amount of data streams, and how to mine valuable information from these data to improve the efficiency of power generation has become a hot spo...
Recently, there has been an explosive generation of streaming data in various fields such as IoT and network attack detection, medical data monitoring, and financial trend analysis. These domains require precise and rapid analysis capabilities by min...
Data growth in today’s world is exponential, many applications generate huge amount of data streams at very high speed such as smart grids, sensor networks, video surveillance, financial systems, medical science data, web click streams, network...
With the rapid development of emerging technologies such as self-media, the Internet of Things, and cloud computing, massive data applications are crossing the threshold of the era of real-time analysis and value realization, which makes data streams...
Ubiquitous automated data collection at an unprecedented scale is making available streaming, real-time information flows in a wide variety of settings, transforming both science and industry. Learning algorithms deployed in such contexts often rely...
In today’s big data era, cleaning big data streams has become a challenging task because of the different formats of big data and the massive amount of big data which is being generated. Many studies have proposed different techniques to overco...
The data landscape has changed, as more and more information is produced in the form of continuous data streams instead of stationary datasets. In this context, several online machine learning techniques have been proposed with the aim of automatical...
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