Gaussian Adapted Markov Model with Overhauled Fluctuation Analysis-Based Big Data Streaming Model in Cloud.

An accurate resource usage prediction in the big data streaming applications still remains as one of the complex processes. In the existing works, various resource scaling techniques are developed for forecasting the resource usage in the big data streaming systems. However, the baseline streaming mechanisms limit with the issues of inefficient resource scaling, inaccurate forecasting, high latency, and running time. Therefore, the proposed work motivates to develop a new framework, named as Gaussian adapted Markov model (GAMM)-overhauled fluctuation analysis (OFA), for an efficient big data streaming in the cloud systems. The purpose of this work is to efficiently manage the time-bounded big data streaming applications with reduced error rate. In this study, the gating strategy is also used to extract the set of features for obtaining nonlinear distribution of data and fat convergence solution, used to perform the fluctuation analysis. Moreover, the layered architecture is developed for simplifying the process of resource forecasting in the streaming applications. During experimentation, the results of the proposed stream model GAMM-OFA are validated and compared by using different measures.

Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysis.

Social media platforms such as (Twitter, Facebook, and Weibo) are being increasingly embraced by individuals, groups, and organizations as a valuable source of information. This social media generated information comes in the form of tweets or posts, and normally characterized as short text, huge, sparse, and low density. Since many real-world applications need semantic interpretation of such short texts, research in Short Text Topic Modeling (STTM) has recently gained a lot of interest to reveal unique and cohesive latent topics. This article examines the current state of the art in STTM algorithms. It presents a comprehensive survey and taxonomy of STTM algorithms for short text topic modelling. The article also includes a qualitative and quantitative study of the STTM algorithms, as well as analyses of the various strengths and drawbacks of STTM techniques. Moreover, a comparative analysis of the topic quality and performance of representative STTM models is presented. The performance evaluation is conducted on two real-world Twitter datasets: the Real-World Pandemic Twitter (RW-Pand-Twitter) dataset and Real-world Cyberbullying Twitter (RW-CB-Twitter) dataset in terms of several metrics such as topic coherence, purity, NMI, and accuracy. Finally, the open challenges and future research directions in this promising field are discussed to highlight the trends of research in STTM. The work presented in this paper is useful for researchers interested in learning state-of-the-art short text topic modelling and researchers focusing on developing new algorithms for short text topic modelling.

Real-time Spread Burst Detection in Data Streaming.

Data streaming has many applications in network monitoring, web services, e-commerce, stock trading, social networks, and distributed sensing. This paper introduces a new problem of real-time burst detection in flow spread, which differs from the traditional problem of burst detection in flow size. It is practically significant with potential applications in cybersecurity, network engineering, and trend identification on the Internet. It is a challenging problem because estimating flow spread requires us to remember all past data items and detecting bursts in real time requires us to minimize spread estimation overhead, which was not the priority in most prior work. This paper provides the first efficient, real-time solution for spread burst detection. It is designed based on a new real-time super spreader identifier, which outperforms the state of the art in terms of both accuracy and processing overhead. The super spreader identifier is in turn based on a new sketch design for real-time spread estimation, which outperforms the best existing sketches.

A federated learning method for real-time emotion state classification from multi-modal streaming.

Emotional and physical health are strongly connected and should be taken care of simultaneously to ensure completely healthy persons. A person's emotional health can be determined by detecting emotional states from various physiological measurements (EDA, RB, EEG, etc.). Affective Computing has become the field of interest, which uses software and hardware to detect emotional states. In the IoT era, wearable sensor-based real-time multi-modal emotion state classification has become one of the hottest topics. In such setting, a data stream is generated from wearable-sensor devices, data accessibility is restricted to those devices only and usually a high data generation rate should be processed to achieve real-time emotion state responses. Additionally, protecting the users' data privacy makes the processing of such data even more challenging. Traditional classifiers have limitations to achieve high accuracy of emotional state detection under demanding requirements of decentralized data and protecting users' privacy of sensitive information as such classifiers need to see all data. Here comes the federated learning, whose main idea is to create a global classifier without accessing the users' local data. Therefore, we have developed a federated learning framework for real-time emotion state classification using multi-modal physiological data streams from wearable sensors, called Fed-ReMECS. The main findings of our Fed-ReMECS framework are the development of an efficient and scalable real-time emotion classification system from distributed multimodal physiological data streams, where the global classifier is built without accessing (privacy protection) the users' data in an IoT environment. The experimental study is conducted using the popularly used multi-modal benchmark DEAP dataset for emotion classification. The results show the effectiveness of our developed approach in terms of accuracy, efficiency, scalability and users' data privacy protection.

Demand-Driven Data Acquisition for Large Scale Fleets.

Automakers manage vast fleets of connected vehicles and face an ever-increasing demand for their sensor readings. This demand originates from many stakeholders, each potentially requiring different sensors from different vehicles. Currently, this demand remains largely unfulfilled due to a lack of systems that can handle such diverse demands efficiently. Vehicles are usually passive participants in data acquisition, each continuously reading and transmitting the same static set of sensors. However, in a multi-tenant setup with diverse data demands, each vehicle potentially needs to provide different data instead. We present a system that performs such vehicle-specific minimization of data acquisition by mapping individual data demands to individual vehicles. We collect personal data only after prior consent and fulfill the requirements of the GDPR. Non-personal data can be collected by directly addressing individual vehicles. The system consists of a software component natively integrated with a major automaker's vehicle platform and a cloud platform brokering access to acquired data. Sensor readings are either provided via near real-time streaming or as recorded trip files that provide specific consistency guarantees. A performance evaluation with over 200,000 simulated vehicles has shown that our system can increase server capacity on-demand and process streaming data within 269 ms on average during peak load. The resulting architecture can be used by other automakers or operators of large sensor networks. Native vehicle integration is not mandatory; the architecture can also be used with retrofitted hardware such as OBD readers.

Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data.

In the last few decades, vehicles are equipped with a plethora of sensors which can provide useful measurements and diagnostics for both the vehicle's condition as well as the driver's behaviour. Furthermore, the rapid increase for transportation needs of people and goods together with the evolution of Information and Communication Technologies (ICT) push the transportation domain towards a new more intelligent and efficient era. The reduction of CO2 emissions and the minimization of the environmental footprint is, undeniably, of utmost importance for the protection of the environment. In this light, it is widely acceptable that the driving behaviour is directly associated with the vehicle's fuel consumption and gas emissions. Thus, given the fact that, nowadays, vehicles are equipped with sensors that can collect a variety of data, such as speed, acceleration, fuel consumption, direction, etc. is more feasible than ever to put forward solutions which aim not only to monitor but also improve the drivers' behaviour from an environmental point of view. The approach presented in this paper describes a holistic integrated platform which combines well-known machine and deep learning algorithms together with open-source-based tools in order to gather, store, process, analyze and correlate different data flows originating from vehicles. Particularly, data streamed from different vehicles are processed and analyzed with the utilization of clustering techniques in order to classify the driver's behaviour as eco-friendly or not, followed by a comparative analysis of supervised machine and deep learning algorithms in the given labelled dataset.

A novel algorithm for minute ventilation estimation in remote health monitoring with magnetometer plethysmography.

PURPOSE: The purpose of this study was to evaluate the accuracy of minute ventilation (V˙E) estimation using a novel method based on a non-linear algorithm coupled with cycle-based features. The experiment protocol was well adapted for remote health monitoring applications by exploiting data streams from respiratory magnetometer plethysmography (RMP) during different physical activity (PA) types. Methods Thirteen subjects with an age distribution of 24.1±3.4 years performed thirteen PA ranging from sedentary to moderate intensity (walking at 4 and 6 km/h, running at 9 and 12 km/h, biking at 90 W and 110 W). In total, 3359 temporal segments of 10s were acquired using the Nomics RMP device while the iWorx spirometer was used for reference V˙E measurements. An artificial neural network (ANN) model based on respiration features was used to estimate V˙E and compared to the multiple linear regression (MLR) model. We also compared the subject-specific approach with the subject-independent approach. Results The ANN model using subject-specific approach achieved better accuracy for the V˙E estimation. The bias was between 0.20±0.87 and 0.78±3 l/min with the ANN model as compared to 0.73±3.19 and 4.17±2.61 l/min with the MLR model. Conclusion Our results demonstrated the pertinence of processing data streams from wearable RMP device to estimate the V˙E with sufficient accuracy for various PA types. Due to its low-complexity and real-time algorithm design, the current approach can be easily integrated into most remote health monitoring applications coupled with wearable sensors.

Protocol for Streaming Data from an RFID Sensor Network †.

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 set up an RFID sensor network. The reader powers up the tags' microcontroller and their attached sensor using radio frequency waves, and tags backscatter, not only their EPC code but also the value of those sensors. The current standard for interrogating these CRFID tags is the EPC global Class 1 Generation 2 (EPC C1G2). When several tags are located inside the reader interrogation area, the EPC C1G2 results in very poor performance to obtain sensor data values. To solve this problem, a novel protocol called Sensor Frmed Slotted Aloha (sFSA) for streaming sensor data dealing with the tag collisions is presented. The proposed protocol increases the Sensor Read Rate (SRR), defined as the number of sensor data reads per second, compared to the standard. Additionally, this paper presents a prototype of an RFID sensor network to compare the proposed sFSA with the standard, increasing the SRR by more than five times on average. Additionally, the proposed protocol keeps a constant sensor sampling frequency for a suitable streaming of these tag sensors.

Improvement of Kafka Streaming Using Partition and Multi-Threading in Big Data Environment.

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 support of many features in the manufacturing industry. In this ecosystem, Apache Hadoop and HBase are used as Big Data storage and handle large scale data. In addition, Apache Kafka is used as a data streaming pipeline which contains many configurations and properties that are used to make a better-designed environment and a reliable system, such as Kafka offset and partition, which is used for program scaling purposes. Moreover, Apache Spark closely works with Kafka consumers to create a real-time processing and analysis of the data. Meanwhile, data security is applied in the data transmission phase between the Kafka producers and consumers. Public-key cryptography is performed as a security method which contains public and private keys. Additionally, the public-key is located in the Kafka producer, and the private-key is stored in the Kafka consumer. The integration of these above technologies will enhance the performance and accuracy of data storing, processing, and securing in the manufacturing environment.

HTMoL: full-stack solution for remote access, visualization, and analysis of molecular dynamics trajectory data.

Research on biology has seen significant advances with the use of molecular dynamics (MD) simulations. The MD methodology enables explanation and discovery of molecular mechanisms in a wide range of natural processes and biological systems. The need to readily share the ever-increasing amount of MD data has been hindered by the lack of specialized bioinformatic tools. The difficulty lies in the efficient management of the data, i.e., in sending and processing 3D information for its visualization. In this work, we present HTMoL, a plug-in-free, secure GPU-accelerated web application specifically designed to stream and visualize MD trajectory data on a web browser. Now, individual research labs can publish MD data on the Internet, or use HTMoL to profoundly improve scientific reports by including supplemental MD data in a journal publication. HTMoL can also be used as a visualization interface to access MD trajectories generated on a high-performance computer center directly. Furthermore, the HTMoL architecture can be leveraged with educational efforts to improve learning in the fields of biology, chemistry, and physics.

Real-Time Management of Multimodal Streaming Data for Monitoring of Epileptic Patients.

New generation of healthcare is represented by wearable health monitoring systems, which provide real-time monitoring of patient's physiological parameters. It is expected that continuous ambulatory monitoring of vital signals will improve treatment of patients and enable proactive personal health management. In this paper, we present the implementation of a multimodal real-time system for epilepsy management. The proposed methodology is based on a data streaming architecture and efficient management of a big flow of physiological parameters. The performance of this architecture is examined for varying spatial resolution of the recorded data.