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共 3243 条记录，以下是241-250 订阅

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Computer Performance Determination System Based on Big data Distributed File

Computer Performance Determination System Based on Big Data ...

引用

international conference on Cyber Security Intelligence and analytics (CSIA)

作者： Lu, Kong Jiangxi Modern Polytech Coll Nanchang 330095 Jiangxi Peoples R China

ISBN: (纸本)9783030152352;9783030152345

The high concurrency, high scalability, high performance, high availability, and large capacity of the distributed file system make it an ideal choice for storing large amounts of data. To determine the performance of the distributed computer in big data analysis, it is necessary to compare the performance test of different distributed file systems. This paper compares the different distributed file systems, analyzes the computer performance characteristics of distributed files, and summarizes the rules of different characteristics.

关键词： Big data Distributed file system Performance decision

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Development and Future Trends of Internet of Things

Development and Future Trends of Internet of Things

引用

international conference on Cyber Security Intelligence and analytics (CSIA)

作者： Liu, Wencheng Li, Fei Li, Yan Jilin Engn Normal Univ Coll Mech Engn 3050 Kaixuan Rd Changchun Jilin Peoples R China

ISBN: (纸本)9783030152352;9783030152345

The rise of the Internet of Things (IoT) is a product of the emergence of the mobile Internet and the new generation of information technology. It is of great significance to the upgrading and transformation of industries, agriculture, and cities. As one of the cutting-edge technologies with great application value, the IoT is characterized by its green, intelligent, and sustainable development, which makes it a promising foundation and an important engine for future technological progress and economic development. This paper starts with the status quo of the development of the IoTs, takes the national development policy as a basis, and focuses on the technical difficulties and application bottlenecks of the IoTs. The key technologies and application areas of the IoTs are also introduced to give a reasonable prediction of its future trend. The development of the IoTs will play an active role in the promotion of industries, agriculture, and cities.

关键词： IoT NB-IoT Industrial information smart city

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A Framework for Edge-Assisted Healthcare data analytics using Federated Learning 8

A Framework for Edge-Assisted Healthcare Data Analytics usin...

引用

8th IEEE international conference on Big data (Big data)

作者： Hakak, Saqib Ray, Suprio Khan, Wazir Zada Scheme, Erik Univ New Brunswick Fac Comp Sci Fredericton NB Canada Jazan Univ Dept CS & IS Jazan Saudi Arabia Univ New Brunswick Elect & Comp Engn Fredericton NB Canada

ISBN: (纸本)9781728162515

With the emergence of wearable technology, IoT, and Edge computing, the nature of healthcare is rapidly shifting towards digital health aided by these ICT technologies. At the same time, consumer devices, such as smart, wearable fitness watches are gaining market share as a way to monitor physical activity and wellness. Despite these advances, and their ability to capture longitudinal behavioural patterns, these devices have yet to be fully leveraged within the healthcare system. If the user-generated data from such devices could be collected without compromising an individual's privacy, these insights could comprise part of a more holistic and preventative healthcare solution. In this article, we propose an Edge-assisted data analytics framework that uses Federated Learning to re-train local machine learning models using user-generated data. This framework could leverage pre-trained models to extract user-customized insights while preserving privacy and Cloud resources. We also identify some potential application scenarios and discuss research challenges to be explored within the proposed framework.

关键词： Edge computing Healthcare analytics Federated Learning Wearable technology Medical IoT

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OLAPing Big Social data: Multidimensional Big data analytics over Big Social data Repositories 20

OLAPing Big Social Data: Multidimensional Big Data Analytics...

引用

4th international conference on Cloud and Big data computing, ICCBDC 2020

作者： Cuzzocrea, Alfredo IDEA Lab University of Calabria Rende Italy

ISBN: (纸本)9781450375382

Nowadays, a great deal of attention is devoted to the relevant problem of supporting big data analytics from social systems (e.g., social networks, smart city applications, skill management platforms, and so forth). Following this innovative trend, the opportunity of adopting advanced OLAP-based tools for supporting the knowledge extraction phase from big social data represents the new frontiers for big social data computing. Indeed, the well-known features of multidimensional data analysis are able to support a "rich"extraction of actionable knowledge, beyond actual limitations of alternative procedural approaches. In line with this emerging research challenge, this paper explores benefits, limitations and challenges of OLAP-based big data analytics tools over (big) social data. © 2020 ACM.

关键词： data analytics

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Portable Parallel Design of Weighted Multi-Dimensional Scaling for Real-Time data Analysis 19

Portable Parallel Design of Weighted Multi-Dimensional Scali...

引用

19th IEEE international conference on High Performance computing and Communications (HPCC) / 15th IEEE international conference on smart City (smartCity) / 3rd IEEE international conference on data Science and Systems (DSS)

作者： Dash, Sajal Verma, Anshuman North, Chris Feng, Wu-chun Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA Virginia Tech Dept Elect & Comp Engn Blacksburg VA USA

ISBN: (纸本)9781538625880

Projecting a high-dimensional dataset onto a lower dimensional space can improve the efficiency of knowledge discovery and facilitate real-time data analysis. One technique for dimension reduction, weighted multi-dimensional scaling (WMDS), approximately preserves pairwise weighted distances during the transformation;but its O(f (n)d) algorithm impedes real-time performance on large datasets. Thus, we present CLARET, our fast and portable parallel WMDS tool that combines algorithmic concepts adapted and extended from the stochastic force-based MDS (SF-MDS) and Glimmer. To further improve CLARET'S performance for real-time data analysis, we propose a preprocessing step that computes approximate weighted Euclidean distances by combining a novel data mapping called stretching and Johnson Lindestrauss' lemma in O(log d) time in place of the original O(d) time. This preprocessing step reduces the complexity of WMDS from O(f (n)d) to O(f (n) log d), which for large d is a significant computational gain. Finally, we present a case study of CLARET by integrating it into an interactive visualization tool called V2PI to facilitate real-time analytics. To ensure the quality of the projections, we propose a geometric shape matching-based alignment process and a quality metric.

关键词： multi-dimensional scaling (MDS) weighted multi-dimensional scaling (WMDS) OpenCL visual analytics

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data Analytical Framework for Internet of Things

Data Analytical Framework for Internet of Things

引用

2020 international conference on Decision Aid Sciences and Application, DASA 2020

作者： Saleem, Tausifa Jan Zahra, Syed Rameem Chishti, Mohammad Ahsan National Institute of Technology Department of Computer Science and Engineering Srinagar India Central University of Kashmir Department of Information Technology India

ISBN: (纸本)9781728196770

Internet of Things (IoT) is characterized by a colossal scale of smart objects that collaborate impeccably with each other through a worldwide network. data obtained from these smart objects can be used to recognize, inspect and manage complicated environments around us, enabling better understanding, intelligent decision-making, and performance optimisation. data analytics plays an incredible part in creating efficient IoT applications. It is used to dig out consequential insights from IoT data and these insights are typically in the form of smart management decisions, trends, and statistics that assist IoT applications in making potent decisions. Hence, exploitation of data analytics in IoT applications provides tremendous benefits including enhanced quality, increased efficiency, automation and better decision-making. This paper provides a knowhow of data analytics in IoT and presents the benefits of data analytics in IoT applications. Moreover, the data analytical frameworks for delay-tolerant and delay-critical applications are also presented. © 2020 IEEE.

关键词： Internet of things

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Research on a Network Communication data Privacy Protection System Based on OFDM Technology

Research on a Network Communication Data Privacy Protection ...

引用

international conference on Cyber Security Intelligence and analytics (CSIA)

作者： Lu, Kong Jiangxi Modern Polytech Coll Nanchang 330095 Jiangxi Peoples R China

ISBN: (纸本)9783030152352;9783030152345

At present, most data theft cases originate from the malicious intrusion of individual hackers to the production database. Therefore, more and more countries attach importance to the next generation of network communication, and a large amount of human and financial resources are invested in the research and development of data privacy protection of network communication. In order to perform real-time monitoring and intelligent control of network communication, the intelligent network communication control center needs to continuously collect the user's network communication data, and if the data leaks, it will expose the network communication rules and living habits of the individual users. In this paper, the actual system of high-speed data communication is realized by OFDM technology, and the problem of network communication data privacy protection is discussed and solved.

关键词： OFDM technology Network communication data Privacy protection

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intelligent Systems 1

引用

丛书名： Lecture Notes in Networks and Systems

1000年

作者： Siba K. Udgata Srinivas Sethi Satish N. Srirama

ISBN: (数字)9789813360815

ISBN: (纸本)9789813360808

This book features best selected research papers presented at the international conference on Machine Learning, Internet of Things and Big data (ICMIB 2020) held at Indira Gandhi Institute of Technology, Sarang, India, during September 2020. It comprises high-quality research work by academicians and industrial experts in the field of machine learning, mobile computing, natural language processing, fuzzy computing, green computing, human–computer interaction, information retrieval, intelligent control, data mining and knowledge discovery, evolutionary computing, IoT and applications in smart environments, smart health, smart city, wireless networks, big data, cloud computing, business intelligence, internet security, pattern recognition, predictive analytics applications in healthcare, sensor networks and social sensing and statistical analysis of search techniques.

关键词： Computational Intelligence Artificial Intelligence Mobile computing Wireless and Mobile Communication Cyber-physical systems, IoT Professional computing

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Edge computing and 5G Integration for Real-time analytics in Interoperable smart Grids

Edge Computing and 5G Integration for Real-time Analytics in...

引用

2023 international conference on Power Energy, Environment and intelligent Control, PEEIC 2023

作者： Devi, Kskn Venkata Ramana Koithyar, Aravinda Lakhanpal, Sorabh Parmar, Ashish Sethi, Vandana Arora Ftaiet, Adnan Allwi Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India Lloyd Institute of Management and Technology Plot No.-11 Knowledge Park-II Uttar Pradesh Greater Noida201306 India College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350357769

In the rapidly evolving landscape of power and energy systems, the smart Grid (SG) emerges as a dynamic and adaptive solution to cater to the ever-increasing energy demands while ensuring sustainable growth. The core essence of a smart Grid is its ability to provide real-time analytics, facilitating efficient energy management and distribution. With the influx of a myriad of heterogeneous devices and systems in the grid, interoperability emerges as a crucial factor to ensure seamless operations. This paper delves deep into the symbiotic relationship between Edge computing (EC) and the fifth generation (5G) mobile networks, illustrating their potential to revolutionize real-time analytics in interoperable smart Grids. Edge computing, with its decentralized data processing capabilities, caters to the latency-sensitive needs of SG systems. On the other hand, 5G, with its high bandwidth and ultra-reliable low latency communication, offers robust support to EC operations. Their integration promises unprecedented improvements in the efficiency, reliability, and resilience of SGs. Through an exhaustive study, this paper highlights the architectural enhancements, challenges, and potential solutions, casting a vision for the future of intelligent energy systems. © 2023 IEEE.

关键词： 5G Mobile Networks Edge computing Energy Management. Top of Form Real-time analytics smart Grid Interoperability

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smart interface development for sensor data analytics in internet of robotic things

Smart interface development for sensor data analytics in int...

引用

2019 international conference on intelligent computing and Control Systems, ICCS 2019

作者： Mahakalkar, Namrata Pethe, Rahul Kimmatkar, Kiran M. Durga Tiwari, Honey Priyadarshini Institute of Engineering and Technology Computer Science Engineering Nagpur Maharashtra India Priyadarshini J. L. College of Engineering Electrical Engineering Nagpur Maharashtra India Priyadarshini Institute of Engineering and Technology Electronics and Communication Engineering Nagpur Maharashtra India Narsimha Reddy Engineering College Department of Electronics and Communication Secunderabad Telangana State India

ISBN: (纸本)9781538681138

In recent years, the number of transducers connected to a common entity has increased significantly. In order to develop collaborative networks of robotic elements, the data from each sensor is shared through the network. As the number of sensors increases the data handled by the network also increases significantly. This causes serious network congestion issues even for a small network. In this paper, a framework for smart sensor interface is presented to optimize the data sharing and diagnostics operations. Different functions, operating conditions and architecture for cloud connectivity of sensor interface is presented. The implementation scheme and verification experiment are presented to show the application of the proposed design in multiple related sensors. The partial experiment results show that the proposed scheme can provide valuable insight to detect any deviation from normal behavior. Based on this information smart sensor and remote application can be used to take appropriate action. The partial results show that the proposed scheme can be employed for sensor data analytics in collaborative robotic networks. © 2019 IEEE.

关键词： data analytics

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