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14th IEEE International Scientific Conference on Informatics

作者： Cegan, Lukas Filip, Petr Univ Pardubice Fac Elect Engn & Informat Pardubice Czech Republic

ISBN: (纸本)9781538608890

Web analytic tools offer important support for better recognition of the web user's behavior, identification of bottlenecks and errors in user interface design, performance measurement of web environment, monitoring of website availability or recommendation of appropriate website content. These tools are based on tracking techniques and sophisticated algorithms that process and evaluate large volumes of captured data. In this paper, we propose a new solution to capture mouse movements of web users, to identify their area of interest. This solution is based on real-time data transformation, which converts discrete position data with high sample period to predefined functions. This transformation has a high degree of accuracy, which is exemplified by case scenarios. The result of this solution is a significant saving in data, transmitted from the client to the server, which leads to significant savings in system resources on the server side.

关键词： mouse tracking real-time data processing user behavior

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Route Memorization in real-time data processing Using Run-Length Encoding

Route Memorization in Real-Time Data Processing Using Run-Le...

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IEEE Intelligent Vehicles Symposium

作者： Luo, Feng Huang, Zhihui Yan, Fei Sun, Dongyuan Tongji Univ Coll Automot Engn Shanghai 200092 Peoples R China

ISBN: (纸本)9781424435036

A real-time data processing algorithm based on Run-Length Encoding (RLE) for an intelligent racing vehicle is introduced in this paper. In order to improve the achievement of the intelligent racing vehicle's running in the second loop by recoding route information, a new method based on RLE is provided by setting an optimal calculus threshold. Simulated by Matlab/Simulink, the route memorization algorithm shows the obvious advantage in the data compression, which makes the compression ratio up to 22.3. In the meantime, the calculus threshold can be constructed in a certain range, which qualifies the algorithm with a good robustness. Compared with the optimal results achieved by Matlab's genetic algorithm and direct search toolbox, the RLE algorithm can satisfy the requirements very well. When the embedded system has to face the flood for the data increasing geometrically, this high-quality real-time algorithm has been demonstrated with great practical potential.

关键词： real-time data processing Intelligent racing vehicle Memorizing algorithm Run-length encoding

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Publish/Subscribe Method for real-time data processing in Massive IoT Leveraging Blockchain for Secured Storage

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SENSORS 2023年第24期23卷 9692-9692页

作者： Ataei, Mohammadhossein Eghmazi, Ali Shakerian, Ali Landry Jr, Rene Chevrette, Guy Ecole Technol Super Dept Elect Engn Montreal PQ H3C 1K3 Canada Corp Off iMETRIK Global Inc Montreal PQ J4P 2K7 Canada

In the Internet of Things (IoT) era, the surge in Machine-Type Devices (MTDs) has introduced Massive IoT (MIoT), opening new horizons in the world of connected devices. However, such proliferation presents challenges, especially in storing and analyzing massive, heterogeneous data streams in real time. In order to manage Massive IoT data streams, we utilize analytical database software such as Apache Druid version 28.0.0 that excels in real-time data processing. Our approach relies on a publish/subscribe mechanism, where device-generated data are relayed to a dedicated broker, effectively functioning as a separate server. This broker enables any application to subscribe to the dataset, promoting a dynamic and responsive data ecosystem. At the core of our data transmission infrastructure lies Apache Kafka version 3.6.1, renowned for its exceptional data flow management performance. Kafka efficiently bridges the gap between MIoT sensors and brokers, enabling parallel clusters of brokers that lead to more scalability. In our pursuit of uninterrupted connectivity, we incorporate a fail-safe mechanism with two Software-Defined Radios (SDR) called Nutaq PicoLTE Release 1.5 within our model. This strategic redundancy enhances data transmission availability, safeguarding against connectivity disruptions. Furthermore, to enhance the data repository security, we utilize blockchain technology, specifically Hyperledger Fabric, known for its high-performance attributes, ensuring data integrity, immutability, and security. Our latency results demonstrate that our platform effectively reduces latency for 100,000 devices, qualifying as an MIoT, to less than 25 milliseconds. Furthermore, our findings on blockchain performance underscore our model as a secure platform, achieving over 800 Transactions Per Second in a dataset comprising 14,000 transactions, thereby demonstrating its high efficiency.

关键词： Massive Internet of Things (MIoT) publish/subscribe methodology real-time data processing Apache Kafka Apache Druid latency Hyperledger Fabric storage security

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A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and real-time data processing

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SENSORS 2018年第7期18卷 2183-2183页

作者： Alfian, Ganjar Syafrudin, Muhammad Ijaz, Muhammad Fazal Syaekhoni, M. Alex Fitriyani, Norma Latif Rhee, Jongtae Dongguk Univ Nano Informat Technol Acad U SCM Res Ctr Seoul 100715 South Korea Dongguk Univ Dept Ind & Syst Engn Seoul 100715 South Korea

Current technology provides an efficient way of monitoring the personal health of individuals. Bluetooth Low Energy (BLE)-based sensors can be considered as a solution for monitoring personal vital signs data. In this study, we propose a personalized healthcare monitoring system by utilizing a BLE-based sensor device, real-time data processing, and machine learning-based algorithms to help diabetic patients to better self-manage their chronic condition. BLEs were used to gather users' vital signs data such as blood pressure, heart rate, weight, and blood glucose (BG) from sensor nodes to smartphones, while real-time data processing was utilized to manage the large amount of continuously generated sensor data. The proposed real-time data processing utilized Apache Kafka as a streaming platform and MongoDB to store the sensor data from the patient. The results show that commercial versions of the BLE-based sensors and the proposed real-time data processing are sufficiently efficient to monitor the vital signs data of diabetic patients. Furthermore, machine learning based classification methods were tested on a diabetes dataset and showed that a Multilayer Perceptron can provide early prediction of diabetes given the user's sensor data as input. The results also reveal that Long Short-Term Memory can accurately predict the future BG level based on the current sensor data. In addition, the proposed diabetes classification and BG prediction could be combined with personalized diet and physical activity suggestions in order to improve the health quality of patients and to avoid critical conditions in the future.

关键词： diabetes BLE real-time data processing classification forecasting

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OPTIMAL real-time data-ACQUISITION AND processing BY A MULTISERVER STANDBY SYSTEM

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OPERATIONS RESEARCH 1994年第1期42卷 24-30页

作者： KREIMER, J MEHREZ, A BEN GURION UNIV NEGEV GEORGE SHRUT CHAIR DECIS MAKING & RISK MANAGEMENTIL-84105 BEER SHEVAISRAEL KENT STATE UNIV KENTOH 44242 OHIO UNIV ATHENSOH 45701 MCMASTER UNIV HAMILTON L8S 4L8ONTARIOCANADA

We obtain an optimal operation policy for a general n-server (channel, machine) stand-by system involving preventive maintenance and operating in real time. We show that such a policy is achieved by successive total use of each server capacity, and does not depend on the arrival pattern of processed data and task duration. We show also that this policy is optimal for any finite, initial period of time, and not only in the sense of long-run availability. The analysis is performed in two stages: a worst-case analysis is performed, and then, a general case is treated. Such a consecutive approach also seems to be useful in the analysis of more complicated systems. Rigorous mathematical proofs are provided. Actual and possible applications of results to military and production control systems are discussed.

关键词： MILITARY, DEFENSE SYSTEMS INTELLIGENCE PRODUCTION SCHEDULING real-time data processing RELIABILITY, AVAILABILITY MULTISERVER STANDBY SYSTEM

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Smart Futures Based Resource Trading and Coalition Formation for real-time Mobile data processing

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2022年第5期15卷 3047-3060页

作者： Chen, Ruitao Wang, Xianbin Liu, Xue Western Univ Dept Elect & Comp Engn London ON N6A 5B9 Canada McGill Univ Sch Comp Sci Montreal PQ H3A 0E9 Canada

Collaboration among mobile devices (MDs) is becoming more important, as it could augment computing capacity at the network edge through peer-to-peer service provisioning, and directly enhance real-time computational performance in smart Internet-of-Things applications. As an important aspect of collaboration mechanism, conventional resource trading (RT) among MDs relies on an onsite interaction process, i.e., price negotiation between service providers and requesters, which, however, inevitably incurs excessive latency and degrades RT efficiency. To overcome this challenge, this article adopts the concept of futures contract (FC) used in financial market, and proposes a smart futures for low latency RT. This new technique enables MDs to form trading coalitions and negotiate multilateral forward contracts applied to a collaboration term in the future. To maximize the benefits of self-interested MDs, the negotiation process of FC is modelled as a coalition formation game comprised of three components executed in an iterative manner, i.e., futures resource allocation, revenue sharing and payment allocation, and distributed decision-making of individual MD. Additionally, a FC enforcement scheme is implemented to efficiently manage the onsite resource sharing via recording resource balances of different task-types and MDs. Simulation results prove the superiority of smart futures in RT latency reduction and trading fairness provisioning.

关键词： Task analysis Resource management real-time systems Internet of Things Collaboration Decision making Privacy Smart futures coalition formation game resource trading task offloading real-time data processing

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Route Memorization in real-time data processing Using Run-Length Encoding

Route Memorization in Real-Time Data Processing Using Run-Le...

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IEEE Intelligent Vehicles Symposium (IV)

作者： Feng Luo Zhihui Huang Fei Yan Dongyuan Sun College of Automotive Engineering Tongji University

ISBN: (纸本)9781424435036

关键词： real-time data processing Intelligent racing vehicle Memorizing algorithm Run-length encoding

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Research on real time data processing Algorithm for Optical Measurement 3

Research on Real time Data Processing Algorithm for Optical ...

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3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Xin Jian Unit 91550 Dalian Peoples R China

ISBN: (纸本)9781538684818

The algorithm of optical measurement for real-time data processing based on Kalman filter is studied, by analyzing the present situation and exiting problem in the real-time data processing of optical measurement. In this paper, the feasible model and data processing method are given. The simulation results show that the algorithm model is accurate and effective. The research result plays an important role in raising the precision of real-time data processing. Meantime, it also has a guiding significance in further improving of measurement & control software system.

关键词： flight test optical measurement real-time data processing Kalman filter

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A Survey of real-time Big data processing Algorithms 1st

A Survey of Real-Time Big Data Processing Algorithms

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1st International and 4th National Conference on Reliability and Safety Engineering, INCRS 2018

作者： Lal, Devesh Kumar Suman, Ugrasen School of Computer Science & IT Devi Ahilya University Indore India

ISBN: (纸本)9789811537455

data collection and processing in real time is one of the most challenging domains for big data. The sustainable proliferation of unbounded streaming data has become arduous for data collection, data pre-process, data optimization, etc. real-time streaming for data collection can effectively be performed by windowing mechanism. In this communication, we have discussed various windowing mechanisms such as sliding window, tumbling window, landmark window, index-based window, adaptive size tumbling window, and partitioned-based window. The reliability measure, which depends upon selection of appropriate windowing mechanism, has also been discussed. These window-based algorithms have been compared on the basis of CPU utilization, memory consumption, time efficiency, and operation compatibility. In this paper, we have surveyed various aggregation algorithms such as reactive aggregator, flatFAT, flatFIT, B-Int, DABA, and two stacks aggregator and compared them based on time complexity. Remarkably, a hybrid window mechanism has been introduced in this study which can handle the most recent data stream and variable rate of data stream by sliding window and tumbling window, respectively. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Aggregation algorithms Big data real-time data processing Streaming data Window algorithms

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real-time Fuzzy data processing Based on a Computational Library of Analytic Models

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data 2018年第4期3卷 59页

作者： Kondratenko, Yuriy Kondratenko, Nina Petro Mohyla Black Sea Natl Univ Intelligent Informat Syst Dept UA-54003 Mykolaiv Ukraine Univ South Carolina Darla Moore Sch Business Columbia SC 29208 USA

This work focuses on fuzzy data processing in control and decision-making systems based on the transformation of real-timeseries and high-frequency data to fuzzy sets with further implementation of diverse fuzzy arithmetic operations. Special attention was paid to the synthesis of the computational library of horizontal and vertical analytic models for fuzzy sets as the results of fuzzy arithmetic operations. The usage of the developed computational library allows increasing the operating speed and accuracy of fuzzy data processing in real time. A computational library was formed for computing of such fuzzy arithmetic operations as fuzzy-maximum. Fuzzy sets as components of fuzzy data processing were chosen as triangular fuzzy numbers. The analytic models were developed based on the analysis of the intersection points between left and right branches of considered triangular fuzzy numbers with different relations between their parameters. Our study introduces the mask for the evaluation of the relations between corresponding parameters of fuzzy numbers that allows to determine the appropriate model from the computational library in automatic mode. The simulation results confirm the efficiency of the proposed computational library for different applications.

关键词： big data fuzzy set vertical and horizontal models maximum computational library real-time data processing

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