scholarly journals Innovative Web Service Grammar for Streaming Computing in Real-Time Technology

2019 ◽  
Vol 9 (2) ◽  
pp. 1589-1595
Keyword(s):  
Real Time ◽  
Research Work ◽  
Parallel Flow ◽  
Streaming Data ◽  
Advanced Technologies ◽  
Query Result ◽  
Different Types ◽  
Pros And Cons ◽  
Event Flow ◽  
Streaming Computing

The purpose of this work is to develop a UJSON web technology with C# application to analyze the student data in real-ime. Execute continuous requests on JSON streaming data based on advanced technologies for parallel streaming computing, suitable for solving analytic problems and calculation of metrics in real-time. The developed management information system in this research work designed to filtering event flow, building an event flow as a query result, grouping and aggregation of events, and creating window semantics. For testing the proposed work, several queries were selected that implement aggregation with different types of semantic windows (Steps, Slides). Testing was done locally and on education moodle clusters. It was used 4 types of configurations 2, 4, 8, and 16 computing nodes. Based on the obtained results, scalability is noticeable with an increase in the number of nodes. The updated functions of the proposed UJSON could improve the construction of parallel flow systems and data processing. The developed approach based on modern and advanced parallel flow technologies for output calculations considering the pros and cons of various approaches found in the current era.

scholarly journals Digital Twin: Origin to Future

2021 ◽  
Vol 4 (2) ◽  
pp. 36
Author(s):  
Maulshree Singh ◽  
Evert Fuenmayor ◽  
Eoin Hinchy ◽  
Yuansong Qiao ◽  
Niall Murray ◽  
...  
Keyword(s):  
Real Time ◽  
Remote Access ◽  
Physical Entity ◽  
Industrial Systems ◽  
Digital Twin ◽  
Planning Optimization ◽  
Different Types ◽  
Pros And Cons ◽  
Number Of Publications ◽  
Digital Shadow

Digital Twin (DT) refers to the virtual copy or model of any physical entity (physical twin) both of which are interconnected via exchange of data in real time. Conceptually, a DT mimics the state of its physical twin in real time and vice versa. Application of DT includes real-time monitoring, designing/planning, optimization, maintenance, remote access, etc. Its implementation is expected to grow exponentially in the coming decades. The advent of Industry 4.0 has brought complex industrial systems that are more autonomous, smart, and highly interconnected. These systems generate considerable amounts of data useful for several applications such as improving performance, predictive maintenance, training, etc. A sudden influx in the number of publications related to ‘Digital Twin’ has led to confusion between different terminologies related to the digitalization of industries. Another problem that has arisen due to the growing popularity of DT is a lack of consensus on the description of DT as well as so many different types of DT, which adds to the confusion. This paper intends to consolidate the different types of DT and different definitions of DT throughout the literature for easy identification of DT from the rest of the complimentary terms such as ‘product avatar’, ‘digital thread’, ‘digital model’, and ‘digital shadow’. The paper looks at the concept of DT since its inception to its predicted future to realize the value it can bring to certain sectors. Understanding the characteristics and types of DT while weighing its pros and cons is essential for any researcher, business, or sector before investing in the technology.

scholarly journals Real-time interpolation of streaming data

2020 ◽  
Vol 21 (4) ◽  
Author(s):  
Roman Dębski
Keyword(s):  
Time Series ◽  
Finite Difference ◽  
Real Time ◽  
Performance Measures ◽  
Spline Interpolation ◽  
Streaming Data ◽  
Data Sets ◽  
Look Ahead ◽  
Different Types ◽  
Hermite Splines

One of the key elements of real-time $C^1$-continuous cubic spline interpolation of streaming data is an estimator of the first derivative of the interpolated function that is more accurate than the ones based on finite difference schemas.Two such greedy look-ahead heuristic estimators (denoted as MinBE and MinAJ2) based on Calculus of Variations are formally defined (in closed form) together with the corresponding cubic splines they generate, and then comparatively evaluated in a series of numerical experiments involving different types of performance measures. The results presented show that the cubic Hermite splines generated by heuristic MinAJ2 significantly outperformed these based on finite difference schemas in terms of all tested performance measures (including convergence).The proposed approach is quite general. It can be directly applied to streams of univariate functional data like time-series. Multidimensional curves defined parametrically, after splitting, can be handled as well. The streaming character of the algorithm means that it can also be useful in processing data sets that are too large to fit in memory (e.g., edge computing devices, embedded time-series databases).

Temporal Geo-Social Personalized Keyword Search Over Streaming Data

2021 ◽  
Vol 7 (4) ◽  
pp. 1-28
Author(s):  
Abdulaziz Almaslukh ◽  
Yunfan Kang ◽  
Amr Magdy
Keyword(s):  
Real Time ◽  
Data Structures ◽  
Data Stream ◽  
Keyword Search ◽  
Streaming Data ◽  
Time Data ◽  
Social Data ◽  
Location Aware ◽  
Different Types ◽  
Social Media Platforms

The unprecedented rise of social media platforms, combined with location-aware technologies, has led to continuously producing a significant amount of geo-social data that flows as a user-generated data stream. This data has been exploited in several important use cases in various application domains. This article supports geo-social personalized queries in streaming data environments. We define temporal geo-social queries that provide users with real-time personalized answers based on their social graph. The new queries allow incorporating keyword search to get personalized results that are relevant to certain topics. To efficiently support these queries, we propose an indexing framework that provides lightweight and effective real-time indexing to digest geo-social data in real time. The framework distinguishes highly dynamic data from relatively stable data and uses appropriate data structures and a storage tier for each. Based on this framework, we propose a novel geo-social index and adopt two baseline indexes to support the addressed queries. The query processor then employs different types of pruning to efficiently access the index content and provide a real-time query response. The extensive experimental evaluation based on real datasets has shown the superiority of our proposed techniques to index real-time data and provide low-latency queries compared to existing competitors.

Opinion Mining with Real Time Ontology Streaming Data

2019 ◽  
Vol 23 (1) ◽  
pp. 346-357
Author(s):  
Vithya G ◽  
Naren J ◽  
Varun V
Keyword(s):  
Real Time ◽  
Opinion Mining ◽  
Streaming Data

scholarly journals Enhancing spatial perception through sound: mapping human movements into MIDI

2021 ◽  
Author(s):  
Bernardo Breve ◽  
Stefano Cirillo ◽  
Mariano Cuofano ◽  
Domenico Desiato
Keyword(s):  
Real Time ◽  
User Study ◽  
Spatial Perception ◽  
Human Movements ◽  
Different Types ◽  
Real Time Tracking ◽  
People’S Perceptions ◽  
Novel Algorithm

AbstractGestural expressiveness plays a fundamental role in the interaction with people, environments, animals, things, and so on. Thus, several emerging application domains would exploit the interpretation of movements to support their critical designing processes. To this end, new forms to express the people’s perceptions could help their interpretation, like in the case of music. In this paper, we investigate the user’s perception associated with the interpretation of sounds by highlighting how sounds can be exploited for helping users in adapting to a specific environment. We present a novel algorithm for mapping human movements into MIDI music. The algorithm has been implemented in a system that integrates a module for real-time tracking of movements through a sample based synthesizer using different types of filters to modulate frequencies. The system has been evaluated through a user study, in which several users have participated in a room experience, yielding significant results about their perceptions with respect to the environment they were immersed.

scholarly journals Modified Nanoclays/Straw Fillers as Functional Additives of Natural Rubber Biocomposites

Polymers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 799
Author(s):  
Justyna Miedzianowska ◽  
Marcin Masłowski ◽  
Przemysław Rybiński ◽  
Krzysztof Strzelec
Keyword(s):  
Natural Rubber ◽  
Raw Materials ◽  
Research Work ◽  
Damping Properties ◽  
Main Research ◽  
Functional Additives ◽  
Different Types ◽  
Elastomeric Materials ◽  
Research Goal ◽  
The Impact

Increasingly, raw materials of natural origin are used as fillers in polymer composites. Such biocomposites have satisfactory properties. To ensure above-average functional properties, modifications of biofillers with other materials are also used. The presented research work aimed to produce and characterize elastomeric materials with a straw-based filler and four different types of montmorillonite. The main research goal was to obtain improved functional parameters of vulcanizates based on natural rubber. A series of composites filled with straw and certain types of modified and unmodified nano-clays in various ratios and amounts were prepared. Then, they were subjected to a series of tests to assess the impact of the hybrids used on the final product. It has been shown that the addition of optimal amounts of biofillers can, inter alia, increase the tensile strength of the composite, improve damping properties, extend the burning time of the material and affect the course of vulcanization or cross-linking density.

NEURAL CLASSIFICATION OF MASS ABNORMALITIES WITH DIFFERENT TYPES OF FEATURES IN DIGITAL MAMMOGRAPHY

2006 ◽  
Vol 06 (01) ◽  
pp. 61-75 ◽  
Author(s):  
R. PANCHAL ◽  
B. VERMA
Keyword(s):  
Breast Cancer ◽  
Research Work ◽  
Breast Cancer Diagnosis ◽  
Classification Rate ◽  
Test Dataset ◽  
Benchmark Database ◽  
Different Types ◽  
Breast Tissues ◽  
Mass Type

Early detection of breast abnormalities remains the primary prevention against breast cancer despite the advances in breast cancer diagnosis and treatment. Presence of mass in breast tissues is highly indicative of breast cancer. The research work presented in this paper investigates the significance of different types of features using proposed neural network based classification technique to classify mass type of breast abnormalities in digital mammograms into malignant and benign. 14 gray level based features, four BI-RADS features, patient age feature and subtlety value feature have been explored using the proposed research methodology to attain maximum classification on test dataset. The proposed research technique attained a 91% testing classification rate with a 100% training classification rate on digital mammograms taken from the DDSM benchmark database.

scholarly journals Stream Data Load Prediction for Resource Scaling Using Online Support Vector Regression

Algorithms ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 37 ◽  
Author(s):  
Zhigang Hu ◽  
Hui Kang ◽  
Meiguang Zheng
Keyword(s):  
Support Vector Regression ◽  
Real Time ◽  
Virtual Machines ◽  
Time Window ◽  
Performance Model ◽  
Streaming Data ◽  
Support Vector ◽  
Load Prediction ◽  
Stream Data ◽  
Online Support Vector Regression

A distributed data stream processing system handles real-time, changeable and sudden streaming data load. Its elastic resource allocation has become a fundamental and challenging problem with a fixed strategy that will result in waste of resources or a reduction in QoS (quality of service). Spark Streaming as an emerging system has been developed to process real time stream data analytics by using micro-batch approach. In this paper, first, we propose an improved SVR (support vector regression) based stream data load prediction scheme. Then, we design a spark-based maximum sustainable throughput of time window (MSTW) performance model to find the optimized number of virtual machines. Finally, we present a resource scaling algorithm TWRES (time window resource elasticity scaling algorithm) with MSTW constraint and streaming data load prediction. The evaluation results show that TWRES could improve resource utilization and mitigate SLA (service level agreement) violation.

Advanced Driver Assists and Safety Management System

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Dipti Chavan ◽  
Aniket Kamble ◽  
Aditya Khadsare ◽  
Vaibhav Chougule ◽  
Vaibhav Chougule
Keyword(s):  
Real Time ◽  
Automobile Industry ◽  
Safety Management ◽  
Public Utilities ◽  
Sensor Platform ◽  
Key Points ◽  
Different Types ◽  
Important Field ◽  
And Control ◽  
Web Platform

Electronics and communication is the most important field. In this paper, we can describe how much safety is in the Automobile industry. In this paper, we are using uno-Arduino. The different types of sensors facilities are also provided using key points. The different sensors are provided to check visitor count. In this system, we can monitor and control all the safety precautions their one IoT web platform. This helps in the proper utilization of drivers and helps in avoiding accidents. This paper can be implemented in any two-wheelers, heavily loaded trucks, small SUVs, compact cars. In our paper, the electronics machine/components will be automatically working with using of Arduino program. The proposed wireless sensor platform is an attempt to develop more safety devices that can be used in multiple areas such as homes, schools, and public utilities to reduce accidents. This Advanced Driver Assists system will provide real-time accident detections and monitoring usage information that helps in real-time by using GSM, GPS, and sensors.

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