Development and Investigation of Adaptive Micro-Service Architecture for Messaging Software Systems

2021 ◽  
pp. 46-49
Author(s):  
Rustam Gamzayev ◽  
Bohdan Shkoda
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Data Model ◽  
Personal Communication ◽  
Software Systems ◽  
Time Data ◽  
Huge Amount ◽  
Service Architecture ◽  
Nosql Database ◽  
Real Time Data

Messaging Software systems (MSS) are one of the most popular tools used by huge amount of people. They could be used for personal communication and for business purposes. Building an own MSS system requires analysis of the quality attributes and considering adaptation to the changing environment. In this paper an overview of existing MSS architecture was done. Data model was developed to support historical and real time data storage and processing. An own approach to build Adaptive Microservice MSS based on the messaging middleware and NoSQL database was proposed.

scholarly journals Analisa Real-Time Data log honeypot menggunakan Algoritma K-Means pada serangan Distributed Denial of Service

Repositor ◽  
2020 ◽  
Vol 2 (5) ◽  
pp. 541
Author(s):  
Denni Septian Hermawan ◽  
Syaifuddin Syaifuddin ◽  
Diah Risqiwati
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Denial Of Service ◽  
The Internet ◽  
Distributed Denial Of Service ◽  
Time Data ◽  
Local Networks ◽  
Real Time Data ◽  
Internet Networks ◽  
Data Log

AbstrakJaringan internet yang saat ini di gunakan untuk penyimpanan data atau halaman informasi pada website menjadi rentan terhadap serangan, untuk meninkatkan keamanan website dan jaringannya, di butuhkan honeypot yang mampu menangkap serangan yang di lakukan pada jaringan lokal dan internet. Untuk memudahkan administrator mengatasi serangan digunakanlah pengelompokan serangan dengan metode K-Means untuk mengambil ip penyerang. Pembagian kelompok pada titik cluster akan menghasilkan output ip penyerang.serangan di ambil sercara realtime dari log yang di miliki honeypot dengan memanfaatkan MHN.Abstract The number of internet networks used for data storage or information pages on the website is vulnerable to attacks, to secure the security of their websites and networks, requiring honeypots that are capable of capturing attacks on local networks and the internet. To make it easier for administrators to tackle attacks in the use of attacking groupings with the K-Means method to retrieve the attacker ip. Group divisions at the cluster point will generate the ip output of the attacker. The strike is taken as realtime from the logs that have honeypot by utilizing the MHN.

scholarly journals Drilling Efficiency Improvement and Rate of Penetration Optimization by Machine Learning and Data Analytics

2020 ◽  
Vol 5 (3) ◽  
pp. 381-394
Author(s):  
Sridharan Chandrasekaran ◽  
G. Suresh Kumar
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Data Storage ◽  
Predictive Accuracy ◽  
Empirical Models ◽  
Operational Parameters ◽  
Time Data ◽  
Bottom Line ◽  
Rate Of Penetration ◽  
Real Time Data

Rate of Penetration (ROP) is one of the important factors influencing the drilling efficiency. Since cost recovery is an important bottom line in the drilling industry, optimizing ROP is essential to minimize the drilling operational cost and capital cost. Traditional the empirical models are not adaptive to new lithology changes and hence the predictive accuracy is low and subjective. With advancement in big data technologies, real- time data storage cost is lowered, and the availability of real-time data is enhanced. In this study, it is shown that optimization methods together with data models has immense potential in predicting ROP based on real time measurements on the rig. A machine learning based data model is developed by utilizing the offset vertical wells’ real time operational parameters while drilling. Data pre-processing methods and feature engineering methods modify the raw data into a processed data so that the model learns effectively from the inputs. A multi – layer back propagation neural network is developed, cross-validated and compared with field measurements and empirical models.

Status Monitoring and Fault Diagnosis Network System Based on UPnP

2007 ◽  
Vol 353-358 ◽  
pp. 2632-2635
Author(s):  
Pei Yu Li ◽  
Da Peng Tan ◽  
Tao Qing Zhou ◽  
Bo Yu Lin
Keyword(s):  
Fault Diagnosis ◽  
Embedded System ◽  
Real Time ◽  
Data Storage ◽  
Network Architecture ◽  
Network System ◽  
Time Data ◽  
Status Monitoring ◽  
Self Recognition ◽  
Real Time Data

Aiming at some problems in the fields of industry monitoring technology (IMT) such as bad dynamic ability and poor versatility, this paper brought forward a kind of intelligent Status monitoring and Fault diagnosis Network System (SFNS) based on UPnP-Universal Plug and Play. The model for fault diagnosis network system was established according to characteristics and requirements of IMT network, and system network architecture was designed and realized by UPnP. Using embedded system technology, real-time data collection node, monitoring center node and data storage server were designed, and that supplies powerful real-time data support for SFNS. Industry fields experiments proved that this system can realize self recognition, seamless linkage and other self adapting ability, and can break through the limitation of real IP address to achieve real-time remote monitoring on line.

QDM: A Generic QoS-Aware Data Model for Real-Time Data Stream Processing

2007 ◽  
Author(s):  
Sven Schmidt ◽  
Benjamin Schlegel ◽  
Wolfgang Lehner
Keyword(s):  
Real Time ◽  
Data Model ◽  
Data Stream ◽  
Stream Processing ◽  
Time Data ◽  
Data Stream Processing ◽  
Real Time Data

WMO Hydrological Observing System (WHOS) broker: implementation progress and outcomes

2020 ◽  
Author(s):  
Enrico Boldrini ◽  
Paolo Mazzetti ◽  
Stefano Nativi ◽  
Mattia Santoro ◽  
Fabrizio Papeschi ◽  
...  
Keyword(s):  
Real Time ◽  
Data Model ◽  
Meteorological Data ◽  
Heterogeneous Systems ◽  
Data Sources ◽  
Technical Documentation ◽  
Time Data ◽  
Hydrological Data ◽  
Real Time Data ◽  
Rest Api

<p>The WMO Hydrological Observing System (WHOS) is a service-oriented System of Systems (SoS) linking hydrological data providers and users by enabling harmonized and real time discovery and access functionalities at global, regional, national and local scale. WHOS is being realized through a coordinated and collaborative effort amongst:</p><ul><li>National Hydrological Services (NHS) willing to publish their data to the benefit of a larger audience,</li> <li>Hydrologists, decision makers, app and portal authors willing to gain access to world-wide hydrological data,</li> <li>ESSI-Lab of CNR-IIA responsible for the WHOS broker component: a software framework in charge of enabling interoperability amongst the distributed heterogeneous systems belonging to data providers (e.g. data publishing services) and data consumers (e.g. web portals, libraries and apps),</li> <li>WMO Commission of Hydrology (CHy) providing guidance to WMO Member countries in operational hydrology, including capacity building, NHSs engagement and coordination of WHOS implementation.</li> </ul><p>In the last years two additional WMO regional programmes have been targeted to benefit from WHOS, operating as successful applications for others to follow:</p><ul><li>Plata river basin,</li> <li>Arctic-HYCOS.</li> </ul><p>Each programme operates with a “view” of the whole WHOS, a virtual subset composed only by the data sources that are relevant to its context.</p><p><strong>WHOS-Plata</strong> is currently brokering data sources from the following countries:</p><ul><li>Argentina (hydrological & meteorological data),</li> <li>Bolivia (meteorological data; hydrological data expected in the near future),</li> <li>Brazil (hydrological & meteorological data),</li> <li>Paraguay (meteorological data; hydrological data in process),</li> <li>Uruguay (hydrological & meteorological data).</li> </ul><p><strong>WHOS-Arctic</strong> is currently brokering data sources from the following countries:</p><ul><li>Canada (historical and real time data),</li> <li>Denmark (historical data),</li> <li>Finland (historical and real time data),</li> <li>Iceland (historical and real time data),</li> <li>Norway (historical and real time data),</li> <li>Russian (historical and real time data),</li> <li>United States (historical and real time data).</li> </ul><p>Each data source publishes its data online according to specific hydrological service protocols and/or APIs (e.g. CUAHSI HydroServer, USGS Water Services, FTP, SOAP, REST API, OData, WAF, OGC SOS, …). Each service protocol and API in turn implies support for a specific metadata and data model (e.g. WaterML, CSV, XML , JSON, USGS RDB, ZRXP, Observations & Measurements, …).</p><p>WHOS broker implements mediation and harmonization of all these heterogeneous standards, in order to seamlessly support discovery and access of all the available data to a growing set of data consumer systems (applications and libraries) without any implementation effort for them:</p><ul><li>52North Helgoland (through SOS v.2.0.0),</li> <li>CUAHSI HydroDesktop (through CUAHSI WaterOneFlow),</li> <li>National Water Institute of Argentina (INA) node.js WaterML client (through CUAHSI WaterOneFlow),</li> <li>DAB JS API (through DAB REST API),</li> <li>USGS GWIS JS API plotting library (through RDB service),</li> <li>R scripts (through R WaterML library),</li> <li>C# applications (through CUAHSI WaterOneFlow),</li> <li>UCAR jOAI (through OAI-PMH/WIGOS metadata).</li> </ul><p>In particular, the support of WIGOS metadata standard provides a set of observational metadata elements for the effective interpretation of observational data internationally.</p><p>In addition to metadata and data model heterogeneity, WHOS needs to tackle also semantics heterogeneity. WHOS broker makes use of a hydrology ontology (made available as a SPARQL endpoint) to augment WHOS discovery capabilities (e.g. to obtain translation of a hydrology search parameter in multiple languages).</p><p>Technical documentation to exercise WHOS broker is already online available, while the official public launch with a dedicated WMO WHOS web portal is expected shortly.</p>

The Real-Time Optimization Strategy on the Mission Database

2014 ◽  
Vol 1049-1050 ◽  
pp. 2001-2005
Author(s):  
Hua Wang ◽  
Bing Liu ◽  
Huan Ming Liu ◽  
Hui Fen Duan ◽  
Jun Lei Bao
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Relational Database ◽  
Optimization Strategy ◽  
Time Data ◽  
The Real ◽  
Storage Mechanism ◽  
Synchronization Mechanism ◽  
Real Time Data ◽  
And Control

In order to make up the real-time performance of tracking and control information database, this paper design a kind of two-layer’s real-time data storage model based on memory database and relational database. In this article, the two-layer’s real-time data storage mechanism and life cycle are expounded in detail, analyzing and inducing the real-time data characteristic and storage strategy, putting forward the memory database’s self-adaptive index algorithm of T-tree index and hash index, and introducing the database synchronization mechanism between the memory database and relational database and so on. In this way, so as to improve and optimize the real-time, reliability and security of database, provides a reliable data guarantee for future expansion of the real-time application.

scholarly journals Machine Learning based Improved Gaussian Mixture Model for IoT Real-Time Data Analysis

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Sivadi Sivadi ◽  
Moorthy Moorthy ◽  
Vijender Solanki
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Real Time ◽  
Gaussian Mixture Model ◽  
Gaussian Mixture ◽  
Sensor Data ◽  
Cloud Platform ◽  
Time Data ◽  
Huge Amount ◽  
Real Time Data

Introduction: The article is the product of the research “Due to the increase in popularity of Internet of Things (IoT), a huge amount of sensor data is being generated from various smart city applications”, developed at Pondicherry University in the year 2019. Problem:To acquire and analyze the huge amount of sensor-generated data effectively is a significant problem when processing the data. Objective:  To propose a novel framework for IoT sensor data analysis using machine learning based improved Gaussian Mixture Model (GMM) by acquired real-time data.  Methodology:In this paper, the clustering based GMM models are used to find the density patterns on a daily or weekly basis for user requirements. The ThingSpeak cloud platform used for performing analysis and visualizations. Results:An analysis has been performed on the proposed mechanism implemented on real-time traffic data with Accuracy, Precision, Recall, and F-Score as measures. Conclusions:The results indicate that the proposed mechanism is efficient when compared with the state-of-the-art schemes. Originality:Applying GMM and ThingSpeak Cloud platform to perform analysis on IoT real-time data is the first approach to find traffic density patterns on busy roads. Restrictions:There is a need to develop the application for mobile users to find the optimal traffic routes based on density patterns. The authors could not concentrate on the security aspect for finding density patterns.

Fog-Enabled Architecture for Data-Driven Cyber-Manufacturing Systems

2016 ◽  
Author(s):  
Dazhong Wu ◽  
Janis Terpenny ◽  
Li Zhang ◽  
Robert Gao ◽  
Thomas Kurfess
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Manufacturing Systems ◽  
Large Scale ◽  
Failure Detection ◽  
Remote Access ◽  
Data Driven ◽  
Time Data ◽  
Real Time Data ◽  
Geographically Distributed

Over the past few decades, both small- and medium-sized manufacturers as well as large original equipment manufacturers (OEMs) have been faced with an increasing need for low cost and scalable intelligent manufacturing machines. Capabilities are needed for collecting and processing large volumes of real-time data generated from manufacturing machines and processes as well as for diagnosing the root cause of identified defects, predicting their progression, and forecasting maintenance actions proactively to minimize unexpected machine down times. Although cloud computing enables ubiquitous and instant remote access to scalable information and communication technology (ICT) infrastructures and high volume data storage, it has limitations in latency-sensitive applications such as high performance computing and real-time stream analytics. The emergence of fog computing, Internet of Things (IoT), and cyber-physical systems (CPS) represent radical changes in the way sensing systems, along with ICT infrastructures, collect and analyze large volumes of real-time data streams in geographically distributed environments. Ultimately, such technological approaches enable machines to function as an agent that is capable of intelligent behaviors such as automatic fault and failure detection, self-diagnosis, and preventative maintenance scheduling. The objective of this research is to introduce a fog-enabled architecture that consists of smart sensor networks, communication protocols, parallel machine learning software, and private and public clouds. The fog-enabled architecture will have the potential to enable large-scale, geographically distributed online machine and process monitoring, diagnosis, and prognosis that require low latency and high bandwidth in the context of data-driven cyber-manufacturing systems.

The Development and Performance Analysis of a Ground Control System for an Unmanned Airship

2005 ◽  
Vol 277-279 ◽  
pp. 771-775
Author(s):  
Hyoun Kyoung Kim ◽  
Sang Jong Lee ◽  
Tae Sik Kim ◽  
Hae Chang Lee
Keyword(s):  
Control System ◽  
Real Time ◽  
Data Storage ◽  
Simulation Software ◽  
Ground Control ◽  
Time Data ◽  
Real Time Data ◽  
Ground System ◽  
And Performance ◽  
Real Time Data Processing

A control system and ground pilots who operate the system are necessary to control an unmanned vehicle. This paper describes a Ground Control System(GCS) of the unmanned airship developed at KARI. The system is capable of mission planning, real-time data processing, remote real-time data analysis, and data storage and extraction. We estimate the performance of the system and verify its reliability using real-time simulation software. The software relies on a priority-based multitasking algorithm. No task has delay time and deadlock with semaphore, all of the ground system works in stable during the test flight.

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