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On Kubernetes-aided Federated database Systems

On Kubernetes-aided Federated Database Systems

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IEEE international conference on Cloud computing Technology and science (CloudCom)

作者： Zheng Li Nicolás Saldías-Vallejos María Andrea Rodríguez Austen Rainer School of EEECS Queen’s University Belfast Belfast UK Department of Computer Science Universidad de Concepción Concepción Chile

ISBN: (纸本)9781665463683

Cloud computing has made federated database systems (FDBS) significantly more practical to implement than in the past. As part of a recent Web-based Geographic Information System (WebGIS) project, we are employing cloud-native technologies (from the container ecosystem) to develop a federated database (DB) infrastructure, to help manage and utilise the distributed and various geospatial data. Unfortunately, there seem to be inherent challenges and complexity of applying the container and Kubernetes technologies to building and running DB systems. Considering that most of the geospatial and theme data are pre-obtained and fixed in our WebGIS project, we decided to focus on the read-only user queries and still resort to Kubernetes to implement an FDBS instance to use. Unlike the de facto practices (e.g., using the StatefulSets mechanism, extending Kuberentes APIs, or employing KubeFed), our solution for Kubernetes-aided FDBS simplifies the tech stack by investigating the fractal object of federated data management, inclusively containerising DB instances, and using the lightweight Deployment mechanism to handle stateless DB containers. Overall, this research not only reveals an easy-to-implement approach to constructing read-only components in a fully-fledged FDBS, but also proposes and demonstrates a novel methodology for FDBS investigations.

关键词： Computers Cloud computing Ecosystems Buildings Distributed databases Containers database systems

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A Systematic Literature Review of Big data Analytics Challenges in Cloud computing

A Systematic Literature Review of Big Data Analytics Challen...

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Electronic and Electrical Engineering and Intelligent System (ICE3IS), international conference on

作者： Warren Stephen Candra Jonathan Jason Joartha Jurike V. Moniaga Muhammad Fadlan Hidayat Bakti Amirul Jabar School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665465427

Big data development has been increasing rapidly especially in the industry and government sectors. Big data has many benefits if it can be analyzed. But since big data is huge, it cannot be analyzed by an ordinary computer due to the lack of computing resources. Cloud computing has those resources needed for analyzing big data and can be accessed by everyone with only a subscription. Although cloud computing has its advantages, it also presents challenges for big data analytics. In this paper, we use a systematic literature review (SLR) for several scientific papers explaining cloud computing models, the technologies used for big data analytics, and big data analytics challenges in cloud computing. This paper explained three cloud computing models: SaaS, PaaS, and IaaS. We also talked about Hadoop and MapReduce; two tools used in big data analytics. This paper also addresses the difficulties of big data analytics in cloud computing.

关键词： Cloud computing Analytical models Solid modeling Systematics Computational modeling Bibliographies Memory

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Synergistic Multi-Sensor Fusion for Enhanced Vehicle Safety and Security-Integrating Advanced technologies and Intelligent Systems

Synergistic Multi-Sensor Fusion for Enhanced Vehicle Safety ...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： T. Akilan P. Manikandan B. Pushpavanam C. Swedheetha P. Naveen Jaya Sinha Dept. of IQAC Amity University Kolkata India Dept. of ECE Kalasalingam Academy of Research and Education Krishnankoil Dept. of EEE PSNA College of Engineering and Technology Dindigul Dept. of CSE Velammal College of Engineering and Technology Madurai India Dept. of EEE KPR Institute Of Engineering And Technology Coimbatore Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

The integration of multiple sensors and advanced technologies in the field of vehicle safety and security has emerged as a promising approach to enhance overall road safety and protect vehicles from potential threats. This paper presents a comprehensive study on the concept of synergistic multi-sensor fusion for advanced vehicle safety and security, focusing on the integration of cutting-edge technologies and intelligent systems. The proposed framework involves fusing data from various sensors to obtain a comprehensive and accurate perception of the vehicle’s surroundings and safety of the driver. By combining the strengths of each sensor, the system overcomes individual limitations and provides robust detection and recognition capabilities. Firstly, it enables accurate and timely detection of potential hazardous gas, infront nearest obstacle detection, accident identification this allowing the vehicle to take proactive measures to avoid collisions. Secondly, it enhances security by detecting and mitigating threats, such as fingerprint-based security system, theft alert. Additionally, the integration of intelligent systems enables driver safety measure based on driver heart rate monitoring, fire detection, current location detection and prone zone vehicle control. This paper highlights the significance of synergistic multi-sensor fusion in advancing vehicle safety and security. The integration of advanced technologies and intelligent systems enables robust detection, recognition, and decision-making capabilities, contributing to a safer and more secure driving experience.

关键词： Heart rate Current measurement Vehicle safety Sensor systems Road safety Security Intelligent systems

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Empirical investigation of the key machine learning elements promoting e-business using an SEM framework 5

Empirical investigation of the key machine learning elements...

引用

5th international conference on Contemporary computing and Informatics, IC3I 2022

作者： Krishna, Somanchi Hari Upadhyay, Alok Tewari, Mallika Gehlot, Anita Girimurugan, B. Pundir, Sumit Vignana Bharathi Institute of Technology Aushapur Village Ghatkesar Mandal Department of Business Management Telangana Malkangiri India Graphic Era Hill University Graphic Era Deemed to Be University Dehradun Bhimtal India Uttaranchal University Uttaranchal Institute of Technology Uttarakhand Dehradun India Koneru Lakshmaiah Education Foundation KL Business School Guntur AP Vaddeswaram India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

The paper's research goal is to identify the crucial elements of putting machine learning (ML) techniques into practice in order to improve electronic commerce (E-business) within the organization. In order to improve e-business and achieve sustainable growth, studies have focused on taking into account important determinants of ML impacted demand forecasting, implementation of ML in purchase behavior, improving consumer interaction, and endorse in ultimate cross-selling of the goods. The firm has been able to achieve additional benefits, assist in properly predicting client needs, encourage interaction, and cross-sell more items as a result of the introduction of novel and sophisticated ML technologies. This research will employ a closed-ended questionnaire to collect information from almost 343 business managers of retail E-business firms. It will then employ structural equation modelling (SEM) and SPSS to conduct an initial descriptive study using the data gathered. The article's major goal is to give readers a detailed and thorough grasp of how ML techniques are used to improve e-business. organizations are forced to implement novel techniques by the volatile business climate in order to boost sales, interact with customers, and improve brand values. Therefore, the focus of this research is mostly on comprehending the ML strategies used by the administration to assist their online business. According to the data, it can be concluded that ML-driven research helps improve user interaction, helps analyze consumer behavior, and keeps track of the goods as they travel from suppliers to buyers. This study concentrates on determining the critical factors that can be used to improve e-business in order to achieve long-term expansion and competitive advantage. E-commerce companies used ML to monitor general consumer behavior and connect customers in a way that would better serve their requirements. The research will be distinctive since it emphasizes examining the influe

关键词： Machine learning

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Enhanced Resource Control in Cloud Environment using Optimized VM Allocation in data Centers

Enhanced Resource Control in Cloud Environment using Optimiz...

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： Vikrant Sharma Shambhu Bhardwaj Department of Engineering & Technology Jaipur National University Jaipur India Department of Computing Science & IT Teerthanker Mahaveer University Moradabad India

ISBN: (纸本)9781665493970

Cloud computing is the most revolutionary technology of the last decade since it makes a new generation of computer resources accessible to consumers via IaaS, PaaS, and SaaS administrations. Infrastructure as a service (IaaS) allows a VM to be deployed, launched, and managed over a cluster of servers. Efficient VM assignment is often regarded as one of the most challenging issues for CPs to conquer because to the inherent tension between the goals of improving customer demand fulfilment while cutting costs and maximising asset efficiency. The purpose of this work is to provide a novel approach to allocating virtual machines to meet the needs of individual customers by using the modified emperor penguin optimization method algorithm. Relying on established optimization methods for efficient VM allocation further demonstrates the value of the suggested approach. This application was built using the Java programming language and deployed to the Netbeans IDE 12.4.

关键词： Cloud computing Java data centers Costs Software as a service Software algorithms Optimization methods

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Power and Performance Issues and Management Approaches in Cloud computing

Power and Performance Issues and Management Approaches in Cl...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Rahat Yezdani S. M. K. Quadri Department of Computer Science Jamia Millia Islamia New Delhi India

Cloud computing is a relatively new developing technology that allows users to pay for services on a pay-as-you-go basis. It provides internet-based information and communication technology services, while virtualization enables it to deliver computer resources. datacenters are the backbone of this technology, consisting of networked computers, cables, power supply units, and other components that host and store corporate data. In cloud datacenters, high performance has always been the most important consideration, but it comes at the expense of power consumption. The key problem is finding a balance between the system's performance and power consumption by lowering power usage without compromising the quality and performance of services. As a result, several attempts are now being done to reduce datacenter energy usage. All concerns relating to energy efficiency are explored and researched in this work. This paper also presents an overview of various power and performance management strategies in cloud computing.

关键词： Cloud computing Power demand Processor scheduling Network topology System performance Quality of service Scheduling

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An Empirical Study on Bankruptcy Prediction using Ensemble Learning

An Empirical Study on Bankruptcy Prediction using Ensemble L...

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IEEE international conference on Research, Innovation and Vision for the Future

作者： Hoang Luu Quang Tien Linh Quang Tran Trong Hop Do Faculty of Information Science and Engineering University of Information Technology Vietnam National University Ho Chi Minh City Vietnam

ISBN: (纸本)9781665461672

Bankruptcy prediction helps to assess the financial condition of a company and its future perspectives within the context of long-term operation on the market. Using machine learning to solve this problem can be a time-efficient and cost-effective approach. This paper introduces an approach using Ensemble learning methods to tackle the bankruptcy classification problem, which achieved the fourth position on the leader board of The 3rd Annual international data science & AI Competition 2022 - Structured data Track. The data set given by the committee of the competition has a lot of challenges so we perform some preprocessing and feature engineering techniques to make the data set become cleaner for modelling. We use three ensemble algorithms, namely Random Forest, Catboost, and LightGBM to compare the performance of three algorithms on the bankruptcy classification problem and find the best result to submit to the competition. After experimenting, we achieve the best result at 98.21% Accuracy on the private leader board of the competition. The result comes from the LightGBM model trained on the data set which is enhanced through feature engineering techniques.

关键词： Computational modeling Companies data science data models Communications technology Classification algorithms Ensemble learning

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Hadoop and Spark based Rectilinear Range Query Analysis

Hadoop and Spark based Rectilinear Range Query Analysis

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： Pranati Amit Kumar Bishnoi Department of Engineering & Technology Jaipur National University Jaipur India Department of Computing Science & IT Teerthanker Mahaveer University Moradabad India

ISBN: (纸本)9781665493970

Many people are curious in what may be learned by querying large amounts of geographical data. Due to the massive size of the data, even with the help of Hadoop/parallel MapReduce's processing, we still need a lot of time to process it, and this prevents us from providing the instantaneous response that is necessary. Most modern database systems, such as Spatial Hadoop or Hadoop-HIS, group data that are near in location together to facilitate indexing and information querying when storing spatial big data. data querying inside polygonal administrative areas like countries and cities is in high demand. But polygon query is not supported by most database management systems. This work investigates the issue of polygon queries on large geographic datasets and proposes a filtering-refinement approach to speed up calculations. Our technique is shown to greatly minimize compute on the Hadoop or Spatial Hadoop systems, particularly when the polygon is complicated, as shown by our studies utilizing seismic datasets.

关键词： Urban areas Big data Search problems Information filters Spatial databases database systems Sparks

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Investigating the Big data Challenges of Deep Learning for data science

Investigating the Big Data Challenges of Deep Learning for D...

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Smart Generation computing, Communication and Networking (SMART GENCON), international conference on

作者： Prateek Aggarwal Shweta Singh Krishnan Batri N.T. Velusudha Sunil D. Kale Gajendra Shrimal Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India School of Computer Science and Engineering Jain (Deemed to be University) Bangalore Karnataka India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Computer Science & Engineering Vivekananda Global University Jaipur India

Deep-gaining knowledge is a subset of synthetic intelligence (AI) that employs superior technology, data mining, and system-gaining knowledge to use massive datasets to find insights and styles. It is an area of advanced computing technology that enables computer systems to get the right of entry to analyze and analyze from many statistics. Even as the ability of deep learning is enormous, where its impact remains uncovered, extensive statistics demanding situations lie at the crossroads of its software and effectiveness. Massive records datasets' high quality and shape are essential for successfully implementing deep studying fashions. There may be an increasing need for highly acceptable statistics, including correctly categorized information, for training deep learning fashions. Moreover, deep studying often requires big datasets to provide correct output. It, therefore, calls for records to be amassed from numerous assets, posing the need for more homogeneous, regular, and standardized information. Adequate statistics garage, processing, and analytic solutions must be followed to analyze massive facts to create reachable and usable datasets for successful deep mastering packages. It includes infrastructure capable of securely keeping a large amount of information, in addition to the cloud-based totally systems to efficiently method extensive quantities of statistics. There has been a shift in the direction of using “large records” technologies like Apache Hadoop, Apache Spark, and different associated libraries. Even as deep mastering holds massive capability for uncovering new insights and turning in gigantic value to fields which include healthcare, finance, enterprise control, and more incredible, the associated massive data demanding situations ought to be addressed for those programs to reach their full capability. Despite the present-day challenges, advances in extensive records solutions like Apache Hadoop and Apache Spark, coupled with advanced analytic

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Streaming of High-Velocity Information using Dynamic Spatio-Temporal Query Processing

Streaming of High-Velocity Information using Dynamic Spatio-...

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： Raminder Singh Ranjana Sharma Department of Engineering & Technology Jaipur National University Jaipur India Department of Computing Science & IT Teerthanker Mahaveer University Moradabad India

ISBN: (纸本)9781665493970

In the event that the status of the query is modified in any way, a restart is required. This reduces the responsiveness of the system and, in the worst-case situation, produces results that are either wrong or out of date. In this work, we recommend using techniques from stream database, spatial information management, and computing systems to improve the processing of dynamic spatio-temporal queries across high-velocity large data streams. We provide strategies for streaming spatio-temporal querying and suggest spatial having earlier on geohashes for efficient parallel computing. Both of these are shown below. The in-memory batch processing that Apache Flink provides is compatible with our spatially ideas and performs very well with them. The viability of our strategy is proved by a comparative examination of our prototype, which maintains high consistent response times for both static and shifting query over high velocity spatiotemporal big data streams. This result demonstrates that our technique is successful.

关键词： Image segmentation Query processing Prototypes Streaming media Parallel processing Big data Spatial databases

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